Image-based Recommendations on Styles and Substitutes

Abstract: Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Abstract: Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.

Pitfalls of Graph Neural Network Evaluation

Abstract: Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In this paper we show that existing evaluation strategies for GNN models have serious shortcomings. We show that using the same train/validation/test splits of the same datasets, as well as making significant changes to the training procedure (e.g. early stopping criteria) precludes a fair comparison of different architectures. We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models. Even more importantly, our findings suggest that simpler GNN architectures are able to outperform the more sophisticated ones if the hyperparameters and the training procedure are tuned fairly for all models.

Geom-GCN: Geometric Graph Convolutional Networks

Abstract: Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs.

Statistics of the eight datasets in our experiments.

Datasets.
As shown in Table {{table:546acfaf-6b87-4d84-9c7d-d3d578e6057e}} , eight widely used real-world datasets are selected to evaluate the performance of our method considering the graph types and graph sizes.
Cora {{cite:eccb9cf}}, DBLP {{cite:5fe9f4b}} and PubMed {{cite:eccb9cf}} are citation networks where each node represents a paper, and each edge represents the citation relation between two papers;
Computers {{cite:727ded0}} is the Amazon co-purchase graph where nodes represent goods that are linked by an edge if these goods are frequently bought together;
Physics {{cite:9c1e0c8}} is a co-authorship graph where nodes are authors who are connected by an edge if they co-author a paper;
Actor {{cite:bf205f2}} is a co-occurrence graph where each node correspond to an actor and each edge denotes co-occurrence of these two actors on the same Wikipedia page;
Wisconsin {{cite:bf205f2}} is the hyperlinked web page graph where nodes represent web pages and edges are hyperlinks between them;
In Flickr {{cite:341ab51}}, nodes represent images and edges represent two images that share some common properties (e.g.,
same geographic location, same gallery, comments by the same user, etc.).


Latent Template Induction with Gumbel-CRFs

Abstract: Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structures. We explore the use of structured variational autoencoders to infer latent templates for sentence generation using a soft, continuous relaxation in order to utilize reparameterization for training. Specifically, we propose a Gumbel-CRF, a continuous relaxation of the CRF sampling algorithm using a relaxed Forward-Filtering Backward-Sampling (FFBS) approach. As a reparameterized gradient estimator, the Gumbel-CRF gives more stable gradients than score-function based estimators. As a structured inference network, we show that it learns interpretable templates during training, which allows us to control the decoder during testing. We demonstrate the effectiveness of our methods with experiments on data-to-text generation and unsupervised paraphrase generation.

Gradient Estimation with Stochastic Softmax Tricks

Abstract: The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators are easy to implement and low variance, but the goal of scaling them comprehensively to large combinatorial distributions is still outstanding. Working within the perturbation model framework, we introduce stochastic softmax tricks, which generalize the Gumbel-Softmax trick to combinatorial spaces. Our framework is a unified perspective on existing relaxed estimators for perturbation models, and it contains many novel relaxations. We design structured relaxations for subset selection, spanning trees, arborescences, and others. When compared to less structured baselines, we find that stochastic softmax tricks can be used to train latent variable models that perform better and discover more latent structure.

Dynamic programming algorithms for different inference over different graphs.Our randomization technique covers a spectrum of classical DPalgorithms on different graphs. Scaled algorithms shown inmypinkred are randomized in this work.

Aligning with a recent trend of exploiting randomization techniques for machine learning problems {{cite:12ca602}}, {{cite:10a3c69}}, {{cite:f830405}},
this work proposes a randomization framework for scaling structured models, which encompasses a family of randomized dynamic programming algorithms with a wide coverage of different structures and inference (Table {{table:66227654-1ba2-4681-ab41-f78a29edf470}} ).
Within our randomization framework,
instead of summing over all possible combinations of latent states, we only sum over paths with the most probable states and sample a subset of less likely paths to correct the bias according to a reasonable proposal.
Since we only calculate the chosen paths, memory consumption can be reduced to a reasonably small budget.
We thus recast the computation
challenge into a tradeoff between memory budget, proposal accuracy,
and estimation error. In practice, we show RDP scales existing models
by two orders of magnitude with memory complexity as
small as one percent.

Problem Statement   We start with a widely-used
structured VAE framework. Let {{formula:f45c434f-499a-4d02-84a8-9d25921abe07}}  be an observed variable.
Let {{formula:819638e0-a3cb-4b9f-8337-c120064cb401}}  be any latent structure (sequences of latent tags, parse
trees, or general latent graphs, see Table {{table:66227654-1ba2-4681-ab41-f78a29edf470}} )
that generates {{formula:03c30143-fdc9-4af6-953b-cbe78e525801}} . Let {{formula:adb1529b-0a59-47d7-aa44-363b0a957c12}}  denote the generative model,
and {{formula:ed4bdd46-dcf7-4c12-9fb2-7a9897e18a82}}  the inference model. We optimize:
{{formula:5badd582-8b18-4c85-9290-21b2a6a192e4}}

Challenges in Scaling   The challenge is how to scale the sum-product computation for {{formula:833fc10c-6082-4db2-b369-29cd59bc6a72}} .
Specifically, gradient-based learning requires three types of
inference: (a) partition estimation (for maximizing likelihood);
(b) re-parameterized sampling (for gradient estimation); and
(c) entropy estimation (for regularization).
Existing sum-product variants (Table {{table:66227654-1ba2-4681-ab41-f78a29edf470}} )
provide exact solutions, but only for a small latent space (e.g., a
linear-chain CRF with state number smaller than 100). Since the
complexity of DP-based inference is usually at least quadratic to the
size of the latent states, it would induce memory overflow if we
wanted to scale it to tens of thousands.


End-to-end Learning for Short Text Expansion

Abstract: Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.

Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling

Abstract: We describe the task of sentence expansion and enhancement, in which a sentence provided by a human is expanded in some creative way. The expansion should be understandable, believably grammatical, and highly related to the original sentence. Sentence expansion and enhancement may serve as an authoring tool, or integrate in dynamic media, conversational agents, and advertising. We implement a neural sentence expander, which is trained on sentence compressions generated from a corpus of modern fiction. We modify the objective loss function to support the task by focusing on new words, and decode at test time with controlled curve-like novelty sampling. We run the sentence expander on sentences provided by human subjects and have humans evaluate these expansions. The generation methods are shown to be comparable to, and as well liked as, subjects' original input sentences, and preferred over baselines.

Efficient text generation of user-defined topic using generative adversarial networks

Abstract: This study focused on efficient text generation using generative adversarial networks (GAN). Assuming that the goal is to generate a paragraph of a user-defined topic and sentimental tendency, conventionally the whole network has to be re-trained to obtain new results each time when a user changes the topic. This would be time-consuming and impractical. Therefore, we propose a User-Defined GAN (UD-GAN) with two-level discriminators to solve this problem. The first discriminator aims to guide the generator to learn paragraph-level information and sentence syntactic structure, which is constructed by multiple-LSTMs. The second one copes with higher-level information, such as the user-defined sentiment and topic for text generation. The cosine similarity based on TF-IDF and length penalty are adopted to determine the relevance of the topic. Then, the second discriminator is re-trained with the generator if the topic or sentiment for text generation is modified. The system evaluations are conducted to compare the performance of the proposed method with other GAN-based ones. The objective results showed that the proposed method is capable of generating texts with less time than others and the generated text is related to the user-defined topic and sentiment. We will further investigate the possibility of incorporating more detailed paragraph information such as semantics into text generation to enhance the result.

A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph

Abstract: Generating a vivid, novel, and diverse essay with only several given topic words is a challenging task of natural language generation. In previous work, there are two problems left unsolved: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge. Therefore, we propose a novel Sentiment-Controllable topic-to-essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational autoencoder (CVAE) framework. We firstly inject the sentiment information into the generator for controlling sentiment for each sentence, which leads to various generated essays. Then we design a Topic Knowledge Graph enhanced decoder. Unlike existing models that use knowledge entities separately, our model treats the knowledge graph as a whole and encodes more structured, connected semantic information in the graph to generate a more relevant essay. Experimental results show that our SCTKG can generate sentiment controllable essays and outperform the state-of-the-art approach in terms of topic relevance, fluency, and diversity on both automatic and human evaluation.

Natrual language generation models for text expansion.

The main purpose of text expansion is to inflate the short texts to longer ones that contains more abundant information, which can be divided into two aspects: short text expansion and topic-to-essay generation. Short text expansion aims to expand a short text into a richer
representation based on a set of long documents. Topic-to-essay generation aims at generating human-like diverse, and topic-consistent paragraph-level text with a set of given topics. The most representative methods for each subtask are shown in Table {{table:8b906846-9a0d-4012-bad9-02932748b865}} .


Largest dataset with rich (43) attributes

Largest 3D mask dataset & open set protocol

Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

Abstract: Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering $3$ ethnicities, $3$ modalities, $1,607$ subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted $340$ teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

Abstract: As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.

Abstract: The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 54, 600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection. It attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranking algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.

Summary of the recent five face PAD competitions organized from 2019 until 2021.

In the context of face PAD, altogether eight international competitions {{cite:411175c}}, {{cite:598e8a0}}, {{cite:c3d99ce}}, {{cite:cfa4ea1}}, {{cite:07a471c}}, {{cite:056fb84}}, {{cite:f4de222}}, {{cite:336b97a}} have been organized in conjunction with major biometrics and computer vision conferences in 2011, 2013, 2017, 2019, 2020 and 2021, each introducing new challenges to the research community. In this chapter, we focus on analysing the design and results of the five latest competitions {{cite:411175c}}, {{cite:07a471c}}, {{cite:056fb84}}, {{cite:f4de222}}, {{cite:336b97a}} from 2019 until 2021, while an extensive review of the first three competitions {{cite:598e8a0}}, {{cite:c3d99ce}}, {{cite:cfa4ea1}} can be found in {{cite:fde746c}}. The key features of the five most recent face PAD competitions are summarized in Table {{table:c7747ddb-8e22-466f-aa9a-978aed819c1e}} .


Nakagami small-scale fading; BGG path loss model

Constant small-scale fading; BGG path loss model

Nakagami for the signal, Rayleigh for the interference contribution; BGG path loss model

Constant small-scale fading; Probabilistic path loss model

Millimeter Wave Vehicular Communication to Support Massive Automotive Sensing

Abstract: As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars (RAdio Detection and Ranging) are used for object detection, visual cameras as virtual mirrors, and LIDARs (LIght Detection and Ranging) for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as dedicated short-range communication (DSRC) and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This paper makes the case that millimeter wave (mmWave) communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Coverage and Rate Analysis for Millimeter Wave Cellular Networks

Abstract: Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.

