Research Article: Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks

Date Published: May 1, 2019

Publisher: Public Library of Science

Author(s): Jia-xin Cai, Ranxu Zhong, Yan Li, Jie Zhang.

http://doi.org/10.1371/journal.pone.0215672

Abstract

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection.

Partial Text

Antenna system has been widely used in many application fields, such as public transportation, shopping malls, smart building, automotive radar, satellite communications, airplane landing, and astronomy [1–5]. The multiple-input multiple-output (MIMO) system also has received increasing interest in the area of wireless communication over the past few decades. Due to the rapid increasing of cellular mobile device usage and the limitation of computing power, antenna selection has attracted more and more attention recently. Antenna selection can keep a balance between communication performance and computational complexity. It can reduce the hardware cost and computational complexity, and keep enough gain rate or signal-to-noise ratio (SNR) at the same time. Usually, obtaining the optimal antenna subset needs to compare all possible combinations by exhaustive searching. It takes great amount of calculation and is very time-consuming. Because the exhaustive searching methods are impractical, many suboptimal models have been proposed. In general, existing methods can be categorized as two types, optimization-driven methods and data-driven methods.

Consider an MIMO system with Nt transmit antennas and Nr receive antennas. The channel matrix is denoted as H=[hij]∈CNr*Nt, where hij is the attenuation coefficient between the jth transmit antenna and the ith receive antenna. Let r(k)=[r1(k),r2(k),…,rNr(k)]T denote the received signal, t(k)=[t1(k),t2(k),…,tNt(k)]T denote the transmitter signal, and w(k)=[w1(k),w2(k),…,wNr(k)]T denote the white Gaussian noise. Here k denotes the time count of a discrete time signal. Denote the SNR as ρ, then the MIMO system model can be presented as follows.
r(k)=ρNtH*t(k)+w(k)(1)

This work introduced a receiving antenna selection framework based on deep CNNs and the channel capacity criterion. The proposed methods used convolutional structure to extract rich features from the channel matrices. CNNs were used to train powerful classifiers for selecting antennas. The proposed approach was validated on simulated antenna system data. The proposed method outperformed the state-of-the-art baselines. Our future work will include the improvement of deep networks and the evaluation on real-life antenna systems.

 

Source:

http://doi.org/10.1371/journal.pone.0215672

 

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