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Deep Learning Based Antenna Selection for MIMO SDR System †
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DOI：10.3390/s20236987.png)
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Abstract
En 中文
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)
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