Revisiting Semi-Supervised Learning with Graph Embeddings

Abstract: We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Abstract: We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale (up to 100+ million nodes and 1+ billion edges), encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at this https URL .

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.

Inductive Representation Learning on Large Graphs

Abstract: Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Datasets

The datasets we employed encompass citation networks, web graphs, co-purchase networks, and social networks. The detailed dataset statistics are shown in Table {{table:0764e439-b188-4f41-ba8c-eff8a959fb4d}} .


2=The sampled model is executed on the hardware target while searching.

A lookup table is created beforehand and filled with each operator latency on the targeted hardware. Once the search starts, the system will calculate the overall cost from the lookup table.

3=Build a ML model to predict the cost using architecture and dataset features.

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Abstract: Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3x faster than NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Abstract: This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct metrics are evaluated using empirical measurements, so that detailed knowledge of the platform and toolchain is not required. NetAdapt automatically and progressively simplifies a pre-trained network until the resource budget is met while maximizing the accuracy. Experiment results show that NetAdapt achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms. For image classification on the ImageNet dataset, NetAdapt achieves up to a 1.7$\times$ speedup in measured inference latency with equal or higher accuracy on MobileNets (V1&V2).

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Abstract: Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture optimality depends on factors such as input resolution and target devices. However, existing approaches are too resource demanding for case-by-case redesigns. Also, previous work focuses primarily on reducing FLOPs, but FLOP count does not always reflect actual latency. To address these, we propose a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods. FBNets (Facebook-Berkeley-Nets), a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically. FBNet-B achieves 74.1% top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8 phone, 2.4x smaller and 1.5x faster than MobileNetV2-1.3 with similar accuracy. Despite higher accuracy and lower latency than MnasNet, we estimate FBNet-B's search cost is 420x smaller than MnasNet's, at only 216 GPU-hours. Searched for different resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higher accuracy than MobileNetV2. The smallest FBNet achieves 50.2% accuracy and 2.9 ms latency (345 frames per second) on a Samsung S8. Over a Samsung-optimized FBNet, the iPhone-X-optimized model achieves a 1.4x speedup on an iPhone X. FBNet models are open-sourced at https://github. com/facebookresearch/mobile-vision.

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Abstract: Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

Abstract: Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs). However, it remains an open problem, how to integrate NAS with Application-Specific Integrated Circuits (ASICs), despite them being the most powerful AI accelerating platforms. The major bottleneck comes from the large design freedom associated with ASIC designs. Moreover, with the consideration that multiple DNNs will run in parallel for different workloads with diverse layer operations and sizes, integrating heterogeneous ASIC sub-accelerators for distinct DNNs in one design can significantly boost performance, and at the same time further complicate the design space. To address these challenges, in this paper we build ASIC template set based on existing successful designs, described by their unique dataflows, so that the design space is significantly reduced. Based on the templates, we further propose a framework, namely NASAIC, which can simultaneously identify multiple DNN architectures and the associated heterogeneous ASIC accelerator design, such that the design specifications (specs) can be satisfied, while the accuracy can be maximized. Experimental results show that compared with successive NAS and ASIC design optimizations which lead to design spec violations, NASAIC can guarantee the results to meet the design specs with 17.77%, 2.49x, and 2.32x reductions on latency, energy, and area and with 0.76% accuracy loss. To the best of the authors' knowledge, this is the first work on neural architecture and ASIC accelerator design co-exploration.

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Abstract: Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t. candidate set size). As a result, they need to utilize~\emph{proxy} tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs. These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task. In this paper, we present \emph{ProxylessNAS} that can \emph{directly} learn the architectures for large-scale target tasks and target hardware platforms. We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization. On CIFAR-10, our model achieves 2.08\% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6$\times$ fewer parameters. On ImageNet, our model achieves 3.1\% better top-1 accuracy than MobileNetV2, while being 1.2$\times$ faster with measured GPU latency. We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g. latency) and provide insights for efficient CNN architecture design.

Summary of Hardware Cost Estimation Methods

Table {{table:e19ef2e7-221e-4746-a4ca-f01b9f9b24f7}}  summarizes all the methods used to estimate the latency, energy consumption in different NAS methods.


Relay-Assisted Multiple Access With Full-Duplex Multi-Packet Reception

Abstract: The effect of full-duplex cooperative relaying in a random access multiuser network is investigated here. First, we model the self-interference incurred due to full-duplex operation, assuming multi-packet reception capabilities for both the relay and the destination node. Traffic at the source nodes is considered saturated and the cooperative relay, which does not have packets of its own, stores a source packet that it receives successfully in its queue when the transmission to the destination has failed. We obtain analytical expressions for key performance metrics at the relay, such as arrival and service rates, stability conditions, and average queue length, as functions of the transmission probabilities, the self interference coefficient, and the links' outage probabilities. Furthermore, we study the impact of the relay node and the self-interference coefficient on the per-user and aggregate throughput, and the average delay per packet. We show that perfect self-interference cancelation plays a crucial role when the SINR threshold is small, since it may result to worse performance in throughput and delay comparing with the half-duplex case. This is because perfect self-interference cancelation can cause an unstable queue at the relay under some conditions.

