Zhang, Jianjun;
Masouros, Christos;
Rodrigues, Miguel;
(2022)
Robust Symbol-Level Precoding Beyond CSI Models: A Probabilistic-Learning Based Approach.
In:
2021 IEEE Global Communications Conference (GLOBECOM).
IEEE: Madrid, Spain.
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Abstract
The use of large-scale antenna arrays poses great difficulties in obtaining perfect channel state information (CSI) in multi-antenna communication systems, which is essential for precoding optimization. To tackle this issue, in this paper we propose a probabilistic-learning based approach (PLA), aiming at alleviating the requirement of perfect CSI. The rationale is that the existing precoding algorithms that output a single precoder are often overconfident in their abilities and the obtained CSI. To avoid overconfidence, we incorporate the idea of regularization in machine learning (ML) into precoding models, so as to limit representative abilities of the precoding models. Compared to the state-of-the-art robust precoding designs, an important advantage of PLA is that CSI uncertainty models are not required. As a specific application of PLA, we design an efficient robust symbol-level hybrid precoding algorithm for the millimeter wave system and confirm the effectiveness of PLA via simulations.
| Type: | Proceedings paper |
|---|---|
| Title: | Robust Symbol-Level Precoding Beyond CSI Models: A Probabilistic-Learning Based Approach |
| Event: | IEEE Global Communications Conference (GLOBECOM) |
| Location: | Madrid, SPAIN |
| Dates: | 7 Dec 2021 - 11 Dec 2021 |
| ISBN-13: | 9781728181042 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GLOBECOM46510.2021.9685545 |
| Publisher version: | https://doi.org/10.1109/GLOBECOM46510.2021.9685545 |
| 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: | Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Telecommunications, Computer Science, Engineering, Probabilistic precoding, probabilistic-learning, robust symbol-level precoding, millimeter wave communication, MILLIMETER-WAVE COMMUNICATIONS, GREEN SIGNAL POWER, BEAM ALIGNMENT, INTERFERENCE, FEEDBACK, 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/10157889 |
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