Tractable Model for Rate in Self-Backhauled Millimeter Wave Cellular Networks

Abstract: Millimeter wave (mmW) cellular systems will require high gain directional antennas and dense base station (BS) deployments to overcome high near field path loss and poor diffraction. As a desirable side effect, high gain antennas provide interference isolation, providing an opportunity to incorporate self-backhauling--BSs backhauling among themselves in a mesh architecture without significant loss in throughput--to enable the requisite large BS densities. The use of directional antennas and resource sharing between access and backhaul links leads to coverage and rate trends that differ significantly from conventional microwave ($\mu$W) cellular systems. In this paper, we propose a general and tractable mmW cellular model capturing these key trends and characterize the associated rate distribution. The developed model and analysis is validated using actual building locations from dense urban settings and empirically-derived path loss models. The analysis shows that in sharp contrast to the interference limited nature of $\mu$W cellular networks, the spectral efficiency of mmW networks (besides total rate) also increases with BS density particularly at the cell edge. Increasing the system bandwidth, although boosting median and peak rates, does not significantly influence the cell edge rate. With self-backhauling, different combinations of the wired backhaul fraction (i.e. the faction of BSs with a wired connection) and BS density are shown to guarantee the same median rate (QoS).

Coverage in mmWave Cellular Networks With Base Station Co-Operation

Abstract: Signal outage, due to shadowing and blockage, is expected to be the main bottleneck in millimeter wave (mmWave) networks. Moreover, the anticipated dense deployment of base stations in mmWave networks is expected to increase the interference from strong line-of-sight base stations too, thus further increasing the probability of outage. To address the issue of reducing outage, this paper explores the possibility of base station co-operation in the downlink of mmWave heterogenous networks. The main focus of this work is showing that, in a stochastic geometry framework that incorporates blockage, co-operation from randomly located base stations decreases the probability of outage/increases the coverage probability. Coverage probabilities are derived accounting for: blockage, different fading distributions on the direct links (but always Rayleigh fading on the interference links), antenna directionality, and different tiers. Numerical results suggest that coverage with base station co-operation in dense mmWave systems (i.e., with high average number of base stations per square meter), without small scale fading on the direct communications links, and with any probability of signal blockage, considerably exceeds coverage without co-operation. In contrast, a small increase in coverage is reported when mmWave networks are less dense, have a high probability of signal blockage and the direct communications links are affected by Rayleigh fading.

Stochastic Geometry Modeling and Analysis of Multi-Tier Millimeter Wave Cellular Networks

Abstract: In this paper, a new mathematical framework to the analysis of millimeter wave cellular networks is introduced. Its peculiarity lies in considering realistic path-loss and blockage models, which are derived from recently reported experimental data. The path-loss model accounts for different distributions of line-of-sight and non-line-of-sight propagation conditions and the blockage model includes an outage state that provides a better representation of the outage possibilities of millimeter wave communications. By modeling the locations of the base stations as points of a Poisson point process and by relying on a noise-limited approximation for typical millimeter wave network deployments, simple and exact integral as well as approximated and closed-form formulas for computing the coverage probability and the average rate are obtained. With the aid of Monte Carlo simulations, the noise-limited approximation is shown to be sufficiently accurate for typical network densities. The proposed mathematical framework is applicable to cell association criteria based on the smallest path-loss and on the highest received power. It accounts for beamforming alignment errors and for multi-tier cellular network deployments. Numerical results confirm that sufficiently dense millimeter wave cellular networks are capable of outperforming micro wave cellular networks, both in terms of coverage probability and average rate.

Related Works on mmWave Systems and Vehicular Communications.

As summarized in Table {{table:15ea0f09-b0a4-416b-94df-0797615d69b6}} , over the past few years, mmWave systems have been proposed as a viable alternative to traditional wireless local area networks {{cite:8b152ad}} or as a wireless backhauling technology for BSs of the same cellular network {{cite:50949ec}}, {{cite:3c07287}}. Furthermore, mmWave technology has also been considered for deploying dense cellular networks characterized by high data rates {{cite:1c57538}}, {{cite:5eacf76}}, {{cite:dd9968d}}. With regards to the vehicular communication domain, J. Choi et al. {{cite:002965a}} pioneered the application of the mmWave technology to partially or completely enable ITS communications. A mmWave approach to ITS communications is also being supported by the European Commission {{cite:b16964c}}.

As both the BS deployment and vehicle locations differ over both time and in different highway regions, any highway network model must account for these variations. In this setting, stochastic geometry provides a means of characterizing the performance of the system by modeling BS locations via a spatial process, such as the Poisson Point Process (PPP) {{cite:f75d5cb}}.
Generally, PPP models for wireless networks are now a well-established methodology {{cite:b1ab501}}, {{cite:d680849}}, {{cite:f75d5cb}}, {{cite:2ef061b}}, {{cite:df89d11}}, {{cite:37a51cc}}; however, there are challenges in translating standard results into the context of mmWave networks for road-side deployments due to the presence of NLOS links resulting from blockages {{cite:1c57538}}.
In particular, the presence of blockages has only been addressed in the context of mmWave cellular networks in urban and suburban environments that are substantially different to a highway deployment {{cite:1c57538}}. In particular, in mmWave cellular networks: (i) the positions of BSs follow a bi-dimensional PPP, and (ii) the positions of blockages are governed by a stationary and isotropic process. Even though this is a commonly accepted assumption for bi-dimensional cellular networks {{cite:f75d5cb}}, this is not satisfied by highway scenarios, where both blockages and BS distributions are clearly not invariant to rotations or translations. With regards to Table {{table:15ea0f09-b0a4-416b-94df-0797615d69b6}} , the path loss contribution of blockages has either been modeled by means of the Boolean Germ Grain (BGG) principle (i.e., only the BSs within a target distance are in LOS) or in a probabilistic fashion (i.e., a BS is in LOS/NLOS with a given probability). To the best of our knowledge, no models for road-side mmWave BS deployment accounting for vehicular blockages have been proposed to date.