Timely Throughput of Heterogeneous Wireless Networks: Fundamental Limits and Algorithms

Abstract: The proliferation of different wireless access technologies, together with the growing number of multi-radio wireless devices suggest that the opportunistic utilization of multiple connections at the users can be an effective solution to the phenomenal growth of traffic demand in wireless networks. In this paper, we consider the downlink of a wireless network with N access points (APs) and M clients, where each client is connected to several out-of-band APs, and requests delay-sensitive traffic (e.g., real-time video). We adopt the framework of Hou and study the maximum total timely throughput of the network, denoted by CT3, which is the maximum average number of packets delivered successfully before their deadline. Solving this problem is challenging since even the number of different ways of assigning packets to the APs is NM. We overcome the challenge by proposing a deterministic relaxation of the problem, which converts the problem to a network with deterministic delays in each link. We show that the additive gap between the capacity of the relaxed problem, denoted by Cdet and CT3 is bounded by 2√(N(Cdet + [N/4])), which is asymptotically negligible compared to Cdet, when the network is operating at high-throughput regime. In addition, our numerical results show that the actual gap between CT3 and Cdet is in most cases much less than the worst-case gap proven analytically. Moreover, using LP rounding methods we prove that the relaxed problem can be approximated within additive gap of N. We extend the analytical results to the case of time-varying channel states, real-time traffic, prioritized traffic, and optimal online policies. Finally, we generalize the model for deterministic relaxation to consider fading, rate adaptation, and multiple simultaneous transmissions.

Resource Allocation and HARQ Optimization for URLLC Traffic in 5G Wireless Networks

Abstract: 5G wireless networks are expected to support ultra-reliable low latency communications (URLLC) traffic which requires very low packet delays (< 1 ms) and extremely high reliability (~99.999%). In this paper, we focus on the design of a wireless system supporting downlink URLLC traffic. Using a queuing network-based model for the wireless system, we characterize the effect of various design choices on the maximum URLLC load it can support, including: 1) system parameters such as the bandwidth, link SINR, and QoS requirements; 2) resource allocation schemes in orthogonal frequency-division multiple access (OFDMA)-based systems; and 3) hybrid automatic repeat request schemes. Key contributions of this paper which are of practical interest are: 1) study of how the minimum required system bandwidth to support a given URLLC load scales with associated QoS constraints; 2) characterization of optimal OFDMA resource allocation schemes which maximize the admissible URLLC load; and 3) optimization of a repetition code-based packet re-transmission scheme.

An Examination of the Benefits of Scalable TTI for Heterogeneous Traffic Management in 5G Networks

Abstract: The rapid growth in the number and variety of connected devices requires 5G wireless systems to cope with a very heterogeneous traffic mix. As a consequence, the use of a fixed TTI during transmission is not necessarily the most efficacious method when heterogeneous traffic types need to be simultaneously serviced.This work analyzes the benefits of scheduling based on exploiting scalable TTI, where the channel assignment and the TTI duration are adapted to the deadlines and requirements of different services. We formulate an optimization problem by taking individual service requirements into consideration. We then prove that the optimization problem is NP-hard and provide a heuristic algorithm, which provides an effective solution to the problem. Numerical results show that our proposed algorithm is capable of finding near-optimal solutions to meet the latency requirements of mission critical communication services, while providing a good throughput performance for mobile broadband services.

The Age of Incorrect Information: A New Performance Metric for Status Updates

Abstract: In this paper, we introduce a new performance metric in the framework of status updates that we will refer to as the Age of Incorrect Information (AoII). This new metric deals with the shortcomings of both the Age of Information (AoI) and the conventional error penalty functions as it neatly extends the notion of fresh updates to that of fresh “informative” updates. The word informative in this context refers to updates that bring new and correct information to the monitor side. After properly motivating the new metric, and with the aim of minimizing its average, we formulate a Markov Decision Process (MDP) in a transmitter-receiver pair scenario where packets are sent over an unreliable channel. We show that a simple “always update” policy minimizes the aforementioned average penalty along with the average age and prediction error. We then tackle the general, and more realistic case, where the transmitter cannot surpass a specific power budget. The problem is formulated as a Constrained Markov Decision Process (CMDP) for which we provide a Lagrangian approach to solve. After characterizing the optimal transmission policy of the Lagrangian problem, we provide a rigorous mathematical proof to showcase that a mixture of two Lagrange policies is optimal for the CMDP in question. Equipped with this, we provide a low complexity algorithm that finds the AoII-optimal operating point of the system in the constrained scenario. Lastly, simulation results are laid out to showcase the performance of the proposed policy and highlight the differences with the AoI framework.

