Preprint Article Version 1 This version is not peer-reviewed

Deep Learning for Joint MIMO Detection and Channel Decoding

Version 1 : Received: 19 December 2018 / Approved: 20 December 2018 / Online: 20 December 2018 (13:08:27 CET)

How to cite: Wang, T.; Zhang, L.; Liew, S.C. Deep Learning for Joint MIMO Detection and Channel Decoding. Preprints 2018, 2018120253 (doi: 10.20944/preprints201812.0253.v1). Wang, T.; Zhang, L.; Liew, S.C. Deep Learning for Joint MIMO Detection and Channel Decoding. Preprints 2018, 2018120253 (doi: 10.20944/preprints201812.0253.v1).

Abstract

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.

Subject Areas

deep learning; MIMO detection; channel decoding; deep neural network