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A Deep-Learning Based Framework for Joint Downlink Precoding and CSI Sparsification

Zhang, J; Masouros, C; (2022) A Deep-Learning Based Framework for Joint Downlink Precoding and CSI Sparsification. In: IEEE International Conference on Communications. (pp. pp. 1974-1979). IEEE: Seoul, Republic of Korea. Green open access

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Abstract

Optimal pilot design to acquire channel state information (CSI) is of critical importance for FDD downlink massive MIMO systems, and is still an open problem. To tackle this issue, in this paper we propose a two-stage precoding approach based on reduced CSI (rCSI-TSP) design framework and an efficient algorithm, whose core is to obtain an optimal precoder while also sparsifying physical CSI (pCSI), so as to save on CSI estimation. The advantages of the rCSI-TSP framework are three-fold. First, the framework enables to simultaneously extract and exploit statistical and instantaneous CSI. Second, it guarantees the most needed rCSI can be obtained and thus avoids performance loss due to heuristic pilot design. Third, we tailor an efficient online deep-learning based method for the TSP framework, which paves the way for practical applications. As an example, we apply the framework to the multi-user symbol-level precoding (SLP) and verify performance improvements.

Type: Proceedings paper
Title: A Deep-Learning Based Framework for Joint Downlink Precoding and CSI Sparsification
Event: ICC 2022 - IEEE International Conference on Communications
Dates: 16 May 2022 - 20 May 2022
ISBN-13: 9781538683477
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICC45855.2022.9838955
Publisher version: https://doi.org/10.1109/ICC45855.2022.9838955
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: Wireless communication, Gold, Precoding, Conferences, Estimation, Massive MIMO, 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/10157893
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