Freshness on Demand: Optimizing Age of Information for the Query Process

Abstract: Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumed that new information is used at any time, which is not always the case and the instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC).

5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View

Abstract: The grand objective of 5G wireless technology is to support three generic services with vastly heterogeneous requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Service heterogeneity can be accommodated by network slicing, through which each service is allocated resources to provide performance guarantees and isolation from the other services. Slicing of the Radio Access Network (RAN) is typically done by means of orthogonal resource allocation among the services. This work studies the potential advantages of allowing for non-orthogonal sharing of RAN resources in uplink communications from a set of eMBB, mMTC and URLLC devices to a common base station. The approach is referred to as Heterogeneous Non-Orthogonal Multiple Access (H-NOMA), in contrast to the conventional NOMA techniques that involve users with homogeneous requirements and hence can be investigated through a standard multiple access channel. The study devises a communication-theoretic model that accounts for the heterogeneous requirements and characteristics of the three services. The concept of reliability diversity is introduced as a design principle that leverages the different reliability requirements across the services in order to ensure performance guarantees with non-orthogonal RAN slicing. This study reveals that H-NOMA can lead, in some regimes, to significant gains in terms of performance trade-offs among the three generic services as compared to orthogonal slicing.

Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)

Abstract: The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the superposition property of a multi-access channel. Thereby, "interference" is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population.

Representative works grouped by their relation to the instances of the statistical framework.

Putting the Prior Art within the Statistical Framework

Now that we have defined the basic framework of timing measures, we can look at the existing body of work on timing in communication networks, trying to frame the rich literature into the categories defined in the previous section. In the following, we examine a few interesting cases, which are familiar to the networking community. The relevant references are summarized by topic in Table {{table:7a2969c6-41c0-4088-a38b-0519078c76b2}} : as the table shows, past anchors are the most common method of measuring timing, and are often used in standards and protocols, while the use of relative timing references, which consider complex networking scenarios, is still largely unexplored.


Unsupervised Primal-dual Counterfactual Optimization

GCN {{cite:f194a79}}, GIN {{cite:df868ff}}

GIN {{cite:df868ff}}, GCN {{cite:f194a79}}

Decentralized Inference With Graph Neural Networks in Wireless Communication Systems

Abstract: Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner during the inference stage with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN during the inference stage in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Abstract: Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods.

Topology Aware Deep Learning for Wireless Network Optimization

Abstract: Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology information that directly impacts the network optimization results. Directly operating on simple representations, e.g., adjacency matrices, results in poor generalization performance as the learned results depend on specific ordering of the network elements in the training data. To address this issue, we propose a two-stage topology-aware machine learning framework (TALF), which trains a graph embedding unit and a deep feed-forward network (DFN) jointly. By propagating and summarizing the underlying graph topological information, TALF encodes the topology in the vector representation of the optimization instance, which is used by the later DFN to infer critical structures of an optimal or near-optimal solution. The proposed approach is evaluated on a canonical wireless network flow problem with diverse network typologies and flow deployments. In-depth study on trade-off between efficiency and effectiveness of the inference results is also conducted, and we show that our approach is better at differentiate links by saving up to 60% computation time at over 90% solution quality.

Efficient Power Allocation Using Graph Neural Networks and Deep Algorithm Unfolding

Abstract: We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we propose a hybrid neural architecture inspired by the algorithmic unfolding of the iterative weighted minimum mean squared error (WMMSE) method, that we denote as unfolded WMMSE (UWMMSE). The learnable weights within UWMMSE are parameterized using graph neural networks (GNNs), where the time-varying underlying graphs are given by the fading interference coefficients in the wireless network. These GNNs are trained through a gradient descent approach based on multiple instances of the power allocation problem. Once trained, UWMMSE achieves performance comparable to that of WMMSE while significantly reducing the computational complexity. This phenomenon is illustrated through numerical experiments along with the robustness and generalization to wireless networks of different densities and sizes.

Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

Abstract: Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the power control policy mapping channel state information (CSI) to the power vector. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional architecture whose spatial weights are tied to the channel coefficients, enabling a direct adaption to channel conditions. This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. To this end, we apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations.

A Graph Neural Network Approach for Scalable Wireless Power Control

Abstract: Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one- layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE.

Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

Abstract: Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability, generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a class of neural networks, named \emph{message passing graph neural networks} (MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a class of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, will demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with $1000$ transceiver pairs within $6$ milliseconds on a single GPU.

Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

Abstract: We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and 5th percentile user rates throughout a range of network configurations.

List of the papers in the wireless network scenario.

To sum up, the papers in the general wireless network scenario are listed in Table {{table:e6498ff5-f187-4687-80c4-e3df2d440eda}} . The target problem, proposed solution and the relevant GNN component(s) are also listed. The similar tabular format for the paper summary applies in the following sections.


Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications

Abstract: Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.

Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network

Abstract: Optimizing power control in multi-cell cellular networks with deep learning enables such a non-convex problem to be implemented in real-time. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently, which calls for low training complexity. To reduce the number of training samples and the size of DNN required to achieve good performance, a promising approach is to embed the DNNs with a priori knowledge. Since cellular networks can be modelled as a graph, it is natural to employ graph neural networks (GNNs) for learning, which exhibit permutation invariance (PI) and equivalence (PE) properties. Unlike the homogeneous GNNs that have been used for wireless problems, whose outputs are invariant or equivalent to arbitrary permutations of vertexes, heterogeneous GNNs (HetGNNs), which are more appropriate to model cellular networks, are only invariant or equivalent to some permutations. If the PI or PE properties of the HetGNN do not match the property of the task to be learned, the performance degrades dramatically. In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties. We then design a parameter sharing scheme for HetGNN such that the learned relationship satisfies the desired properties. Simulation results show that the sample complexity and the size of designed GNN for learning the optimal power control policy in multi-user multi-cell networks are much lower than the existing DNNs, when achieving the same sum rate loss from the numerically obtained solutions.

DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting

Abstract: Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.

List of the papers in the cellular network scenario.

To sum up, the papers in the cellular network scenario are listed in Table {{table:4e48bcb8-af97-48f9-859f-a255409c01ae}} .


Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks

Abstract: Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

Graph Embedding based Wireless Link Scheduling with Few Training Samples

Abstract: Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and generalizability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training samples. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

Abstract: This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks. To the best of our knowledge, our proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT using flow-based data. Our extensive experimental evaluation on four recent NIDS benchmark datasets shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of GNNs in network intrusion detection, and provides motivation for further research.

Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

Abstract: The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based framework, called SMART, is proposed to model and keep track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I) communication latency across a large geographical area. SMART first formulates the spatio-temporal performance of a vehicular network as a graph in which each vertex corresponds to a subregion consisting of a set of neighboring location points with similar statistical features of V2I latency and each edge represents the spatio-correlation between latency statistics of two connected vertices. Motivated by the observation that the complete temporal and spatial latency performance of a vehicular network can be reconstructed from a limited number of vertices and edge relations, we develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm in order to capture the spatial and temporal statistic of feature map pf latency performance for a large-scale vehicular network. Extensive simulations have been conducted based on a five-month latency measurement study on a commercial LTE network. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and reconstructing the latency performance of large vehicular networks.

List of the papers specified in other wireless network scenarios.

To sum up, the papers in other wireless network scenarios are listed in Table {{table:2403b13c-f58a-46c2-b2e9-ea4552028b06}} .


Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Abstract: Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.

Reinforcement Learning of Graph Neural Networks for Service Function Chaining

Abstract: In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF). To provide the highest quality of services, the SFC module should generate a valid path quickly even in various network topology situations including dynamic VNF resources, various requests, and changes of topologies. The previous supervised learning method demonstrated that the network features can be represented by graph neural networks (GNNs) for the SFC task. However, the performance was limited to only the fixed topology with labeled data. In this paper, we apply reinforcement learning methods for training models on various network topologies with unlabeled data. In the experiments, compared to the previous supervised learning method, the proposed methods demonstrated remarkable flexibility in new topologies without re-designing and re-training, while preserving a similar level of performance.

Graph Neural Network based Service Function Chaining for Automatic Network Control

Abstract: Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.

List of the papers in the SDN scenario.

To sum up, the papers in the SDN scenario are listed in Table {{table:34004f67-6cd1-489f-9d21-4cd84326552d}} .


PointPillars: Fast Encoders for Object Detection from Point Clouds

Abstract: Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.

Abstract: We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.

Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection

Abstract: In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: {\url{https://github.com/zhixinwang/frustum-convnet}.}

Abstract: We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avod

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

Abstract: In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. Besides, a consistency enforcing loss is employed to explicitly encourage the consistency of both the localization and classification confidence. We design an end-to-end learnable framework named EPNet to integrate these two components. Extensive experiments on the KITTI and SUN-RGBD datasets demonstrate the superiority of EPNet over the state-of-the-art methods. Codes and models are available at: \url{https://github.com/happinesslz/EPNet}.

Baidu Apollo EM Motion Planner

Abstract:
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results.
The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios.
The algorithm described in this manuscript is available at this https URL.

LiDAR-only and camera-LiDAR fusion perception algorithms of multiple architectures are all evaluated under the naive and frustum attacks and with defenses.

