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.

Hyunsoo Kim

Seungnyun Kim

Jiao Wu

Byonghyo Shim

IEEE Transactions on Cognitive Communications and Networking

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。

Category	Quartile
计算机科学	1
计算机科学, 电信学	1

Transformer-Based RIS Phase Shift Control for Ultra-Low Latency V2X Systems

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