SINR Outage and Rate Coverage Characterization

As acknowledged in {{cite:002965a}} there is a lack of extensive measurements for vehicular mmWave networks, as well as widely accepted channel models. Therefore, it is necessary to adopt conservative assumptions on signal propagation.
As summarized in Table {{table:15ea0f09-b0a4-416b-94df-0797615d69b6}} , several channel models have been proposed in the literature.
Typically publicly available system-level mmWave simulators {{cite:446c5c4}} adopt channel models entirely {{cite:1c57538}} or partially {{cite:a5e8ca5}} based on the Nakagami model, which are more refined alternatives to the widely adopted models dictating constant small-scale contributions {{cite:50949ec}}, {{cite:3c07287}}, {{cite:dd9968d}}. In particular, we adopt the same channel model in {{cite:a5e8ca5}}, which is based on the following observations: (i) because of the beamforming capabilities and the sectorial antenna pattern, the signal is impaired only by a limited number of scatterers, and (ii) the interfering transmissions cluster with many scatterers and reach the standard user. Furthermore, the considered sectorial antennal model at the transmitter and receiver sides (see Assumption REF ) significantly reduces the angular spread of the incoming signals – thus reducing the Doppler spread. Moreover, the incoming signals are concentrated in one direction. Hence, it is likely that there is a non-zero bias in the Doppler spectrum, which can be compensated by the automatic frequency control loop at the receiver side {{cite:a6873c2}}. For these reasons, the Doppler effect has been assumed to be mitigated.


MIMO Assisted Networks Relying on Intelligent Reflective Surfaces

Abstract: Intelligent reflective surfaces (IRSs) are invoked for improving both spectral efficiency (SE) and energy efficiency (EE). Specifically, an IRS-aided multiple-input multiple-output network is considered, where the performance of randomly roaming users is analyzed by utilizing stochastic geometry tools. As such, to distinguish the superposed signals at each user, the passive beamforming weight at the IRSs and detection weight vectors at the users are jointly designed. As a benefit, by adopting a zero-forcing-based design, the intra-cell interference imposed by the IRS can be suppressed. In order to evaluate the performance of the proposed network, we first derive the approximated channel statistics in the high signal-to-noise-ratio (SNR) regime. Then, we derive the closed-form expressions both for the outage probability and for the ergodic rate of users. Both the high-SNR slopes of ergodic rate and the diversity orders of outage probability are derived for gleaning further insights. The network's SE and EE are also derived. Our numerical results are provided to confirm that: i) the high-SNR slope of the proposed network is one; ii) the SE and EE can be significantly enhanced by increasing the number of IRS elements.

Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?

Abstract: The rate and energy efficiency of wireless channels can be improved by deploying software-controlled metasurfaces to reflect signals from the source to the destination, especially when the direct path is weak. While previous works mainly optimized the reflections, this letter compares the new technology with classic decode-and-forward (DF) relaying. The main observation is that very high rates and/or large metasurfaces are needed to outperform DF relaying, both in terms of minimizing the total transmit power and maximizing the energy efficiency, which also includes the dissipation in the transceiver hardware.

Reconfigurable Intelligent Surface Enabled IoT Networks in Generalized Fading Channels

Abstract: This paper studies an Internet-of-Things (IoT) network employing a reconfigurable intelligent surface (RIS) over generalized fading channels. Inspired by the promising potential of RIS-based transmission, we investigate a RIS-enabled IoT network with the source node employing a RIS-based access point. The system is modelled with reference to a receiver-transmitter pair and the Fisher-Snedecor $\mathcal{F}$ model is adopted to analyse the composite fading and shadowing channel. Closed-form expressions are derived for the system with regards to the average capacity, average bit error rate (BER) and outage probability. Monte-Carlo simulations are provided throughout to validate the results. The results investigated and reported in this study extend early results reported in the emerging literature on RIS-enabled technologies and provides a framework for the evaluation of a basic RIS-enabled IoT network over the most common multipath fading channels. The results indicate the clear benefit of employing a RIS-enabled access point, as well as the versatility of the derived expressions in analysing the effects of fading and shadowing on the network. The results further demonstrate that for a RIS-enabled IoT network, there is the need to balance between the cost and benefit of increasing the RIS cells against other parameters such as increasing transmit power, especially at low SNR and/or high to moderate fading/shadowing severity.

On the Impact of Phase Shifting Designs on IRS-NOMA

Abstract: In this letter, the impact of two phase shifting designs, namely random phase shifting and coherent phase shifting, on the performance of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) is studied. Analytical results are developed to show that the two designs achieve different tradeoffs between reliability and complexity. Simulation results are provided to compare IRS-NOMA to conventional relaying and IRS assisted orthogonal multiple access, and also to verify the accuracy of the obtained analytical results.

Analysis and Optimization of Outage Probability in Multi-Intelligent Reflecting Surface-Assisted Systems

Abstract: Intelligent reflecting surface (IRS) is envisioned to be a promising solution for designing spectral and energy efficient wireless systems. In this letter, we study a multi-IRS-assisted system under Rician fading where the phase shifts adapt to only the line of sight (LoS) components. First, we analyze and optimize the outage probability of the multi-IRS-assisted system in the slow fading scenario for the non-LoS (NLoS) components. We also show that the optimal outage probability decreases with the numbers of IRSs and elements of each IRS when the LoS components are stronger than the NLoS ones. Then, we characterize the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime, and show that it decreases with the powers of the LoS components. To the best of our knowledge, this is the first work that studies the outage probability in multi-IRS-assisted systems.

A Simple Design of IRS-NOMA Transmission

Abstract: This letter proposes a simple design of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink transmission. In particular, conventional spatial division multiple access (SDMA) is used first at the base station to generate orthogonal beams by using the spatial directions of the near users’ channels. Then, IRS-assisted NOMA is used to ensure that additional cell-edge users can also be served on these beams by aligning the cell-edge users’ effective channel vectors with the predetermined spatial directions. Both analytical and simulation results are provided to demonstrate the performance of the proposed IRS-NOMA scheme and also study the impact of hardware impairments on IRS-NOMA.

