Compressed Feedback Using Autoencoder Based on Deep Learning for D2D Communication Networks

Abstract

In this letter, we propose a feedback reduction scheme based on AutoEncoder and deep reinforcement learning for frequency division duplex (FDD) overlay device-to-device (D2D) communication networks. All D2D receivers and transmitters are equipped with an Encoder and Decoder, respectively, of the trained AutoEncoder. The D2D receivers compress the feedback information using the Encoder before transmitting while the transmitters decompress the received feedback information. We also employ a dueling deep Q network (DQN) to allow each D2D transmitter to autonomously determine whether to transmit data based on the decompressed feedback information. The performance of the proposed feedback reduction scheme is analyzed in terms of the average MSE of the AutoEncoder and the average sum-rate of a D2D communication network. Our numerical results show that the proposed feedback reduction scheme using the AutoEncoder can achieve 100%, 89%, and 86% of the average sum-rate of the perfect feedback scheme with no compression when the signal-to-noise ratio is 10dB, −5dB, and −20dB, respectively, while reducing the feedback by 50%.