IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN) 致力于及时出版高质量的手稿，推动认知通信和网络研究的最新发展。交易的重点将放在通信和网络控制各个方面的“认知”行为，从 PHY 功能（包括硬件）到应用程序（包括架构），以及在各种类型的通信网络和系统中，无论其类型如何流量、传输媒体、操作环境或通信设备的能力。 TCCN 将欢迎有关认知通信和网络系统的设计、分析、评估、实验和测试的论文。鼓励跨学科方法。专注于认知通信和网络的实验基础设施或工具的论文也将被考虑，前提是它们包含在通信或网络领域的重要原创贡献。

The IEEE Transactions on Cognitive Communications and Networking (TCCN) is dedicated to the timely publication of high-quality manuscripts that advance the latest developments in cognitive communications and networking research. The journal focuses on the 'cognitive' behaviors in various aspects of communication and network control, ranging from physical layer functionalities (including hardware) to applications (including architecture), applicable to various types of communication networks and systems, regardless of their traffic, transmission media, operational environments, or capabilities of communication devices. TCCN welcomes papers on the design, analysis, evaluation, experimentation, and testing of cognitive communication and networking systems, and encourages interdisciplinary approaches. Papers focusing on experimental infrastructures or tools for cognitive communications and networking will also be considered, provided they contain significant original contributions to the field of communications or networking.

《IEEE Transactions on Cognitive Communications and Networking》致力于及时出版高质量的手稿，推动认知通信和网络研究的最新发展。期刊重点关注通信和网络控制各个方面的“认知”行为，涵盖从物理层功能（包括硬件）到应用程序（包括架构）的广泛主题，适用于各种类型的通信网络和系统，无论其流量、传输媒体、操作环境或通信设备的能力如何。期刊欢迎有关认知通信和网络系统的设计、分析、评估、实验和测试的论文，并鼓励跨学科的方法。同时，专注于认知通信和网络的实验基础设施或工具的论文也将被考虑，前提是它们在通信或网络领域具有重要的原创贡献。

IEEE Transactions on Cognitive Communications and Networking

Wireless spectrum environment is complex and uncertain in practical scenarios. For spectrum resource management, there are two crucial challenges to be resolved. First, channel states are always time-varying, thus violent fluctuations of interference links bring difficulties to interference mitigation. Second, the optimization efficiency degrades when jointly considering frequency coordination and power adaption. In this paper, we focus on the interference-aware resource management in a dynamic radio environment. First, we propose a new metric named effective weighted aggregate interference to capture the fluctuations of time-varying interference links. Then, we investigate the joint channel selection and power assignment problem from the perspective of game theoretic, and propose a novel mechanism called action freezing to significantly improve the optimization efficiency. Furthermore, we develop a higher-order statistic optimization based multi-agent strategic learning (HSOMSL) algorithm to alleviate the effect of fluctuating payoffs and obtain stable solutions. Simulation results illustrate that the proposed algorithm has lower experienced interference, higher achievable throughput, and better energy efficiency compared to existing state-of-the-art algorithms. In addition, it is verified to be effective in mobile scenarios.

Yihang Du

Xiaoqiang Qiao

Yu Zhang

Lei Xue

Tao Zhang

Pengfei Ma

Chun Chen

Interference-Aware Spectrum Resource Management in Dynamic Environment: Strategic Learning With Higher-Order Statistic Optimization

Yanpeng Dai

Ling Lyu

Nan Cheng

Min Sheng

Junyu Liu

Xiucheng Wang

Shuguang Cui

Lin Cai

Xuemin Shen

A Survey of Graph-Based Resource Management in Wireless Networks -Part II: Learning Approaches

A Survey of Graph-Based Resource Management in Wireless Networks -Part I: Optimization Approaches

