移动通信2024，Vol.48Issue(4)：54-60,7.DOI:10.3969/j.issn.1006-1010.20240410-0001
基于神经网络的RIS通感一体编码优化方法
Neutral Network-based RIS Coding Optimization Method for ISAC
摘要
Abstract
The integrated sensing and communication(ISAC)technology based on reconfigurable intelligent surface(RIS)can effectively enhance the overall system communication and sensing capabilities through direction of arrival(DOA)and beamforming techniques.To address the challenges of high computational complexity and constrained design flexibility in RIS encoding optimization,this paper proposes a neural network-based approach for coding optimization in RIS-enabled ISAC systems.This approach employs a neural network based on an asynchronous space-time coding metasurface to reduce the DOA estimation error to 0.22° under low signal-to-noise ratio(SNR)conditions of-12 dB.Furthermore,by utilizing a cascaded neural network driven by free-form design indicators,it accomplishes high-precision beamforming optimization with an error of merely 0.025.This method offers a solution with low complexity and high real-time performance for the coding optimization in RIS-enabled ISAC systems.关键词
智能超表面/通感一体化/深度学习/循环神经网络/DOA估计/编码优化Key words
Reconfigurable intelligent surface/integrated sensing and communications/deep learning/RNN/DOA estimation/coding optimization分类
电子信息工程引用本文复制引用
关东方,卞小贝,谷紫洋,陈冠潮,安康..基于神经网络的RIS通感一体编码优化方法[J].移动通信,2024,48(4):54-60,7.基金项目
国家自然科学基金面上项目"信息超材料宽角散射增强及雷达无源干扰技术"(62271493) （62271493）