We consider three broad classes of multi-sensor fusion for perception, illustrated in Fig. {{figure:51dffdf3-1f13-4b65-bc6a-8ed6239afc9d}} : (1) cascaded semantic fusion (e.g., {{cite:7dd481e}}, {{cite:1b2d6a5}}) using the output of perception on one or more sensors to augment the input of other single-sensor perception, (2) integrated semantic fusion (e.g., {{cite:94c7955}}) that runs isolated perception for each sensor and fuses semantic outputs (e.g., in tracking), and (3) feature-level fusion (e.g., {{cite:5db7f17}}, {{cite:b53edc8}}) that combines low-level (machine-learned) features from multiple perception sources to produce a unified output. Specifically, we analyze state-of-the-art LiDAR-only (PointPillars {{cite:aa1b838}}, PointRCNN {{cite:2372e9d}}, PIXOR {{cite:4071cce}}) and camera-LiDAR fusion (Frustum-ConvNet {{cite:1b2d6a5}}, Frustum-PoinNet {{cite:7dd481e}}, AVOD {{cite:5db7f17}}, EPNET {{cite:b53edc8}}, Baidu Apollo {{cite:94c7955}}) perception algorithms, as summarized in Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}} .

We test a perception algorithm from each of the three categories of LiDAR-only perception architectures, consistent with {{cite:41cf48e}} and outlined in Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}} . Specifically, we use voxel-based PointPillars {{cite:aa1b838}}, point-based PointRCNN {{cite:2372e9d}}, and BEV-based PIXOR {{cite:4071cce}} for 3D object detection. We reproduce the attack success rate (ASR) from {{cite:41cf48e}}. Details on the reproduced results are in Appendix REF , showing high ASR of the naive spoof attacks at front-near positions.

No prior work has systematically evaluated whether sensor fusion is more resilient to spoofing. Thus, we evaluate the naive spoofing attacks against multiple camera-LiDAR fusion algorithms across multiple architectures summarized in Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}} . We find the majority of tested sensor fusion are inherently resilient to the naive spoofing attacks (Fig. {{figure:a7149da7-b9e4-4376-be3e-89e5e37a3761}} ). Overall, this level of intrinsic defense renders naive spoofing attacks ineffective even without the addition of specialized defenses. Specifically, widely used camera-LiDAR fusion algorithms FPN {{cite:7dd481e}}, FCN {{cite:1b2d6a5}}, and AVOD {{cite:5db7f17}} have high resiliency with ASR generally lower than {{formula:1ba4f365-fb80-45a7-bb9c-e363e38df210}} . We find EPNET {{cite:b53edc8}} is still vulnerable; we believe this is due to EPNET's high baseline FP rate. On the (unattacked) KITTI validation set, EPNET has 220% the number of FPs compared to AVOD; nearly 40% of all EPNET's detections on (unattacked) KITTI are FPs.

In this section, after establishing the feasibility of the frustum attack, we evaluate the impact of frustum spoofing on modern perception methods. We show both LiDAR-only and camera-LiDAR fusion perception are widely vulnerable to the context-aware frustum attacks: all 8 tested algorithms falling across 6 different architectures from Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}}  are vulnerable and none of the state-of-the-art defenses against LiDAR spoofing are capable of defending against the frustum attack.

For each of the first 7 perception algorithms in Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}} , we select each valid vehicle in each frame of the KITTI validation set. Each vehicle becomes the "target vehicle". For our analysis, we discard any valid vehicles not detected by the unattacked perception algorithm, as this would artificially inflate the FN attack success metric. For each valid vehicle, we simulate frustum attack spoofing using different combinations of the number of spoof points {{formula:5b240cdb-fa7e-4a5c-a02f-45465c1ce1b9}}  and the relative distance of placement {{formula:b24304a1-2a8e-4c2c-947a-a9bca3f8f4cc}} , i.e., {{formula:9cbedda1-0b72-4f74-bc3a-32303b68e983}} , within {{formula:8babd8f1-9449-426d-9961-c6a3aba70a34}}  points and {{formula:e2d5d9d0-dedf-49b8-a217-c76eadb8bb35}} ; here, {{formula:85446436-657f-4fad-b816-18b22d3c0862}}  is the original range to the target. The experiment captures existence of spoofing-induced: (a) FP at the spoof location, and (b) FN of the target object.

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Existing Attacks and Defenses
Naive Spoofing on LiDAR-only Perception
We implement the naive spoofing of {{cite:8ff9780}}, {{cite:41cf48e}} with the attack model described in Section REF . We follow {{cite:41cf48e}} to evaluate the attack success; i.e., we selected 5 attack traces using {{formula:f7833039-f4ef-45f7-80ea-54094ae60a50}}  points per trace over multiple trials for 100 attack evaluations, and placed the spoofed points in front-near positions, {{formula:ffd5c9bf-f37e-4d7c-a9e9-f0fb6b8377e7}}  from the victim vehicle. We extend the evaluation to outside of the front-near when evaluating defenses. Attack Success Rate (ASR) for FP outcomes is defined as the fraction of times a fictitious object is detected over the number of targeted attempts (e.g., number of spoof point clusters), as there could be more than one FP per frame. Similarly, we define the FN ASR as the fraction of times an object is missed over the number of attempts.
Our results, summarized in Fig. {{figure:68bd987b-7544-4a4d-bfcd-6344059fc995}} , confirm the success of the naive black-box spoofing attacks. The ASR is consistently high when 60 or more spoof points are used for all LiDAR-only algorithms showing that each of the 3 architectures are deeply vulnerable to spoof injections in front-near positions.