Reconfigurable Intelligent Surface Aided NOMA Networks

Abstract: Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

Spatial Throughput Characterization for Intelligent Reflecting Surface Aided Multiuser System

Abstract: Intelligent Reflecting Surface (IRS) has been recently proposed as a promising solution to enhance the spectral and energy efficiency of future wireless networks by tuning a massive number of low-cost passive reflecting elements and thereby constructing favorable wireless propagation environment. Different from the prior works that focus on link-level performance optimization for IRS-aided wireless systems, this letter characterizes the spatial throughput of a single-cell multiuser system aided by multiple IRSs that are randomly deployed in the cell. It is shown by simulation that our analysis is valid and the IRS-aided system outperforms the full-duplex relay-aided counterpart system in terms of spatial throughput when the number of IRSs exceeds a certain value. Moreover, it is shown that different deploying strategies for IRSs/active relays should be adopted for their respective throughput maximization. Finally, it is revealed that given the total number of reflecting elements for IRSs, the system spatial throughput increases when fewer IRSs are deployed each with more reflecting elements, but at the cost of more spatially varying user rates.

Beamforming Through Reconfigurable Intelligent Surfaces in Single-User MIMO Systems: SNR Distribution and Scaling Laws in the Presence of Channel Fading and Phase Noise

Abstract: We consider a fading channel in which a multi-antenna transmitter communicates with a multi-antenna receiver through a reconfigurable intelligent surface (RIS) that is made of $N$ reconfigurable passive scatterers impaired by phase noise. The beamforming vector at the transmitter, the combining vector at the receiver, and the phase shifts of the $N$ scatterers are optimized in order to maximize the signal-to-noise-ratio (SNR) at the receiver. By assuming Rayleigh fading (or line-of-sight propagation) on the transmitter-RIS link and Rayleigh fading on the RIS-receiver link, we prove that the SNR is a random variable that is equivalent in distribution to the product of three (or two) independent random variables whose distributions are approximated by two (or one) gamma random variables and the sum of two scaled non-central chi-square random variables. The proposed analytical framework allows us to quantify the robustness of RIS-aided transmission to fading channels. For example, we prove that the amount of fading experienced on the transmitter-RIS-receiver channel linearly decreases with $N$. This proves that RISs of large size can be effectively employed to make fading less severe and wireless channels more reliable.

Multi-Antenna Relaying and Reconfigurable Intelligent Surfaces: End-to-End SNR and Achievable Rate

Abstract: In this report, we summarize the end-to-end signal-to-noise ratio and the rate of half-duplex, full-duplex, amplify-and-forward, and decode-and-forward relay-aided communications, and well as the signal-to-noise ratio and the rate of the emerging technology known as reconfigurable intelligent surfaces.

MIMO-NOMA Networks Relying on Reconfigurable Intelligent Surface: A Signal Cancellation Based Design

Abstract: Reconfigurable intelligent surface (RIS) technique stands as a promising signal enhancement or signal cancellation technique for next generation networks. We design a novel passive beamforming weight at RISs in a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network for simultaneously serving paired users, where a signal cancellation based (SCB) design is employed. In order to implement the proposed SCB design, we first evaluate the minimal required number of RISs in both the diffuse scattering and anomalous reflector scenarios. Then, new channel statistics are derived for characterizing the effective channel gains. In order to evaluate the network's performance, we derive the closed-form expressions both for the outage probability (OP) and for the ergodic rate (ER). The diversity orders as well as the high-signal-to-noise (SNR) slopes are derived for engineering insights. The network's performance of a finite resolution design has been evaluated. Our analytical results demonstrate that: i) the inter-cluster interference can be eliminated with the aid of large number of RIS elements; ii) the line-of-sight of the BS-RIS and RIS-user links are required for the diffuse scattering scenario, whereas the LoS links are not compulsory for the anomalous reflector scenario.

Reconfigurable Intelligent Surfaces Assisted Communications With Limited Phase Shifts: How Many Phase Shifts Are Enough?

Abstract: Reconfigurable intelligent surface (RIS) has drawn a great attention worldwide as it can create favorable propagation conditions by controlling the phase shifts of the reflected signals at the surface to enhance the communication quality. However, the practical RIS only has limited phase shifts, which will lead to the performance degradation. In this paper, we evaluate the performance of an uplink RIS assisted communication system by giving an approximation of the achievable data rate, and investigate the effect of limited phase shifts on the data rate. In particular, we derive the required number of phase shifts under a data rate degradation constraint. Numerical results verify our analysis.

Channel Estimation and Passive Beamforming for Intelligent Reflecting Surface: Discrete Phase Shift and Progressive Refinement

Abstract: Prior studies on Intelligent Reflecting Surface (IRS) have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address them, we consider in this paper an IRS-aided single-user communication system with discrete phase shifts and design the IRS training reflection matrix for channel estimation as well as the passive beamforming for data transmission, both subject to the constraint of discrete phase shifts. We show that the training reflection matrix design for discrete phase shifts greatly differs from that for continuous phase shifts, and thus the corresponding passive beamforming should be optimized by taking into account the correlated channel estimation error due to discrete phase shifts. Specifically, we consider a practical block-based transmission, where each block has a finite (insufficient) number of training symbols for channel estimation. A novel hierarchical training reflection design is proposed to progressively estimate IRS elements' channels over multiple blocks by exploiting IRS-elements grouping and partition. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks.

