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Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam prediction solutions, which utilize \textit{raw RGB images} captured at the basestation to predict the optimal beams, have shown initial promising results. However, they still have a considerable computational complexity, limiting their adoption in the real world. To address these challenges, this paper focuses on developing and comparing various approaches that extract lightweight semantic information from the visual data. The results show that the proposed solutions can significantly decrease the computational requirements while achieving similar beam prediction accuracy compared to the previously proposed vision-aided solutions.

We study a multi-objective multi-armed bandit problem in a dynamic environment. The problem portrays a decision-maker that sequentially selects an arm from a given set. If selected, each action produces a reward vector, where every element follows a piecewise-stationary Bernoulli distribution. The agent aims at choosing an arm among the Pareto optimal set of arms to minimize its regret. We propose a Pareto generic upper confidence bound (UCB)-based algorithm with change detection to solve this problem. By developing the essential inequalities for multi-dimensional spaces, we establish that our proposal guarantees a regret bound in the order of $\gamma_T\log(T/{\gamma_T})$ when the number of breakpoints $\gamma_T$ is known. Without this assumption, the regret bound of our algorithm is $\gamma_T\log(T)$. Finally, we formulate an energy-efficient waveform design problem in an integrated communication and sensing system as a toy example. Numerical experiments on the toy example and synthetic and real-world datasets demonstrate the efficiency of our policy compared to the current methods.

In the Internet-of-Things (IoT), massive sensitive and confidential information is transmitted wirelessly, making security a serious concern. This is particularly true when technologies, such as non-orthogonal multiple access (NOMA), are used, making it possible for users to access each other's data. This paper studies secure communications in multiuser NOMA downlink systems, where each user is potentially an eavesdropper. Resource allocation is formulated to achieve the maximum sum secrecy rate, meanwhile satisfying the users' data requirements and power constraint. We solve this non-trivial, mixed-integer non-linear programming problem by decomposing it into power allocation with a closed-form solution, and user pairing obtained effectively using linear programming relaxation and barrier algorithm. These subproblems are solved iteratively until convergence, with the convergence rate rigorously analyzed. Simulations demonstrate that our approach outperforms its existing alternatives significantly in the sum secrecy rate and computational complexity.

Federated learning (FL) is a popular distributed machine learning (ML) paradigm, but is often limited by significant communication costs and edge device computation capabilities. Federated Split Learning (FSL) preserves the parallel model training principle of FL, with a reduced device computation requirement thanks to splitting the ML model between the server and clients. However, FSL still incurs very high communication overhead due to transmitting the smashed data and gradients between the clients and the server in each global round. Furthermore, the server has to maintain separate models for every client, resulting in a significant computation and storage requirement that grows linearly with the number of clients. This paper tries to solve these two issues by proposing a communication and storage efficient federated and split learning (CSE-FSL) strategy, which utilizes an auxiliary network to locally update the client models while keeping only a single model at the server, hence avoiding the communication of gradients from the server and greatly reducing the server resource requirement. Communication cost is further reduced by only sending the smashed data in selected epochs from the clients. We provide a rigorous theoretical analysis of CSE-FSL that guarantees its convergence for non-convex loss functions. Extensive experimental results demonstrate that CSE-FSL has a significant communication reduction over existing FSL techniques while achieving state-of-the-art convergence and model accuracy, using several real-world FL tasks.

The ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and is dependent on the ego-vehicle's field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and vehicle-to-everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper.

Integrated sensing and communication improves the design of systems by combining sensing and communication functions for increased efficiency, accuracy, and cost savings. The optimal integration requires understanding the trade-off between sensing and communication, but this can be difficult due to the lack of unified performance metrics. In this paper, an information-theoretical approach is used to design the system with a unified metric. A sensing rate is introduced to measure the amount of information obtained by a pulse-Doppler radar system. An approximation and lower bound of the sensing rate is obtained in closed forms. Using both the derived sensing information and communication rates, the optimal bandwidth allocation strategy is found for maximizing the weighted sum of the spectral efficiency for sensing and communication. The simulation results confirm the validity of the approximation and the effectiveness of the proposed bandwidth allocation.

With the proliferating of wireless demands, wireless local area network (WLAN) becomes one of the most important wireless networks. Network intelligence is promising for the next generation wireless networks, captured lots of attentions. Sensing is one efficient enabler to achieve network intelligence since utilizing sensing can obtain diverse and valuable non-communication information. Thus, integrating sensing and communications (ISAC) is a promising technology for future wireless networks. Sensing assisted communication (SAC) is an important branch of ISAC, but there are few related works focusing on the systematical and comprehensive analysis on SAC in WLAN. This article is the first work to systematically analyze SAC in the next generation WLAN from the system simulation perspective. We analyze the scenarios and advantages of SAC. Then, from system simulation perspective, several sources of performance gain brought from SAC are proposed, i.e. beam link failure, protocol overhead, and intra-physical layer protocol data unit (intra-PPDU) performance decrease, while several important influencing factors are described in detail. Performance evaluation is deeply analyzed and the performance gain of the SAC in both living room and street canyon scenarios are verified by system simulation. Finally, we provide our insights on the future directions of SAC for the next generation WLAN.

We study distributed algorithms for finding a Nash equilibrium (NE) in a class of non-cooperative convex games under partial information. Specifically, each agent has access only to its own smooth local cost function and can receive information from its neighbors in a time-varying directed communication network. To this end, we propose a distributed gradient play algorithm to compute a NE by utilizing local information exchange among the players. In this algorithm, every agent performs a gradient step to minimize its own cost function while sharing and retrieving information locally among its neighbors. The existing methods impose strong assumptions such as balancedness of the mixing matrices and global knowledge of the network communication structure, including Perron-Frobenius eigenvector of the adjacency matrix and other graph connectivity constants. In contrast, our approach relies only on a reasonable and widely-used assumption of row-stochasticity of the mixing matrices. We analyze the algorithm for time-varying directed graphs and prove its convergence to the NE, when the agents' cost functions are strongly convex and have Lipschitz continuous gradients. Numerical simulations are performed for a Nash-Cournot game to illustrate the efficacy of the proposed algorithm.

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. However, the workflows, computational patterns, communication patterns, and optimization techniques of distributed GNN training remain preliminarily understood. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.