CNN-Based Receiver Architecture for Full-Duplex MIMO Communication Systems

In the domain of wireless communication, Full-Duplex Multiple-Input Multiple-Output (FD-MIMO) systems have emerged as a promising technology for achieving higher spectral efficiency and increased data rates. Conventional FD-MIMO systems employ complex receivers for channel estimation, self-interference cancellation, and precoding. However, these conventional receivers often struggle to adapt to dynamic channel conditions and impose computational challenges. This paper utilizes Convolutional Neural Networks (CNNs) as a replacement for traditional receivers in FD-MIMO systems. The proposed CNN-based receiver demonstrates adaptability and robustness, enabling it to handle the challenges of channel estimation, self-interference cancellation, and precoding in real-time. Our results show the performance of the CNN-based receiver, which significantly outperforms a conventional receiver that uses Kalman Filter for channel estimation and Zero Forcing for pre-coding in terms of bit error rate (BER) and overall system efficiency. The proposed receiver enhanced the average BER by 43 % as compared to the conventional receiver. Results have shown that the proposed receiver is robust against pilot contamination due to synchronization errors. The use of convolutional neural networks is a promising approach to replace the conventional receiver as it has the ability to learn and mitigate the effect of changes in the environment.