Yu, Ercong and Zhu, Jinle and Li, Qiang and Liu, Zilong and Chen, Hongyang and Shamai (Shitz), Shlomo and Poor, H Vincent (2023) Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications. IEEE Transactions on Wireless Communications, 23 (7). pp. 6750-6764. DOI https://doi.org/10.1109/TWC.2023.3332617
Yu, Ercong and Zhu, Jinle and Li, Qiang and Liu, Zilong and Chen, Hongyang and Shamai (Shitz), Shlomo and Poor, H Vincent (2023) Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications. IEEE Transactions on Wireless Communications, 23 (7). pp. 6750-6764. DOI https://doi.org/10.1109/TWC.2023.3332617
Yu, Ercong and Zhu, Jinle and Li, Qiang and Liu, Zilong and Chen, Hongyang and Shamai (Shitz), Shlomo and Poor, H Vincent (2023) Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications. IEEE Transactions on Wireless Communications, 23 (7). pp. 6750-6764. DOI https://doi.org/10.1109/TWC.2023.3332617
Abstract
This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMAimposed constant-power downlink precoding, we propose an FASbased normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DLNBD) algorithm for adaptive codebook design and codebookindependent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DLNBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.
Item Type: | Article |
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Uncontrolled Keywords: | Load modulation arrays, multiuser MIMO systems, Deep Learning, codebook design, precoding, block diagonalization |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 10 Nov 2023 21:05 |
Last Modified: | 15 Jul 2024 12:04 |
URI: | http://repository.essex.ac.uk/id/eprint/36833 |
Available files
Filename: DL-Assisted MU-MIMO LM (with department).pdf