Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems

Fluid Antenna Empowered Integration of Sensing, Communications and Computing With Hybrid Multi-Task Offloading

This paper proposes a fluid antenna (FA)-empowered integration of sensing, communications and computing (ISCC) system, where an access node dynamically adjusts the positions of its transmit antennas to enhance the ISCC performance. In particular, the access node can offload its hybrid multi-task containing both computational tasks and collected sensing data to a group of computational nodes via multi-access edge computing (MEC) for efficient processing. We formulate a joint optimization of the beamforming, FAs’ positions, offloading strategies, and computing resource allocations, with the objective of maximizing the processing efficiency of the sensing task, which quantifies the system’s ability to process the sensing data in the hybrid multi-task MEC scenario and is defined as the ratio of the collected sensing data volume to its processing latency. We propose an efficient algorithm based on the block coordinate descent optimization to solve this non-convex problem. Numerical results evaluate the effectiveness and performance advantages of the proposed FA-empowered ISCC with hybrid multi-task MEC

Result Fusion for Integrated Active and Passive Sensing in DFRC Systems

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme

Power Optimization for Integrated Active and Passive Sensing in DFRC Systems

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case

Federated-Learning-Based Client Scheduling for Low-Latency Wireless Communications

Motivated by the ever-increasing demands for massive data processing and intelligent data analysis at the network edge, federated learning (FL), a distributed architecture for machine learning, has been introduced to enhance edge intelligence without compromising data privacy. Nonetheless, due to the large number of edge devices (referred to as clients in FL) with only limited wireless resources, client scheduling, which chooses only a subset of devices to participate in each round of FL, becomes a more feasible option. Unfortunately, the training latency can be intolerable in the iterative process of FL. To tackle the challenge, this article introduces update-importance-based client scheduling schemes to reduce the required number of rounds. Then latency-based client scheduling schemes are proposed to shorten the time interval for each round. We consider the scenario where no prior information regarding the channel state and the resource usage of the devices is available, and propose a scheme based on the multi-armed bandit theory to strike a balance between exploration and exploitation. Finally, we propose a latency-based technique that exploits update importance to reduce the training time. Computer simulation results are presented to evaluate the convergence rate with respect to the rounds and wall-clock time consumption

An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models

Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future networks. However, training and deploying big artificial intelligence models (BAIMs) introduces substantial computational and communication overhead. This poses a critical challenge to centralized approaches, due to the need of high-performance computing infrastructure and the reliability, secrecy and timeliness issues in long-distance access of cloud services. Therefore, there is an urging need to decentralize the services, partly moving them from the cloud to the edge and establishing native GenAI services to enable private, timely, and personalized experiences. In this paper, we propose a brand-new bottom-up BAIM architecture with synergetic big cloud model and small edge models, and design a distributed training framework and a task-oriented deployment scheme for efficient provision of native GenAI services. The proposed framework can facilitate collaborative intelligence, enhance adaptability, gather edge knowledge and alleviate edge-cloud burden. The effectiveness of the proposed framework is demonstrated through an image generation use case. Finally, we outline fundamental research directions to fully exploit the collaborative potential of edge and cloud for native GenAI and BAIM applications

Resilience of DoS Attacks in Designing Anonymous User Authentication Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) include spatially allotted autonomous instruments that employ sensors to check environmental or physical conditions. These autonomous instruments or nodes blend with routers or gateway to make several WSN-based real-Time applications. In many critical applications, an external user can directly access the real-Time data from sensor node. In this context, before offering access, the legitimacy of the user is required to be verified through a secure authentication scheme. Since, in WSN-based real-Time applications, the privacy of the user is greatly important, the authentication scheme for such environment should be anonymous. Till now, impressive efforts have been made in designing lightweight anonymous authentication protocol for WSN-based real-Time applications. However, most of such protocols are vulnerable to DoS attacks, which are occurred due to the loss of synchronization between the participants. Furthermore, to rebuilt synchronization between the participants, a protocol may need to compromise un-link-Ability property. Therefore, it can be argued that the problem of DoS attack has not been addressed properly in the existing literatures. In this paper, we present a way to deal with DoS attacks in designing lightweight anonymous authentication protocol for WSN-based real-Time applications without compromising any anonymity support. We argue that our proposed solution can easily be incorporated with the existing schemes to be resilient to DoS attacks. © 2001-2012 IEEE.

Intelligent Computation Offloading for Joint Communication and Sensing-Based Vehicular Networks

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance