Mohammad, A;
Masouros, C;
Andreopoulos, Y;
(2022)
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach.
In:
2022 IEEE Wireless Communications and Networking Conference (WCNC).
(pp. pp. 429-434).
IEEE: Austin, TX, USA.
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Abstract
Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power. This paper proposes an unsupervised-learning based SLP that applies to quantized deep neural networks (DNNs). Rather than simply training a DNN in a supervised mode, our proposal unfolds a power minimization SLP formulation in an imperfect channel scenario using the interior point method (IPM) proximal 'log' barrier function. We use binary and ternary quantizations to compress the DNN's weight values. The results show significant memory savings for our proposals compared to the existing full-precision SLP-DNet with significant model compression of ~ 21× and ~ 13× for both binary DNN-based SLP (RSLP-BDNet) and ternary DNN-based SLP (RSLP-TDNets), respectively.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach |
| Event: | 2022 IEEE Wireless Communications and Networking Conference (WCNC) |
| Dates: | 10 Apr 2022 - 13 Apr 2022 |
| ISBN-13: | 9781665442664 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WCNC51071.2022.9771799 |
| Publisher version: | https://doi.org/10.1109/WCNC51071.2022.9771799 |
| 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: | Training, Tensors, Quantization (signal), Precoding, Symbols, Minimization, Downlink |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152966 |
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