无线网络中基于图的资源管理综述——第I部分：优化方法

Incorporating both semantic level information and effectiveness level performance, the task-oriented semantic communication system has been designed for various tasks of different datatype. Although semantic communication improves the spectral utilization to some extent, indiscriminate transmission of semantic information for task-oriented semantic communication can still result in waste of wireless resources. In this paper, we propose an importance-aware joint source-channel coding (I-JSCC) framework for task-oriented semantic communications. A joint semantic-channel transmission (JSCT) mechanism is designed by selectively transmitting task-important features to reduce communication overhead. We define a new metric named task-oriented semantic spectral efficiency (TOSSE) to evaluate the effectiveness and efficiency of the proposed system, which measures the effective semantic information carried by each semantic symbol. An importance-aware semantic resource allocation problem is formulated to maximize the total TOSSE of all users by jointly optimizing the channel assignment and feature selection vector. To solve this problem, a knowledge-assisted proximal policy optimization (K-PPO) based reinforcement learning (RL) algorithm is proposed. The experimental results conducted on CIFAR100 dataset demonstrate the efficacy of the K-PPO algorithm, while also highlighting the superiority of the importance-aware semantic communication system in terms of the TOSSE.

Yining Wang

Shujun Han

Xiaodong Xu

Haotai Liang

Rui Meng

Chen Dong

Ping Zhang

Feature Importance-Aware Task-Oriented Semantic Transmission and Optimization

特征重要性感知的面向任务语义传输与优化

Chenyang Yi

Wei Xu

Yan Sun

Derrick Wing Kwan Ng

Zhaocheng Wang

Distributed Multiuser Precoding via Unfolding WMMSE for Multicell MIMO Downlinks

Zeyu Tian

Li Wang

Lianming Xu

Chen Xu

Aiguo Fei

Coded Caching for Reliable Map Dissemination in Symbiotic Communication Aided Emergency UAV Systems

编码缓存技术在应急无人机系统中可靠地图传播的共生通信中的应用

Bin Rao

Jie Hu

Kun Yang

Unmanned Aerial Vehicle Based Hybrid Backscatter and Wireless Powered Communications: 3D Trajectory Design and Resource Scheduling

基于无人机混合反向散射与无线供电通信的三维轨迹设计与资源调度

Yongjun Xu

Yingxiang Bai

Yunjian Jia

Haibo Zhang

Qingqing Wu

Chau Yuen

Robust Energy Efficiency Optimization for Double-RIS-Assisted Wireless-Powered Backscatter Communications

双RIS辅助无线能量收集反向散射通信的鲁棒能效优化

Mengyu Liu

Hong Ren

Cunhua Pan

Boshi Wang

Zhiyuan Yu

Ruisong Weng

Kangda Zhi

Yongchao He

Joint Beamforming Design for Double Active RIS-Assisted Radar-Communication Coexistence Systems

双主动智能反射面辅助雷达通信共存系统的联合波束成形设计

To meet the stringent latency and reliability requirements of vehicular applications (e.g., vehicle platooning and autonomous driving), reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) communications have received great attention recently. A major challenge in the RIS-aided V2X systems is channel aging effect, the mismatch between the channels at the channel estimation stage and the data transmission stage caused by the short coherence time of V2X channel. In this paper, we propose a deep learning (DL)-based phase shift control technique for RIS-aided V2X systems to overcome the channel aging effect. Key idea of the proposed Transformer-based RIS phase shift control (T-RPSC) is to predict the proper phase shifts from the past RIS-aided channels using Transformer. The attention mechanism in Transformer quantifies the long and short-term correlations between the input (i.e., past RIS-aided channels) and output (i.e., future RIS phase shifts) to assign proper attention weights to input data based on the measured correlations. In doing so, we can effectively extract temporally correlated features, thereby improving the transmission latency reduction performance of RIS phase shifts. From the simulation results, we demonstrate that T-RPSC achieves more than 23% latency reduction over the alternating direction method of multiplier (ADMM)-based phase shift control scheme.

Abbreviation	IEEE Trans. Cogn. Commun. Netw.
Journal Impact	7.59
Quartiles(Global)	TELECOMMUNICATIONS(Q1)

ISSN	2332-7731, 2372-2045
h-index	54

Publication Information	Publisher: Institute of Electrical and Electronics Engineers Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2015，Proportion of original research papers: 100.00%，Self Citation Rate:4.10%， Gold OA Rate: 7.48%

IEEE Transactions on Cognitive Communications and Networking

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