Model-Driven Deep Learning for Massive Space-Domain Index Modulation MIMO Detection  

作　　者：Ping Yang Qin Yi Yiqian Huang Jialiang Fu Yue Xiao Wanbin Tang 

机构地区：[1]The National Key Laboratory of Science and Technology on Communications,University of Electronic Science and Technology of China,Sichuan 611731,China

出　　处：《China Communications》2023年第10期43-57,共15页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant U19B2014;the Sichuan Science and Technology Program under Grant 2023NSFSC0457;the Fundamental Research Funds for the Central Universities under Grant 2242022k60006.

摘　　要：In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.

关 键 词：deep learning generalized spatial modulation index modulation massive MIMO message passing orthogonal approximate 

分 类 号：TN929.5[电子电信—通信与信息系统] TN911.3[电子电信—信息与通信工程]