| Issue |
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|
|
|---|---|---|
| Article Number | 04007 | |
| Number of page(s) | 5 | |
| Section | Communication Technology | |
| DOI | https://doi.org/10.1051/matecconf/202133604007 | |
| Published online | 15 February 2021 | |
Deep learning-aided high-precision data detection for massive MU-MIMO systems
College of Computer, National University of Defence Technology, 410073 Changsha, China
* Corresponding author: zcxing@nudt.edu.cn,
The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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