IEEE COMMUNICATIONS LETTERS 在快速出版周期中发表短文，介绍通过不同媒体和渠道（包括有线、地下、波导、光纤和存储通道）进行通信的最新技术的进展。鼓励理论贡献（包括新技术、概念和分析）和实践贡献（包括系统实验和原型以及新应用）。该杂志主要关注通信系统的物理层和链路层。

IEEE Communications Letters is a rapid publication journal that publishes short articles on the latest technological advancements in communication through various media and channels, including wired, underground, waveguide, optical fiber, and storage channels. The journal encourages theoretical contributions (such as new technologies, concepts, and analyses) as well as practical contributions (including system experiments, prototypes, and new applications). It primarily focuses on the physical layer and link layer of communication systems.

IEEE Communications Letters 是一本快速出版的期刊，主要发表关于通过各种媒介和渠道（包括有线、地下、波导、光纤和存储通道）进行通信的最新技术进展的短文。期刊鼓励理论贡献（如新技术、概念和分析）以及实践贡献（如系统实验、原型和新应用）。该期刊主要关注通信系统的物理层和链路层。

IEEE Communications Letters

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimating the near-field wireless channel of XL-MIMO communications. In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA). Due to the near-field characteristic, the spatial gridding-based sparsifying dictionary may result in low channel estimation accuracy and a heavy computational burden. To address this issue, we propose a novel sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that formulates the sparsifying dictionary as a neural network layer and embeds it into LISTA neural network. The numerical results show that our proposed algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves better performance than LISTA with ten times atoms reduction.

Xiangyu Zhang

Zening Wang

Haiyang Zhang

Luxi Yang

IEEE Commun. Lett.

Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A Model-Based Deep Learning Approach

This letter theoretically compares the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided system. For a fair comparison, we consider that these two systems have the same overall power budget that can be used at both the base station (BS) and the RIS. For active RIS, we first derive the optimal power splitting between the BS’s transmit signal power and RIS’s output signal power. We also analyze the impact of various system parameters on the optimal power splitting ratio. Then, we theoretically and numerically compare the performance between the active RIS and the passive RIS, which demonstrates that the active RIS would be superior if the power budget is not very small and the number of RIS elements is not very large.

Kangda Zhi

Cunhua Pan

Hong Ren

Kok Keong Chai

Maged Elkashlan

Active RIS Versus Passive RIS: Which is Superior With the Same Power Budget?

有源RIS与无源RIS：在相同功率预算下，谁更胜一筹？

In this letter, we consider an unmanned aerial vehicle (UAV) relay network, where the UAV works as an amplify-and-forward relay. We optimize the trajectory of UAV, the transmit power of UAV, and the mobile device by minimizing the outage probability of this relay network. The analytical expression of outage probability is derived first. A closed-form low-complexity solution with joint trajectory design and power control is proposed to solve this non-convex problem. Simulation results show that the outage probability of the proposed solution is significantly lower than that of the fixed power relay and circle trajectory for the UAV relay.

Shuhang Zhang

Hongliang Zhang

Qichen He

Kaigui Bian

Lingyang Song

Joint Trajectory and Power Optimization for UAV Relay Networks

Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.

Nan Ma

Kui Xu

Xiaochen Xia

Chen Wei

Qiao Su

Maiying Shen

Wei Xie

Reinforcement Learning-Based Dynamic Anti-Jamming Power Control in UAV Networks: An Effective Jamming Signal Strength Based Approach

This letter presents a new approach to enhance the anti-jamming capability of unmanned aerial vehicle (UAV)-assisted wireless data collection, specifically addressing the challenge of multi-jammers attacks in the context of imperfect channel state information (CSI). The proposed approach is designed for a scenario where a single UAV is responsible for collecting data from multiple ground sensors, while encountering multi-jammers from a ground-based adversary. The objective is to maximize the average data rate of all sensors while ensuring a sufficient amount of data is collected from each sensor. The optimization problem is formulated as a mixed-integer non-convex problem, and successive convex approximation(SCA) technique is employed, which jointly optimizes the collection schedule, power control and UAV trajectory. Numerical simulations demonstrate that the proposed approach outperforms in the presence of interference and imperfect CSI.

Yuan Bian

Jianbo Hu

Peng Zhang

Shuo Wang

Yingyang Wang

Jiping Cong

Chaoqi Fu

Joint Trajectory Control, Power Control and Collection Schedule in UAV-assisted Anti-jamming Wireless Data Collection With Imperfect CSI

Mingjun Sun

Zeng Li

Shaochuan Wu

Ruofei Ma

Litong Jiang

A Matrix-Inverse-Free WMMSE Algorithm to MISO Beamforming Based on Quasi-Newton

基于拟牛顿法的免矩阵求逆MISO波束成形WMMSE算法

Mayuka Chiba

Tadashi Nakano

Performance Analysis of a Biologically Inspired Collective Gradient Detection Method for a Bio-nanomachine Cluster

生物纳米机器集群的生物启发式集体梯度检测方法性能分析

Pedro H. C. de Souza

Luciano Mendes

Doppler Shift Compensation Assisted by RIS Systems in High-mobility Communications

RIS系统辅助的高移动性通信中的多普勒频移补偿

Nway Nway Ei

Kitae Kim

Yan Kyaw Tun

Zhu Han

Choong Seon Hong

Data Service Maximization in Space-Air-Ground Integrated 6G Networks

空间-空-地一体化6G网络中的数据服务最大化

Wenyu Wang

Lei Zhu

Yuantao Gu

Yufan Chen

Xingyu Zhou

Lu Yu

Adversarial Samples Detection Based on Feature Attribution and Contrast in Modulation Recognition

基于特征归因和对比的调制识别对抗样本检测

Abbreviation	IEEE Commun. Lett.
Journal Impact	3.72
Quartiles(Global)	TELECOMMUNICATIONS(Q2)

ISSN	1089-7798, 1558-2558, 2373-7891
h-index	164

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: 1997，Proportion of original research papers: 100.00%，Self Citation Rate:8.10%， Gold OA Rate: 14.01%
Average review cycle	网友分享经验：平均2.8个月
Average recruitment ratio	网友分享经验：75%

IEEE Communications Letters

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