An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise

Abstract

In this paper, we propose an adaptive deep learning-based unmanned aerial vehicle (UAV) receiver design for coded multiple-input multiple-output (MIMO) systems, where the noise in the systems presents some correlation among time domain, which deteriorates the system transmission performance severely. To improve the system performance, we employ the linear convolutional code at the transmitter, and then propose an adaptive deep learning based iterative UAV receiver. The iterative UAV receiver contains three parts: the detector such as zero-forcing (ZF) or minimum mean square error (MMSE) detector, the deep convolutional neural network (DCNN) which can help suppress the noise by capturing the correlation characteristics among noise, and the decoder such as Viterbi decoding. In particular, the cyclic redundancy check (CRC) appended to the code can help control the iteration of the detection, DCNN and decoding, which leads to an adaptive implementation of receiver. Simulation results demonstrate that the proposed UAV receiver can achieve a much better bit error rate (BER) performance over conventional receivers with a reduced computational complexity.