Important contributions on RIS-aided MIMO. “DL” and “UL” represent downlink and uplink, respectively. The “sum-rate gain” implies that the gain brought by invoking RIS technique

By offering extra diversity in the spatial domain, multiple antenna techniques are of significant importance. The application of multiple antenna assisted RIS network has attracted substantial interest from academia {{cite:33d30a0}}, {{cite:592eda0}}, {{cite:3201d82}} and industry {{cite:7595134}}, {{cite:8a7cff3}}, {{cite:7cc9058}}.
Given the increasing research contributions on RIS, its advantages are becoming clear, especially in terms of its high spectral efficiency (SE) and energy efficiency (EE). There are several key challenges for performance analysis in RIS-aided networks. One of the main challenges is to evaluate the exact distributions of the cascade channels between the BS and users through the RISs. Another challenge is evaluating the effective channel gain after passive beamforming at the RIS. Table {{table:68c17526-8ae8-409f-a4c5-8f9d17521aac}}  summarizes the existing contributions on RISs with multiple antennas and illustrates their comparisons. The single user designs were introduced in Section IV.A. We then turn our attention to the multi-user cases. Note that the single user design can be further extended to multi-user design, a prioritized signal-enhancement-based (SEB) was proposed by Hou et al. {{cite:592eda0}}, where the passive beamforming is designed for the user with the best channel gain, and all other users rely on RIS-aided beamforming.


AO-SDR-based algorithm and two stage algorithm

The worst-case robust beamforming design

Imperfect cascaded channels at the transmitter

Overhead model for channel estimation and RIS configuration

Fixed point iteration and manifold optimization

Frequency-flat fading and frequency-flat selective channels

Iterative algorithms with closed-form expressions

Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming

Abstract: Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS’s passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.

Intelligent Reflecting Surface Aided Network: Power Control for Physical-Layer Broadcasting

Abstract: As a recently proposed idea for the future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service constraints, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to validate the proposed optimization method. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without the IRS.

Power Efficient IRS-Assisted NOMA

Abstract: In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

Intelligent Reflecting Surface-Assisted Multiple Access With User Pairing: NOMA or OMA?

Abstract: The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

Abstract: Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). In this paper, we study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring that the achievable rate of each user meets the quality of service (QoS) requirement for all possible channel error realizations. With unit-modulus and rate constraints, this problem is non-convex. The imperfect CSI further increases the difficulty of solving this problem. By using approximation and transformation techniques, we convert this problem into a squence of semidefinite programming (SDP) subproblems that can be efficiently solved. Numerical results show that the proposed robust beamforming design can guarantee the required QoS targets for all the users.

A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels

Abstract: Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model and the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the worst-case rate constraints by using the S-procedure and the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Overhead-Aware Design of Reconfigurable Intelligent Surfaces in Smart Radio Environments

Abstract: Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used, and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is developed and incorporated in the expressions of the system rate and energy efficiencies, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, and the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is carried out as well. The proposed framework allows characterizing the trade-off between optimized radio resources and the related overhead in networks with reconfigurable intelligent surfaces.

MISO Wireless Communication Systems via Intelligent Reflecting Surfaces

Abstract: Intelligent reflecting surfaces (IRSs) have received considerable attention from the wireless communications research community recently. In particular, as low-cost passive devices, IRSs enable the control of the wireless propagation environment, which is not possible in conventional wireless networks. To take full advantage of such IRS-assisted communication systems, both the beamformer at the access point (AP) and the phase shifts at the IRS need to be optimally designed. However, thus far, the optimal design is not well understood. In this paper, a point-to-point IRS-assisted multiple-input single-output (MISO) communication system is investigated. The beamformer at the AP and the IRS phase shifts are jointly optimized to maximize the spectral efficiency. Two efficient algorithms exploiting fixed point iteration and manifold optimization techniques, respectively, are developed for solving the resulting non-convex optimization problem. The proposed algorithms not only achieve a higher spectral efficiency but also lead to a lower computational complexity than the state-of-the-art approach. Simulation results reveal that deploying large-scale IRSs in wireless systems is more efficient than increasing the antenna array size at the AP for enhancing both the spectral and the energy efficiency.

Optimal Beamforming for MISO Communications via Intelligent Reflecting Surfaces

Abstract: Wireless communications via intelligent reflecting surfaces (IRSs) has received considerable attention from both academia and industry. In particular, IRSs are able to create favorable wireless propagation environments with typically low-cost passive devices. While various IRS-aided wireless communication systems have been investigated in the literature, thus far, the optimal design of such systems is not well understood. In this paper, IRS-assisted single-user multiple-input single-output (MISO) communication is investigated. To maximize the spectral efficiency, a branch-and-bound (BnB) algorithm is proposed to obtain globally optimal solutions for both the active and passive beamformers at the access point (AP) and the IRS, respectively. Simulation results confirm the effectiveness of deploying IRSs in wireless systems. Furthermore, by taking the proposed optimal BnB algorithm as the performance benchmark, the optimality of existing design algorithms is investigated.

Beamforming Optimization for Intelligent Reflecting Surface Assisted MIMO: A Sum-Path-Gain Maximization Approach

Abstract: Recently, intelligent reflecting surface (IRS) has emerged as an appealing technique that enables wireless communications with low hardware cost and low power consumption. In this letter, we consider an IRS-assisted point-to-point multi-input multi-output (MIMO) system, where a source communicates with its destination with the help of an IRS. Our goal is to maximize the spectral efficiency of this system by jointly optimizing the (active) precoding at the source and the (passive) phase shifters (PSs) at the IRS. However, this turns out to be an intractable mixed-integer non-convex optimization problem. To circumvent the intractability, we propose a new sum-path-gain maximization (SPGM) criterion to obtain a high-quality and efficient suboptimal solution to this problem. Specifically, the PSs are first designed based on a simplified optimization problem, which aims to maximize the sum-gains of the spatial paths between the source and the destination. Then, a low-complexity alternating direction method of multipliers (ADMM) algorithm is utilized to solve this simplified problem. Finally, with the above obtained PSs, the source precoding is derived by performing the singular value decomposition (SVD) on the effective channel between the source and the destination. Numerical results demonstrate that the proposed scheme can achieve near-optimal performance.

GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO

Abstract: Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RIS mainly focus on optimizing the spectral efficiency for single carrier systems. To avoid the complicated bit allocation on different spatial domain subchannels in MIMO systems, in this paper, we propose a geometric mean decomposition-based beamforming for RIS-assisted millimeter wave (mmWave) hybrid MIMO systems so that multiple parallel data streams in the spatial domain can be considered to have the same channel gain. Specifically, by exploiting the common angular-domain sparsity of mmWave massive MIMO channels over different subcarriers, a simultaneous orthogonal match pursuit algorithm is utilized to obtain the optimal multiple beams from an oversampling 2D-DFT codebook. Moreover, by only leveraging the angle of arrival and angle of departure associated with the line of sight (LoS) channels, we further design the phase shifters for RIS by maximizing the array gain for LoS channel. Simulation results show that the proposed scheme can achieve better BER performance than conventional approaches. Our work is an initial attempt to discuss the broadband hybrid beamforming for RIS-assisted mmWave hybrid MIMO systems.

Channel Capacity Optimization Using Reconfigurable Intelligent Surfaces in Indoor mmWave Environments

Abstract: Indoor millimeter-wave (mmWave) environment channels are typically sparsely-scattered and dominated by a strong line-of-sight (LOS) path. Therefore, communication over such channels is in general extremely difficult when the LOS path is not present. However, the recent introduction of reconfigurable intelligent surfaces (RISs), which have the potential to influence the propagation environment in a controlled manner, has the potential to change the previous paradigm. Motivated by this, we study the channel capacity optimization utilizing RISs in indoor mmWave environments where no LOS path is present. More precisely, we propose two optimization schemes that exploit the customizing capabilities of the RIS reflection elements in order to maximize the channel capacity. The first optimization scheme exploits only the adjustability of the RIS reflection elements; for this scheme we derive an approximate expression which explains the connection between the channel capacity gains and the system parameters. The second optimization scheme jointly optimizes the RIS reflection elements and the transmit phase precoder; for this scheme, we propose a low-complexity technique called global co-phasing to determine the phase shift values for use at the RIS. Simulation results show that the optimization of the RIS reflection elements produces a significant channel capacity gain, and that this gain increases with the number of RIS elements.

Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

Abstract: Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices are optimized for different subcarriers while only one common set of IRS reflection coefficients is designed to cater to all the subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

Achievable Rate Maximization by Passive Intelligent Mirrors

Abstract: This paper investigates the use of a Passive Intelligent Mirrors (PIM) to operate a multi-user MISO downlink communication. The transmit powers and the mirror reflection coefficients are designed for sum-rate maximization subject to individual QoS guarantees to the mobile users. The resulting problem is non-convex, and is tackled by combining alternating maximization with the majorization-minimization method. Numerical results show the merits of the proposed approach, and in particular that the use of PIM increases the system throughput by at least 40%, without requiring any additional energy consumption.

Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks

Abstract: Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.

On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation

Abstract: Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error ratio is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization

Abstract: Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase-shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI, while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective channels with the optimized IRS phase-shifts, thus significantly reducing the channel training overhead and passive beamforming complexity over the existing schemes based on the I-CSI of all channels. For the single-user case, a novel penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase-shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS optimization algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Contributions on Joint Transmit and Passive Beamforming Design

All the aforementioned research contributions on the joint transmit and passive beamforming design are summarized in Table {{table:3ee38237-9473-41f4-a543-4318cdacaf3c}} . “SU" and “MU" represent single-user and multi-user, respectively. “DL" and “UL" represent downlink and
uplink, respectively.


Describing Textures in the Wild

Abstract: Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

Fine-Grained Visual Classification of Aircraft

Abstract: This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy. At the finer level, differences between models are often subtle but always visually measurable, making visual recognition challenging but possible. A benchmark is obtained by defining corresponding classification tasks and evaluation protocols, and baseline results are presented. The construction of this dataset was made possible by the work of aircraft enthusiasts, a strategy that can extend to the study of number of other object classes. Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable. They, however, present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding.

Non-standard Datasets for MSuNAS

Existing NAS approaches are rarely evaluated for their search ability beyond standard benchmark datasets, i.e., ImageNet, CIFAR-10, and CIFAR-100. Instead, they follow a conventional transfer learning setup, in which the architectures found by searching on standard benchmark datasets are transferred, with weights fine-tuned, to new datasets. We argue that such a process is conceptually contradictory to the goal of NAS, and the architectures identified under such a process are sub-optimal. In this section we demonstrate the scalability of MSuNAS to six additional datasets with various forms of difficulties, in terms of diversity in classification classes (multi-classes vs. fine-grained) and size of training set (see Table {{table:5bc71a8d-57cf-4213-9351-2827af9e91a8}} ). We adopt the settings of the CIFAR datasets as outlined in Section . For each dataset, one search takes less than one day on 8 GPU cards.


The cooperative resource sharing among the vehicles potentially improve the latency-aware and AI related services. It is challenging to ensure direct resource sharing among the vehicles with security and proper incentives due to the trust and mobility issues.

Due to the heterogeneity and a large number of entities involved in IoV network such as RSUs, vehicles, and service provider units to provide smart parking like services, it is challenging to preserve privacy of users as well as implement the access control while the system is under centralized controlled. Moreover, the IoV network becomes more complex to manage.

The vehicles, in particular autonomous vehicles desire automated while ensuring data integrity for post-accident forensics.

