IEEE TRANSACTIONS ON COMMUNICATIONS 通过包括无线电在内的电磁传播发表高质量的手稿，介绍所有电信技术（包括电话、电报、传真和电视）的最新进展；金属丝;天线;地下、同轴和海底电缆；波导、通信卫星和激光器；在海洋、航空、空间和固定站服务方面；中继器、无线电中继、信号存储和再生；电信错误检测和纠正；多路复用和载波技术；通信交换系统；数据通信；和传播理论。

The IEEE Transactions on Communications publishes high-quality manuscripts that cover the latest advancements in electromagnetic propagation, including telecommunications technologies such as radio, telephone, telegraph, fax, and television. The content encompasses technologies like metallic wires, antennas, underground, coaxial, and submarine cables, waveguides, communication satellites, and lasers, as well as their applications in marine, aviation, space, and fixed station services. Additionally, the journal explores topics such as repeaters, radio relays, signal storage and regeneration, telecommunications error detection and correction, multiplexing and carrier technologies, communication switching systems, data communication, and propagation theory.

《IEEE Transactions on Communications》期刊发表高质量的手稿，涵盖电磁传播领域的最新进展，包括无线电、电话、电报、传真和电视等电信技术。内容涉及金属丝、天线、地下、同轴和海底电缆、波导、通信卫星和激光器等技术，以及在海洋、航空、空间和固定站服务中的应用。此外，期刊还探讨中继器、无线电中继、信号存储和再生、电信错误检测与纠正、多路复用和载波技术、通信交换系统、数据通信和传播理论等主题。

IEEE Transactions on Communications

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale-Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

Zhenyu Zhou

Junhao Feng

Bo Gu

Bo Ai

Shahid Mumtaz

Jonathan Rodriguez

Mohsen Guizani

IEEE Trans. Commun.

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

当移动众包遇上无人机：节能任务分配与路径规划

A decision-directed, digitally implemented carrier synchronizer for channels with both frequency and phase uncertainty is presented. This combined algorithm and its derivatives are analyzed with respect to the achievable carrier acquisition time and the resulting bit error probability. For certain data rates (approximately 50 MSps or less), this algorithm can be implemented using CMOS gate array technology. As examples BPSK and QPSK modulation formats are studied herein and compared to their differentially coherent counterparts of DPSK and DQPSK.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

M.P. Fitz

W.C. Lindsey

Decision-directed burst-mode carrier synchronization techniques

判决引导的突发模式载波同步技术

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy.

Xiuhong Wei

Chen Hu

Linglong Dai

Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

毫米波大规模MIMO系统中的波束空间信道估计深度学习方法

Amna Kopic

Erma Perenda

Haris Gacanin

A Collaborative Multi-agent Deep Reinforcement Learning-based Wireless Power Allocation with Centralized Training and Decentralized Execution

基于协作多智能体深度强化学习的无线功率分配：集中训练与分散执行

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.

Shuyan Hu

Xin Yuan

Wei Ni

Xin Wang

Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing

蜂窝连接无人机通信辅助雷达感知的轨迹规划

In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

Shuai Liu

Helin Yang

Liang Xiao

Mengting Zheng

Huabing Lu

Zehui Xiong

Learning-Based Resource Management Optimization for UAV-Assisted MEC Against Jamming

基于学习的抗干扰无人机辅助移动边缘计算资源管理优化

In distributed computing systems, to mitigate the adverse effect of stragglers on the computation time, computation redundancy is used. The redundancy can be added proactively at the beginning, or reactively after some time based on the delay pattern of the workers. While most of the existing work with reactive mitigation strategy only considered task replication, we propose a coded reactive straggler mitigation with an uncoded and a coded phase for distributed matrix-matrix multiplications. Specifically, in the uncoded phase of the proposed strategy, the master distributes the computational job without redundancy among the workers. After a predetermined waiting time, the master cancels the remaining tasks. It then encodes the remaining tasks and distributes them among the workers. In the uncoded phase, in addition to the conventional erasure model, where workers can communicate only once, we consider multi-message communication (MMC) model to exploit the partial works done by workers. The optimum waiting time for the uncoded phase and the optimum code rate for the coded phase are also obtained. Our simulation results demonstrate that the proposed coded reactive mitigation significantly decreases the execution time in comparison with both the proactive mitigation strategy or the existing reactive mitigation strategy.

