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Efficient Low-Complexity Antenna Selection Algorithms in Multi-User Massive MIMO Systems with Matched Filter Precoding

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journal contribution
posted on 2020-04-07, 15:01 authored by Z Abdullah, CC Tsimenidis, G Chen, M Johnston, JA Chambers
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.


10.13039/501100000266-Engineering and Physical Sciences Research Council; M3NETs



IEEE Transactions on Vehicular Technology ( Volume: 69 , Issue: 3 , March 2020 )


  • VoR (Version of Record)

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IEEE Transactions on Vehicular Technology






2993 - 3007


Institute of Electrical and Electronics Engineers (IEEE)





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