A MIMO Detector With Deep Learning in the Presence of Correlated Interference
Junjuan Xia, Ke He, Wei Xu, Shengli Zhang, Lisheng Fan, George K. Karagiannidis
Abstract
In this paper, we investigate the classical detection problem for vehicle networks with multiple antennas, by considering practical communication scenarios, where the interfering signals are correlated over time or frequency. In such cases, the conventional detector requires to estimate the joint distribution of the interfering signals, which imposes a huge computational complexity. To tackle this issue, we propose the joint use of a maximum likelihood detector (MLD) and a deep convolutional neural network (DCNN), where MLD is used to produce an initial detection result and DCNN improves the detection by exploiting the local correlation to suppress the interference. Furthermore, the improved DCNN is enhanced by devising the loss function through the cross-entropy of the detection, which can help to suppress the interfering signals and simultaneously force the residual interference to approach the Gaussian distribution. Simulation results are presented to verify the effectiveness of the proposed detector compared to the conventional one. The trained model and source code for this work are available at https://github.com/skypitcher/project_dcnnmld.