Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimizatio...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2016-12, Vol.15 (12), p.8236-8248
Main Authors: Junyuan Wang, Huiling Zhu, Lin Dai, Gomes, Nathan J., Jiangzhou Wang
Format: Article
Language:English
Subjects:
Algorithms
Antennas
Array signal processing
beam allocation algorithm
Combinatorial analysis
Complexity
Complexity theory
Data transmission
Frequencies
massive multiple-input-multiple-output (MIMO)
Millimeter waves
MIMO
MIMO communication
Optimization
Resource management
service ratio
submodular optimization
sum data rate
Switched-beam based systems
Switches
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Summary:This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2016.2613517