Existing Defenses against LiDAR Spoofing
We reproduce the state-of-the-art defenses against LiDAR spoofing; we showed that both CARLO and SVF dramatically reduce the ASR in front-near positions, while ShadowCatcher has challenges defending naive attacks (see Figs. {{figure:6a086848-2c39-4a6d-8ca3-9d0bb4bf8640}}  and {{figure:87b469a1-cd6e-4f39-a66b-5cec7ec6db76}} ). Note that SVF requires model-level changes and expensive retraining which are not possible with all the tested perception algorithms. Thus, we rearchitect and retrain PointPillars with SVF, following the approach from {{cite:41cf48e}}.
Naive spoofing attacks against LiDAR-only perception: Reproduced naive black-box spoofing attacks from {{cite:41cf48e}} applied to LiDAR-only perception, one method from each of the three LiDAR-only architecture categories from Table {{table:00c78f1b-ccbb-4f83-bd8c-5e7ff20ac492}} .
{{figure:68bd987b-7544-4a4d-bfcd-6344059fc995}}Naive spoofing attacks on LiDAR-only perception with CARLO: CARLO guards LiDAR-only perception against naive black-box spoofing attacks only in front-near positions.
{{figure:6a086848-2c39-4a6d-8ca3-9d0bb4bf8640}}Naive spoofing attacks on LiDAR-only perception with SVF: SVF guards LiDAR-only perception against naive black-box spoofing in front-near positions. SVF requires rearchitecting the perception model which is not feasible for every algorithm. We test SVF-modified PointPillars.
{{figure:87b469a1-cd6e-4f39-a66b-5cec7ec6db76}}Naive spoofing attacks on LiDAR-only perception with ShadowCatcher: ShadowCatcher has limited ability to guard against LiDAR spoofing attacks presented in {{cite:41cf48e}} due to difficulty handling noisy shadow estimation.
{{figure:a4ea0348-c156-40f4-8ea0-8ba6b74f100a}}While the ShadowCatcher defense is impressively simple, we do not expect to obtain the high accuracy reported in {{cite:a9992de}}. The reason is that the original work made several assumptions that are unrealistic, including tuning parameters on the test set, using ground truth bounding boxes instead of outputs of a perception algorithm, which significantly alters the shadow-region estimation noise, and only testing on 200 scenes with only three selections of 200 spoofed points. Still, we reproduced the original ShadowCatcher defense and obtained not as strong detection results without these assumptions (Fig. {{figure:a4ea0348-c156-40f4-8ea0-8ba6b74f100a}} ).

CARLO Vulnerabilities

We described in Section REF  that CARLO introduces vulnerability to FP attacks outside front-near and FN invalidation attacks. Here, we provide additional quantitative analysis.
To evaluate CARLO outside front-near, we collect receiver operating characteristics (ROC) on CARLO's ability to distinguish between valid and spoof objects placed at different ranges from the victim AV; the results are presented in Fig. {{figure:47611e9c-e4c1-470b-bc7b-a0639cd005a2}} . As in {{cite:41cf48e}}, we use PointPillars for this test; yet, CARLO is model-agnostic and these results generalize across other algorithms. We observe that, as range of the spoofed objects increases, CARLO's classification performance deteriorates (i.e., the defense breaks) and the ROC curve moves towards the center; e.g., in the case of 200 spoof points at 30 m (green), CARLO is similar to the random-guessing classifier.
ROC for FP attack on CARLO using PointPillars as perception with (left) 60 and (right) 200 spoofed points.
0.48(a) c{{table:8c982da7-aaa5-4ac7-a24c-0786daf22f2b}}0.48(b) c{{table:3d0ac57d-115a-4ef2-8b39-b0f9e27bc2ee}}{{figure:fd0f0a44-79ff-4fff-8ed0-96904d8bba90}}{{figure:db533432-7cb2-4235-92cb-deed536985e7}}{{figure:47611e9c-e4c1-470b-bc7b-a0639cd005a2}}Similarly, to test the FN invalidation attack, we collect ROC curves where each curve represents the range to the targeted valid object in Fig. {{figure:c9fcb7df-0134-47da-8524-6b6dd3edcebe}} . For each object, we record the range to that object, add 200 spoof points in a random pattern behind it, run CARLO on the detected result, and check if it
invalidated the true object. We find that CARLO's performance against the invalidation attack deteriorates when true objects are at an increased range from the victim, likely due to the decreased density of LiDAR points when objects are farther away.
The CARLO defense is vulnerable to FN attacks. Classification of valid objects significantly degrades when randomly spoofing 200 points behind valid objects, as well as when
range to target object increases.
{{figure:c9fcb7df-0134-47da-8524-6b6dd3edcebe}}

