Yang, Junwen;
Li, Ang;
Liao, Xuewen;
Masouros, Christos;
(2024)
ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding.
IEEE Transactions on Vehicular Technology
, 73
(1)
1376 -1381.
10.1109/tvt.2023.3301241.
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Abstract
Constructive interference (CI)-based symbol-level precoding (SLP) is an emerging downlink transmission technique for multi-antenna communications systems, and its low-complexity implementations are of practical importance. In this paper, we propose an interpretable model-driven deep learning framework to accelerate the processing of SLP. Specifically, the network topology is carefully designed by unrolling a parallelizable algorithm based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM), attaining parallel and distributed architecture. Moreover, the parameters of the iterative PJ-ADMM algorithm are untied to parameterize the network. By incorporating the problem-domain knowledge into the loss function, an unsupervised learning strategy is further proposed to discriminatively train the learnable parameters using unlabeled training data. Simulation results demonstrate significant efficiency improvement of the proposed ADMM-SLPNet over benchmark schemes.
| Type: | Article |
|---|---|
| Title: | ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tvt.2023.3301241 |
| Publisher version: | https://doi.org/10.1109/TVT.2023.3301241 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Deep learning, deep unfolding, algorithm unrolling, model-driven, symbol-level precoding, ADMM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174857 |
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