Mohammad, A;
Masouros, C;
Andreopoulos, Y;
(2021)
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding.
In:
Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM).
IEEE: Madrid, Spain.
(In press).
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Abstract
This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal `log' barrier function. The proximal `log' barrier function is derived from the strict power minimization formulation subject to signal-to-interference-plus-noise ratio (SINR) constraint. The proposed scheme exploits the known interference via symbol-level precoding (SLP) to minimize the transmit power and is named strict Symbol-Level-Precoding deep network (SLP-SDNet). The results show that SLP-SDNet outperforms the conventional block-level-precoding (Conventional BLP) scheme while achieving near-optimal performance faster than the SLP optimization-based approach
| Type: | Proceedings paper |
|---|---|
| Title: | An Unsupervised Learning-Based Approach for Symbol-Level-Precoding |
| Event: | 2021 IEEE Global Communications Conference (GLOBECOM) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://globecom2021.ieee-globecom.org/ |
| 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. |
| 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/10127372 |
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