Generalizing projected gradient descent algorithm for massive MIMO detection based on deep-learning

被引：0

作者：

Yongming, Huang ^{[1
,2
]}

Zheng, Wang ^{[1
,2
]}

机构：

[1] National Mobile Communications Research Laboratory, Southeast University, Nanjing

[2] School of Information Science and Engineering, Southeast University, Nanjing

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2024年 / 54卷 / 04期

关键词：

deep learning; denoising auto-encoder; massive multiple input multiple output(MIMO)detection; projected gradient descent;

D O I：

10.3969/j.issn.1001-0505.2024.04.020

中图分类号：

学科分类号：

摘要：

The projected gradient descent (PGD)-based detector, which consists of two basic operations, projection and gradient descent (GD), was studied to achieve the performance improvement for massive multiple input multiple output(MIMO)detection. In a PGD-based detector for massive MIMO system, since the projection and GD step have different loss functions, necessary compromise has to be made to balance them during iterations. For this reason, the generalized PGD (GPGD)method was proposed with flexible choices of projection and GD. Different from traditional way of performing projection and GD alternatively, GPGD implements projection after every multiple GD steps offers significant advantages. Meanwhile, the step-size of GD was also investigated for convergence efficiency. After that, by unfolding the proposed GPGD method with deep neural networks, the self-corrected auto-detector was established to achieve better decoding performance and efficiency. The simulation results show that the GPGD method achieves an apparent system gain and has a significant superiority. © 2024 Southeast University. All rights reserved.

引用

页码：961 / 971

页数：10

共 26 条

[1] Tariq F, Khandaker M R A, Wong K K, Et al., A speculative study on 6G[J], IEEE Wireless Communications, 27, 4, pp. 118-125, (2020)
[2] Jiang W, Han B, Habibi M A, Et al., The road towards 6G:A comprehensive survey[J], IEEE Open Journal of the Communications Society, 2, (2021)
[3] Larsson E G, Edfors O, Tufvesson F, Et al., Massive MIMO for next generation wireless systems[J], IEEE Communications Magazine, 52, 2, pp. 186-195, (2014)
[4] Emmert-Streib F, Yang Z, Feng H, Et al., An introductory review of deep learning for prediction models with big data[J], Frontiers in Artificial Intelligence, 3, (2020)
[5] Buduma N, Locascio N., Fundamentals of deep learning:Designing next-generation machine intelligence algorithms, (2017)
[6] Wang J J, Jiang C X, Zhang H J, Et al., Thirty years of machine learning:The road to pareto-optimal wireless networks[J], IEEE Communications Surveys and Tutorials, 22, 3, pp. 1472-1514, (2020)
[7] Otter D W, Medina J R, Kalita J K., A survey of the usages of deep learning for natural language processing [J], IEEE Transactions on Neural Networks and Learning Systems, 32, 2, pp. 604-624, (2021)
[8] Tse D, Viswanath P., Fundamentals of wireless communication, (2005)
[9] Agrell E, Eriksson T, Vardy A, Et al., Closest point search in lattices[J], IEEE Transactions on Information Theory, 48, 8, pp. 2201-2214, (2002)
[10] Damen M O, El Gamal H, Caire G., On maximum-likelihood detection and the search for the closest lattice point[J], IEEE Transactions on Information Theory, 49, 10, pp. 2389-2402, (2003)

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