Design of Energy Modulation Massive SIMO Transceivers via Machine Learning  

作　　者：Muhang Lan Jianhao Huang Han Zhang Chuan Huang 

机构地区：[1]National Key Laboratory of Science and Technology on Communications,University of Electronic Science and Technology of China,Chengdu 611731,China [2]The Future Network of Intelligence Institute and the School of Science and Engineering,the Chinese University of Hong Kong,Shenzhen 518172,China [3]Department of Electrical and Computer Engineering,University of California,Davis 95616,USA

出　　处：《Journal of Communications and Information Networks》2020年第3期358-368,共11页通信与信息网络学报（英文）

基　　金：The work was supported in part by the Key Area R&D Program of Guangdong Province with Grant No.2018B030338001;by the National Key R&D Program of China with Grant No.2018YFB1800800;y Natural Science Foundation of China with grant NSFC-61629101;by Guangdong Research Project No.2017ZT07X152;by Shenzhen Key Lab Fund No.ZDSYS201707251409055.

摘　　要：This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading,we propose a joint transceiver design method based on machine learning,requiring a limited number of channel realizations.In the proposed method,the multiple transmitters,the channel,and the receiver are represented with a deep neural network(NN),and an autoencoder is adopted to minimize the end-to-end transmission error probability.Besides,the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method.Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios,and is more robust against the channel parameters variation compared with the existing methods.

关 键 词：neural network(NN) energy modulation massive single-input multiple-output(SIMO) joint transceiver design confidence interval 

分 类 号：TN83[电子电信—信息与通信工程] TP181[自动化与计算机技术—控制理论与控制工程]