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Article

Deep Learning Based Antenna Selection for MIMO SDR System †

by
Shida Zhong
1,
Haogang Feng
1,
Peichang Zhang
1,*,
Jiajun Xu
1,
Huancong Luo
1,
Jihong Zhang
1,
Tao Yuan
2 and
Lei Huang
1
1
College of Electronics and Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen 518060, China
2
Guangdong Provincial Mobile Terminal Microwave and Millimeter Wave Antenna Engineering Research Center, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Luo, H.; Xu, J.; Zhang, J.; Zhang, P.; Huang, L. Deep Learning Based Antenna Selection Aided Space-Time Shift Keying Systems. In Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science (AICS 2019), New York, NY, USA, 12–13 July 2019.
Sensors 2020, 20(23), 6987; https://doi.org/10.3390/s20236987
Submission received: 4 November 2020 / Revised: 1 December 2020 / Accepted: 4 December 2020 / Published: 7 December 2020
(This article belongs to the Special Issue Multi-Antenna Techniques for 5G and beyond 5G Communications)
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Abstract

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

Share and Cite

MDPI and ACS Style

Zhong, S.; Feng, H.; Zhang, P.; Xu, J.; Luo, H.; Zhang, J.; Yuan, T.; Huang, L. Deep Learning Based Antenna Selection for MIMO SDR System. Sensors 2020, 20, 6987. https://doi.org/10.3390/s20236987

AMA Style

Zhong S, Feng H, Zhang P, Xu J, Luo H, Zhang J, Yuan T, Huang L. Deep Learning Based Antenna Selection for MIMO SDR System. Sensors. 2020; 20(23):6987. https://doi.org/10.3390/s20236987

Chicago/Turabian Style

Zhong, Shida, Haogang Feng, Peichang Zhang, Jiajun Xu, Huancong Luo, Jihong Zhang, Tao Yuan, and Lei Huang. 2020. "Deep Learning Based Antenna Selection for MIMO SDR System" Sensors 20, no. 23: 6987. https://doi.org/10.3390/s20236987

APA Style

Zhong, S., Feng, H., Zhang, P., Xu, J., Luo, H., Zhang, J., Yuan, T., & Huang, L. (2020). Deep Learning Based Antenna Selection for MIMO SDR System. Sensors, 20(23), 6987. https://doi.org/10.3390/s20236987

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