Spoofing Scenarios for Frustum Attacks

We illustrate common situations where vehicle configurations enable frustum attack spoofing (Fig. {{figure:4a58762a-27cb-4828-8ebe-75d9a02a7dcb}} ); the specific scenarios are described in Sec. REF . We execute the physical experiment in Section REF  corresponding to the scenario in Fig. {{figure:4a58762a-27cb-4828-8ebe-75d9a02a7dcb}}  and similar to the scenario in Fig. {{figure:4a58762a-27cb-4828-8ebe-75d9a02a7dcb}} . We also perform the longitudinal case study in Sec. REF  in accordance with the scenario from Fig. {{figure:4a58762a-27cb-4828-8ebe-75d9a02a7dcb}} . Anticipated advances in optical technology and tracking will soon enable spoofing points outside of line-of-sight as demonstrated in e.g., {{cite:33c736c}}.
Spoofer placed on target vehicle and points spoofed in LOS.
.98(a) t{{table:289c1310-c6be-46d4-b350-01460db96faf}}.98(b) t{{table:7d67c172-2eae-4c7d-a287-a692ebd6abe6}}.98(c) t{{table:7cf9bb74-003c-4540-99b8-9c067f7e1d86}}.98(d) t{{table:88a3991b-2046-4a9d-a222-1f2a374a53f4}}{{figure:44677c3c-5ec4-45fa-8284-d8f84bc51516}}{{figure:5f2e8d13-db69-41ee-9a39-381a64231263}}Spoofer placed on larger vehicle in front of target, pointing in LOS.{{figure:1344574e-82aa-441b-9a3a-aba494420360}}Spoofer placed on other vehicle in nearby lane, pointing out of LOS.{{figure:231d4214-86b9-4077-96e7-f4015f2aca0c}}Spoofer placed in environment e.g., on roadside obstacle, in LOS (e.g., on bridge) or out of LOS (e.g., on roadside)The frustum attack in everyday driving scenarios. Adversaries can place a spoofer on target car, on other cars, or on roadside obstacles, placing points anywhere along line-of-sight (LOS) (e.g., in front or behind the target car).{{figure:4a58762a-27cb-4828-8ebe-75d9a02a7dcb}}

Impact of Spoof Point Placement

Prior works required spoofed points to be placed in patterns of occluded vehicles {{cite:41cf48e}}. Here, we show that spoofing in a normally-distributed pattern for the frustum attack can have success nearly matching using occluded traces.
In Fig. {{figure:5f88c2cb-bfa4-430d-9edb-8c5e3f365057}} , we compare the attackability of FPN perception when using the two spoof point generation methods (similar results are also obtained for the other aforementioned perception algorithms). We first provide a comparison of the fraction of instances where an attack succeeds using both methods, as function of the distance to the target (Fig. {{figure:5f88c2cb-bfa4-430d-9edb-8c5e3f365057}} -left)
As can be observed, it is difficult to distinguish between the performance of the two methods (random points vs. car-pattern).
We further provide an analysis of the attackability as a function of the number of points in the target objects' bounding box (Fig. {{figure:5f88c2cb-bfa4-430d-9edb-8c5e3f365057}} -right)
We define attackability as the ability to find an attack that succeeds within the attacker-specified capabilities. Both methods perform similarly,
with a small benefit of car patterned injections at medium range.
This improve the feasibility of LiDAR-based spoofing, as a normally distributed pattern does not require unrealistically careful placement of spoof points, and is robust to small displacements.
Spoofing in a Gaussian random pattern (Table {{table:7e51f3a7-ba24-41d4-aa72-9c5c78679b04}} ) achieves performance on-par with using an occluded car pattern; we test spoofing patterns on FPN and show dependence of attackability on (left) range to target and (right) number of points in target object bounding box with histogram showing frequency (%) of occurrence of such objects in KITTI.
0.480.48(a) t(b) t{{table:ad9d358a-7194-480c-8bf5-dcc3ce9ecf29}}{{figure:4cc5c801-e412-43ed-bb89-a1e18da65345}}{{figure:747dd545-65a1-404f-8be4-63b579056451}}{{figure:5f88c2cb-bfa4-430d-9edb-8c5e3f365057}}

