Deep learning based low complexity joint antenna selection scheme for MIMO vehicular adhoc networks

In this paper, we propose deep learning based transmit and receive antenna selection scheme for multiple-inputmultiple-output vehicular adhoc networks (MIMO-VANETs). The dynamic nature of VANETs instills the need for regular updates in antenna selections, moreover massive computations involved for antenna selection due to changes in channel statistics results in large delay in message communication. The delay in antenna selection in case of conventional approaches can be reduced substantially by automating the redundant computations using deep learning approach. In this work norm-based low complexity joint transmit and receive antenna selection along with its implementation through convolution neural network has been proposed. The proposed supervised deep learning approach to select a group of antennas to enhance capacity and minimize delay has shown encouraging results in MIMO VANET scenario as compared to the conventional antenna selection scheme.