Low‐complexity linear massive MIMO detection based on the improved BFGS method

Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization prob...

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Bibliographic Details
Published in:IET communications Vol. 16; no. 14; pp. 1699 - 1707
Main Authors: Li, Lin, Hu, Jianhao
Format: Journal Article
Language:English
Published: Stevenage John Wiley & Sons, Inc 01.08.2022
Subjects:
Algorithms
Approximation
Complexity
Error detection
MIMO communication
Newton methods
Optimization
Random variables
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Summary:Linear minimum mean square error (MMSE) detection achieves a good trade‐off between performance and complexity for massive multiple‐input multiple‐output (MIMO) systems. To avoid the high‐dimensional matrix inversion involved, MMSE detection can be transformed into an unconstrained optimization problem and then solved by efficient numerical algorithms in an iterative way. Three low‐complexity Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) quasi‐Newton methods are proposed to iteratively realize massive MIMO MMSE detection without matrix inversion. The complexity can be reduced from O(K3)$\mathcal {O}(K^{3})$ to O(LK2)$\mathcal {O}(LK^{2})$, where K and L denote the number of users and iterations, respectively. Leveraging the special properties of massive MIMO, the authors first explore a simplified BFGS method (named S‐BFGS) to alleviate the computational burden in the search direction. For lower complexity, BFGS method with the unit step size (named U‐BFGS) is presented subsequently. When the base station (BS)‐to‐user‐antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U‐S‐BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate convergence. Simulation results verify that the proposed detection scheme can achieve near‐MMSE performance with a small number of iterations L as low as 2 or 3.
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ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12419