A modified deep learning based MIMO communication for integrated sensing, communication and computing systems

Abstract

The future wireless networks are expected to be data-driven integrated sensing, communication and computing (ISCC) systems and benefit from employing deep learning to process the large amount of data with less resources and computational limit. As a result, deep learning based learning MIMO communication has been widely discussed in recent years and show fascinating performance advantages over human designed MIMO systems. However, we find that the autoencoder frameworks proposed in current deep learning based methods are not able to be implemented under multipath channels, which is the most common channel model in actual environment. To overcome this problem, a modified autoencoder framework is proposed in this paper. This proposed autoencoder framework introduces well designed convolutional neural layers into the widely used feedforward neural networks, and demonstrates significant performance improvements over traditional Turbo code method adopted in existing wireless MIMO systems. Moreover, computer simulations are performed and the proposed autoencoder framework is also demonstrated to be effective in SISO and MISO systems under AWGN and Rayleigh fading channels, which verifies the generality of the proposed autoencoder framework for end-to-end learning communication systems.