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Efficient Low-Complexity Antenna SelectionAlgorithms in Multi-User Massive MIMO Systemswith Matched Filter Precoding

Lookup NU author(s): Zaid Abdullah, Professor Harris Tsimenidis, Dr Martin JohnstonORCiD, Professor Jonathon Chambers

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Abstract

In this work, two efficient low complexity Antenna Selection (AS) algorithms are proposed for downlink Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) systems with Matched Filter (MF) precoding. Both algorithms avoid vector multiplications during the iterative selection procedure to reduce complexity. Considering a system with N antennas at the Base Station (BS) serving K single-antenna users in the same time-frequency resources, the first algorithm divides the available antennas into K groups, with the kth group containing the N/K antennas that have the maximum channel norms for the kth user. Therefore, the Signal-to-Interference plus Noise Ratio (SINR) for the kth user can be maximized by selecting a subset of the antennas from only the kth group, thereby resulting in a search space reduction by a factor of K. The second algorithm is a semiblind interference rejection method that relies only on the signs of the interference terms, and at each iteration the antenna that rejects the maximum number of interference terms will be selected. The performance of our proposed methods is evaluated under perfect and imperfect Channel State Information (CSI) and compared with other low complexity AS schemes in terms of the achievable sum rate as well as the energy efficiency. In particular, when the Signal-to-Noise Ratio (SNR) is 10 dB, and for a system with 20 MHz of bandwidth, the proposed methods outperform the case where all the antennas are employed by 108.8 and 49.2 Mbps for the first and second proposed algorithms, respectively, given that the BS has perfect CSI knowledge and is equipped with 256 antennas, out of which 64 are selected to serve 8 single-antenna users.

Publication metadata

Author(s): Abdullah Z, Tsimenidis CC, Chen G, Johnston M, Chambers JA

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Vehicular Technology

Year: 2020

Volume: 69

Issue: 3

Pages: 2993-3007

Print publication date: 12/03/2020

Online publication date: 14/01/2020

Acceptance date: 22/12/2019

ISSN (print): 0018-9545

ISSN (electronic): 1939-9359

Publisher: IEEE

URL: https://doi.org/10.1109/TVT.2020.2966565

DOI: 10.1109/TVT.2020.2966565

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