Gautam, PR and Zhang, L orcid.org/0000-0002-4535-3200 (2021) Hybrid Precoding for Partial-Full Mixed Connection mmWave MIMO. In: 2021 IEEE Statistical Signal Processing Workshop (SSP). 2021 IEEE Statistical Signal Processing Workshop (SSP), 11-14 Jul 2021, Rio de Janeiro, Brazil. IEEE , pp. 271-275. ISBN 978-1-7281-5768-9
Abstract
We propose hybrid precoding algorithm for less-investigated partial-full mixed connection (PFMC) architecture in millimeter wave (mmWave) multiple input multiple output (MIMO). The RF chains and the antennas are divided into various subgroups with full connection existing between the RF chains and the antennas of a particular subgroup. We cast the hybrid precoding problem for the PFMC architecture as a matrix factorization problem and propose an algorithm based on alternating minimization with a view to minimize the Euclidean distance between the hybrid precoding sub-matrix for each subgroup and the corresponding sub-matrix of the fully digital optimal precoder. The proposed precoder is able to produce energy efficiency better than the precoders for FC and PC architectures and the existing precoder for the PFMC architecture.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Aug 2021 13:23 |
Last Modified: | 16 Oct 2023 15:23 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SSP49050.2021.9513847 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177167 |