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. Preprints2018, 2018120253. https://doi.org/10.20944/preprints201812.0253.v1
Wang, T.; Zhang, L.; Liew, S.C. Deep Learning for Joint MIMO Detection and Channel Decoding. Preprints 2018, 2018120253. https://doi.org/10.20944/preprints201812.0253.v1
Wang, T.; Zhang, L.; Liew, S.C. Deep Learning for Joint MIMO Detection and Channel Decoding. Preprints2018, 2018120253. https://doi.org/10.20944/preprints201812.0253.v1
APA Style
Wang, T., Zhang, L., & Liew, S.C. (2018). Deep Learning for Joint MIMO Detection and Channel Decoding. Preprints. https://doi.org/10.20944/preprints201812.0253.v1
Chicago/Turabian Style
Wang, T., Lihao Zhang and Soung Chang Liew. 2018 "Deep Learning for Joint MIMO Detection and Channel Decoding" Preprints. https://doi.org/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.
Keywords
deep learning; MIMO detection; channel decoding; deep neural network
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
18 June 2019
Commenter:
aziza
The commenter has declared there is no conflict of interests.
Comment:
Hello sir, I hope you're fine, I'm very interested in your article, I'm doing my master project and I need your codea.
Thank you.
Yours sincerely
Commenter: aziza
The commenter has declared there is no conflict of interests.
Thank you.
Yours sincerely