Maryam Haghighi Ardakani

Masoud Ardakani

Chintha Tellambura

Coded Reactive Stragglers Mitigation in Distributed Computing Systems

分布式计算系统中的编码反应式掉队者缓解

This paper delves into the potential of intelligent reflecting surfaces (IRSs) in enabling integrated sensing and communication (ISAC) in multi-user multi-path scenarios. We introduce a three-dimensional (3D) multi-user ISAC framework with distributed IRSs, which offers simultaneous signal demodulation, channel estimation, and localization. The transmission is divided into a user access stage and a downlink transmission stage. In the first stage, we propose an algorithm for simultaneous uplink signal demodulation and angles of arrival (AoA) estimation at the semi-passive IRS. Moreover, a joint active and passive beamforming scheme inspired by radar-communication, is proposed to enhance both communication and localization performance in the downlink stage, while eliminating the need for distinct localization reference signals. Numerical results demonstrate that the proposed ISAC framework achieves centimeter-level localization accuracy while maintaining comparable communication performance to communication-only systems, thus validating its effectiveness.

Xingyu Peng

Xiaoling Hu

Jiabao Gao

Richeng Jin

Xiaoming Chen

Caijun Zhong

Integrated Localization and Communication for IRS-Assisted Multi-User mmWave MIMO Systems

IRS辅助多用户毫米波MIMO系统的集成定位与通信

Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC). By harnessing the power of multi-antennas and a multitude of elements, the dual-function base station (BS) and IOS collaborate to realize joint active and passive beamforming, enabling seamless 360-degree ISAC coverage. The objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of multi-target sensing while ensuring the multi-user multi-stream communications. To achieve this, a comprehensive optimization approach is employed, encompassing the design of radar receive vector, transmit beamforming matrix, and IOS transmissive and reflective coefficients. Due to the non-convex nature of the formulated problem, an auxiliary variable is introduced to transform it into a more tractable form. Consequently, the problem is decomposed into three sub-problems based on the block coordinate descent algorithm. Semidefinite relaxation and successive convex approximation methods are leveraged to convert the sub-problem into a convex problem, while the iterative rank minimization algorithm and penalty function method ensure the equivalence. Furthermore, the scenario is extended to mode switching and time switching protocols. Simulation results validate the convergence and superior performance of the proposed algorithm compared to other benchmark algorithms.

Ziheng Zhang

Wen Chen

Qingqing Wu

Zhendong Li

Xusheng Zhu

Jinhong Yuan

Intelligent Omni Surfaces Assisted Integrated Multi-Target Sensing and Multi-User MIMO Communications

智能全向表面辅助集成多目标感知与多用户MIMO通信

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

Wangjun Jiang

Dingyou Ma

Zhiqing Wei

Zhiyong Feng

Ping Zhang

Jinlin Peng

ISAC-NET: Model-Driven Deep Learning for Integrated Passive Sensing and Communication

ISAC-NET：模型驱动的深度学习用于综合被动感知与通信

Abbreviation	IEEE Trans. Commun.
Journal Impact	7.18
Quartiles(Global)	TELECOMMUNICATIONS(Q1)

ISSN	0090-6778, 1558-0857
h-index	235
Top Journals	Yes

Publication Information	Publisher: Institute of Electrical and Electronics Engineers Inc.，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1970，Proportion of original research papers: 100.00%，Self Citation Rate:8.30%， Gold OA Rate: 9.44%
Average review cycle	网友分享经验：平均6.0个月
Average recruitment ratio	网友分享经验：容易

IEEE Transactions on Communications

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