An Improved Successive Filter-Based Dropping Algorithm for Massive MIMO With Max-Min Power Control

In line-of-sight massive MIMO, there are use cases where the channel vectors of some users become highly correlated. Highly correlated users lead to a large reduction in the sum-rate of linear and nonlinear precoders with max-min power control. To alleviate the loss in the sum-rate, some users can b...

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Bibliographic Details
Published in:IEEE communications letters 2021-09, Vol.25 (9), p.3099-3103
Main Authors: Farsaei, A., Gustavsson, U., Alvarado, A., Willems, F. M. J.
Format: Article
Language:English
Subjects:
Algorithms
Computational complexity
Correlated users
Correlation
Downlink
dropping algorithm
line-of-sight
Massive MIMO
max-min power control
MIMO (control systems)
MIMO communication
Power control
Precoding
Signal to noise ratio
Simulation
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Summary:In line-of-sight massive MIMO, there are use cases where the channel vectors of some users become highly correlated. Highly correlated users lead to a large reduction in the sum-rate of linear and nonlinear precoders with max-min power control. To alleviate the loss in the sum-rate, some users can be dropped and rescheduled. The optimal dropping strategy can be found by an exhaustive search. In this letter, a successive filter-based dropping algorithm (SFDA) is proposed, which improves upon the existing dropping algorithms in the literature. At each step, the user with the highest filter norm is dropped. By comparing the sum-rate of all the steps, the best set of dropped users is found. In contrast to previous threshold-based algorithms in the literature, SFDA does not require a predefined threshold for the spatial correlation of users. Compared to an exhaustive search, the complexity of SFDA is reduced. Simulations results show when a 100 antennas base station serves 10 users, SFDA improves the 5th percentile sum-rate compared to previous algorithms in the literature up to 6 bits/channel use.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3091676