Mohammad, A;
Masouros, C;
Andreopoulos, Y;
(2023)
A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights.
IEEE Open Journal of the Communications Society
, 4
pp. 1334-1349.
10.1109/OJCOMS.2023.3285790.
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Abstract
This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory trade-off, by quantizing a scalable percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield ~3.46× and ~2.64× model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer ~20× and ~10× computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.
| Type: | Article |
|---|---|
| Title: | A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/OJCOMS.2023.3285790 |
| Publisher version: | https://doi.org/10.1109/OJCOMS.2023.3285790 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Symbol-level-precoding, constructive interference, power minimization, deep neural networks (DNNs), stochastic quantization (SQ) |
| 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/10173291 |
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