A Low Complexity Beam Selection Method for MmWave Massive MIMO Systems

The levels of inter-user interference (IUI) usually vary due to the different geographical locations of users, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade-off between complexity and system perfor...

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Bibliographic Details
Published in2023 International Conference on Ubiquitous Communication (Ucom) pp. 30 - 35
Main Authors Zhu, Chunhua, Ji, Qinwen, Guo, Xinying
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2023
Subjects
Beam selection
Candidate beam set
Inter-user interference
Interference
Massive MIMO
Millimeter wave MIMO
Millimeter wave technology
MIMO
Power distribution
Simulation
System performance
User grouping
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Summary:The levels of inter-user interference (IUI) usually vary due to the different geographical locations of users, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade-off between complexity and system performance. Considering the discrepancy of IUI comprehensively, a low complexity beam selection method for millimeter-wave massive multiple-input multiple-output (MIMO) systems is proposed. Firstly, a candidate beam set (CBS) is constructed in light of the sparsity and power distribution of the beamspace channel, thereby all users are classified into non-interfering users (NIUs), low-interference users (LIUs) and high-interference users (HIUs). For NIUs, the beams with large power are selected from CBS under the magnitude maximization (MM) criterion; for LIUs, a novel MM criterion with a tabu list is designed, which can effectively tackle the drawback of assigning shared beams in traditional MM; for HIUs, the incremental algorithm based on the sum-rate maximization criterion is developed to find the optimal beams from CBS. Simulation results validate that the proposed method can achieve almost the same sum-rate performance as the existing methods with the lower computational complexity.
DOI:10.1109/Ucom59132.2023.10257619