Deep Learning Based Antenna Selection for MIMO SDR System

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.

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

“…While the majority of the works have focused on theoretical analysis, some works have carried out hardware implementation of ML for AS. For example, Zhong et al demonstrated the use of DNN for AS by implementing a DL AS MIMO aided software defined radio (SDR) system [160] and the use of k-NN for AS in a real-time system was presented in [163]. The use of DNN for AS was considered in [164] in order to effectively extrapolate downlink channels from the partial uplink channels.…”

Application of Machine Learning in Electromagnetics: Mini-Review

“…For modern MIMO communication transmit antennae selection, a learn-to-select (L2S) approach was implemented by Diamantaras et al [75] where achieving the optimal uniform linear array of antennas was expensive, both from the design and cost of materials perspectives. A DNN-based approach was implemented by Zhong et al [76], which seems to outperform the conventional norm-based approach for antenna selection in MIMO software-defined radio systems by 53%. Aside from MIMO technology, ML was used previously [77], where the Gaussian Mixture Model was adopted to sort the features of an RF fingerprinting dataset, and later, an SVM was used to classify the antenna for the classification and wireless identification of different RF devices.…”

Designing the MIMO SDR-based Antenna Array for 5G Telecommunication

“…Multiple-input-multiple-output becomes the most challenging technology in the 5G and the upcoming 6G telecommunication applications, given the ability of increasing the quality of communication and the rate of data [1], [2].…”

“…Much research such as [2]- [7] focus mostly on simulations and theorical analysis ignoring the system implementation because of its complexity, power consumption and low cost. the software defined radio has been found to avoid such limitations [1], [3]. More specially, by using SDR flexibility, reconfigurability and Rapid prototyping are covered to support new standards and protocols [8]- [10].…”