Frustum Attack By Range to Target

Fig. {{figure:ee7f28e2-3604-4f3c-9d27-13893ade83be}} , in Section REF , summarizes the frustum attack performance against the AVOD perception algorithm for different parameter combinations. Here, we provide the results for all other aforementioned algorithms (Fig. {{figure:5ace2fd3-b4af-42d9-a4e9-ac0115afea4e}} ).
The majority of algorithms are vulnerable to frustum attacks both in front and behind the targeted object. In general, attack success increases as the range to the target object increases (left to right in a row), with low attack success for all algorithms when attacks occur near the target object; this is expected as the original object and false positive will be "merged" (i.e. only a single detection) once they are on top of each other.
Full point cloud with ground truth object box at {{formula:51d4bcb3-1307-442d-992a-55aecd4a92ea}}  (white)
(a) c{{table:54aeb3be-bfc2-4c33-aeab-e42762b27022}}(b) c{{table:30842a10-d8ba-461d-9bd8-f74450a22189}}(c) c{{table:28a045e2-7e12-4c4c-9a0c-40348c308fba}}(d) c{{table:57b083c1-2bc7-4d04-9869-c418a4dc0c2b}}{{figure:a8ff2048-06ac-4ec3-9a7d-2cb59b7af142}}{{figure:983f7a70-766a-471b-8ac3-85fb9eaf3d29}}Frustum with original and spoof points - detection (red) at {{formula:acae28ec-fbde-48e8-abc6-ac5f566ae06b}}{{figure:888037f4-ede0-4737-939b-0d142114722d}}Frustum with original and spoof points - detection (red) at {{formula:b90946e7-2395-4fe7-b4a4-b623a245eae8}}{{figure:7f039910-eccb-4866-a4c6-a08dd68fd4b4}}Frustum with original and spoof points - detection (red) at {{formula:a8ba7dcd-27a5-4cff-9df4-53ee0e253bc3}}Target object (front, white box) at {{formula:fd0cef2b-bea1-447c-816f-f448c9a5ec4e}}  with 492 points in bounding box. Just 65 points alter the target vehicle’s location and achieve detections (translations) using FPN.{{figure:38023a5a-45a9-4fa0-b1b1-c94475dffc85}}

Longitudinal Frustum Attack Visualizations

Spoofing points in successively changing distances causes the FP injection to appear to travel longitudinally. Fig. {{figure:38023a5a-45a9-4fa0-b1b1-c94475dffc85}}  shows a BEV visualization of such a longitudinal attack where the perception detects motion of the spoofed points (red).
Frustum attack success rate against all perception algorithm for different parameters (i.e., number of spoofed points, target vehicle range, distance of spoofing behind the target) combinations, tested on all objects in KITTI validation set. Each tested algorithm (row) is widely vulnerable to the frustum attack. ASR depends on the range to the target vehicle (column). Note a dead-zone near the target vehicle (i.e. relative distance=0) where attacks do not succeed and increased ASR as target range increases. Most algorithms are vulnerable to spoofing both in front ({{formula:4f1236f2-60db-4aef-a6fa-d831c5d63be5}}  on x-axis) and behind ({{formula:44489081-0b27-4522-b65f-db3469ce5ddb}}  on x-axis) target objects.
{{figure:5ace2fd3-b4af-42d9-a4e9-ac0115afea4e}}


Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

Abstract: Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Abstract: Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods.

Topology Aware Deep Learning for Wireless Network Optimization

Abstract: Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology information that directly impacts the network optimization results. Directly operating on simple representations, e.g., adjacency matrices, results in poor generalization performance as the learned results depend on specific ordering of the network elements in the training data. To address this issue, we propose a two-stage topology-aware machine learning framework (TALF), which trains a graph embedding unit and a deep feed-forward network (DFN) jointly. By propagating and summarizing the underlying graph topological information, TALF encodes the topology in the vector representation of the optimization instance, which is used by the later DFN to infer critical structures of an optimal or near-optimal solution. The proposed approach is evaluated on a canonical wireless network flow problem with diverse network typologies and flow deployments. In-depth study on trade-off between efficiency and effectiveness of the inference results is also conducted, and we show that our approach is better at differentiate links by saving up to 60% computation time at over 90% solution quality.

Efficient power allocation using graph neural networks and deep algorithm unfolding

Abstract: We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we propose a hybrid neural architecture inspired by the algorithmic unfolding of the iterative weighted minimum mean squared error (WMMSE) method, that we denote as unfolded WMMSE (UWMMSE). The learnable weights within UWMMSE are parameterized using graph neural networks (GNNs), where the time-varying underlying graphs are given by the fading interference coefficients in the wireless network. These GNNs are trained through a gradient descent approach based on multiple instances of the power allocation problem. Once trained, UWMMSE achieves performance comparable to that of WMMSE while significantly reducing the computational complexity. This phenomenon is illustrated through numerical experiments along with the robustness and generalization to wireless networks of different densities and sizes.

Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

Abstract: Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the power control policy mapping channel state information (CSI) to the power vector. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional architecture whose spatial weights are tied to the channel coefficients, enabling a direct adaption to channel conditions. This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. To this end, we apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations.

A Graph Neural Network Approach for Scalable Wireless Power Control

Abstract: Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in $K$-user interference channels. Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE. The code is available on https://github.com/yshenaw/Globecom2019.

Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

Abstract: Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a family of neural networks, named message passing graph neural networks (MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems, while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a family of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

Abstract: We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture, and we then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and 5th percentile user rates throughout a range of network configurations.

To sum up, the papers in the general wireless network scenario are listed in Table {{table:31105535-3c4b-407e-aa44-e7507382f43b}} . The target problem, proposed solution and the relevant GNN component(s) are also listed. The similar tabular format for the paper summary applies in the following sections.