To expedite the resource sharing among the vehicles, blockchain has eliminated the reliance of trusted third parties, brokers, and intermediaries. Rather, blockchain offers direct resource sharing between service provider and user which ultimately results lower latency services and increase the overall performance of services.

The use of blockchain has been potentially enhanced the vehicle managements in smart parking and vehicle platooning systems by its decentralization feature. Vehicle management over the decentralized network has fostered the verification of the service requests and contributions through the smart contract.

Blockchain adoption in forensics application has showed a promising alternative to store the vehicle related records permanently with an aim to prevent malicious tampering and accurate auditing. The research works have also demonstrated that utilizing blockchain along with modern cryptographic techniques forensic application systems enhances traceability and transparency. Moreover, the smart contract implemented in blockchain facilitates a decentralized fair agreement among the vehicles, insurance companies, and other related parties, which provides proper deployment of rules.

Permissioned Blockchain for Efficient and Secure Resource Sharing in Vehicular Edge Computing

Abstract: With the fast expanding scale of vehicular networks, vehicular edge computing (VEC) has emerged and attracted growing attention from both industry and academia. Parked vehicles (PVs) have great potential to join vehicular networks for sharing their idle computing and networking resources. However, due to the underlying security and privacy threats, it is challenging to fairly motivate PVs for resource sharing in an efficient and secure way. In this paper, we propose a permissioned vehicular blockchain for secure and efficient resource sharing in VEC, namely, Parkingchain. We first design smart contract to achieve secure resource sharing and efficient service provisioning between PVs and service requesters (SRs). A multi-weight subjective logic based delegated Byzantine Fault Tolerance (DBFT) consensus mechanism is presented to improve the consensus process in Parkingchain. Further, we design a contract theory-based incentive mechanism to model the interactions between SR and PVs under asymmetric information scenario. Finally, numerical results demonstrate that the proposed incentive mechanism is effective and efficient compared with existing schemes.

Privacy-Preserving Smart Parking System Using Blockchain and Private Information Retrieval

Abstract: Searching for available parking spaces is a major problem for drivers in crowded cities, causing traffic congestion, air pollution, and wasting drivers' time. Smart parking systems enable drivers to solicit real-time parking information and book parking slots. However, current smart parking systems require drivers to disclose their sensitive information, such as their desired destinations. Moreover, existing schemes are centralized, which makes them vulnerable to bottlenecks and single point of failure problems and privacy breaches by service providers. In this paper, we propose a privacy-preserving smart parking system using blockchain and private information retrieval. First, a consortium blockchain is created by different parking lot owners to ensure security, transparency, and availability of the parking offers. Then, to preserve the drivers' location privacy, we adopt private information retrieval technique to privately retrieve parking offers from blockchain nodes. In addition, a short randomizable signature is used to allow drivers to authenticate for reserving available parking slots from parking owners anonymously. Our evaluations demonstrate that our proposed scheme preserves drivers' privacy with low communication and computation overheads.

Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles

Abstract: Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.

Summary of blockchain application scenarios in the IoV

In the aforesaid studies, we have presented a number of blockchain-assisted IoV applications which we have divided into eight major categories such as (i) data protection and management, (ii) data and resource trading, (iii) resource sharing, (iv) vehicle management, (v) ride sharing, (vi) content broadcasting, (vii) traffic control and management, and (viii) forensics application. Although the emergence of IoV offers a wide range of applications, we have discussed only the blockchain efforts in IoV. These aforesaid IoV application scenarios are presented in Table {{table:9ddd247b-a709-4c21-a323-e09a5cf0a797}}  as a summary in terms of descriptions, how blockchain brings opportunities to the applications, as well as related papers.


AO, gradient descent, majorize-minimization (MM)

Penalty method, xxxx stochastic optimization

Penalty method, Alternative direction method of multipliers (ADMM)

Intelligent Reflecting Surface Enhanced Wireless Network: Joint Active and Passive Beamforming Design

Abstract: Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT Under QoS Constraints

Abstract: Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.

Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI

Abstract: In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

Intelligent Reflecting Surface-Assisted Multiple Access with User Pairing: NOMA or OMA?

Abstract: The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces

Abstract: Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

A summary of representative works on IRS reflection optimization.

Although this section focuses on the single-cell system with one IRS, it is worthy of extending the results to more general setups with multiple APs and/or multiple IRSs in an IRS-aided wireless network as shown in Fig. {{figure:d43ba366-06a7-47fc-a671-0b1d5ee6c125}} . As such, the reflection coefficients of IRSs need to be jointly designed with the transmissions of multiple APs to not only improve the desired signal power but also mitigate the intra-cell interference as well as inter-cell interference (ICI) {{cite:cfa61f8}}, {{cite:75e9bf8}}. In Table {{table:67afb7b2-7a8b-4c6f-90a7-8f46b8b8cd4e}} , we summarize the representative works on IRS reflection optimization based on their considered system setups, adopted optimization techniques, etc. Furthermore, besides discrete reflection amplitudes/phase shifts, the performance of IRS-aided systems under other hardware constraints, such as coupled reflection amplitude and phase shift shown in Section II-B, is also worth investigating. In particular, it was shown in {{cite:0cfab17}} that the asymptotic power scaling order, i.e., {{formula:a417b111-5ee1-451b-a496-562507cc0bcf}}  unveiled in {{cite:a4c6ecd}} under the ideal phase shift model, still holds for the practical case with phase-shift dependent non-uniform IRS reflection amplitude. On the other hand, from the optimization perspective, it is also important to develop more advanced and computationally efficient algorithms such as machine learning based methods for IRS reflection design, especially for practically large IRSs {{cite:7081592}}, {{cite:aa8ed5a}}, {{cite:189b22d}}.