Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach  

作　　者：Zhao Jiwei Chen Jiacheng Sun Zeyu Shi Yuhang Zhou Haibo Xuemin(Sherman)Shen 

机构地区：[1]School of Electronic Science and Engineering,Nanjing University,Nanjing 210023,China [2]College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310058,China [3]Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen 518000,China [4]Department of Electrical and Computer Engineering,University of Waterloo,Waterloo,Ontario,N2L 3G1,Canada

出　　处：《China Communications》2024年第4期10-22,共13页中国通信（英文版）

基　　金：supported in part by the National Natural Science Foundation Original Exploration Project of China under Grant 62250004;the National Natural Science Foundation of China under Grant 62271244;the Natural Science Fund for Distinguished Young Scholars of Jiangsu Province under Grant BK20220067;the Natural Sciences and Engineering Research Council of Canada (NSERC)

摘　　要：As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

关 键 词：beamforming complex neural networks deep learning FD-RAN 

分 类 号：TN929.5[电子电信—通信与信息系统]