Deep Learning Based Antenna Selection for MIMO SDR System

Sensors

Lei Huang¹  Huancong Luo¹  Shida Zhong¹  Jihong Zhang¹  Peichang Zhang¹  Haogang Feng¹  Jiajun Xu¹  Tao Yuan² 

[1] College of Electronics and Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen 518060, China;Guangdong Provincial Mobile Terminal Microwave and Millimeter Wave Antenna Engineering Research Center, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China;

关键词: antenna selection;    deep learning;    multiple-input multiple-output (MIMO);    software defined radio (SDR);    deep neural network (DNN);   

DOI  :  10.3390/s20236987

来源: DOAJ

【 摘 要 】

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

【 授权许可】

Unknown