A Fast Converging and Low Complexity Learning-Based MMSE-IRC Detection Algorithm for Massive MIMO

Massive MIMO is a pivotal technology for 5G and beyond, significantly enhancing spectrum efficiency and system capacity by deploying numerous receiving antennas at the base station. However, the increasing number of antennas imposes strong requirements for effective interference suppression in uplink MIMO detection. While traditional MMSE-IRC provides excellent performance in mitigating interference, its computational complexity scales cubically with the number of receiving antennas, limiting its practical applicability in massive MIMO systems. This paper presents a learning-based algorithm employing Mini-batch Gradient Descent (MBGD) to solve the weight matrix of MMSE-IRC, complemented by convergence acceleration strategies that incorporate adaptive initial learning rate determination and noise-plus-interference power regularization. Convergence and robustness of the proposed algorithm are validated across diverse modulation schemes, interference levels, and antenna configurations. Performance results indicate that, under high interference conditions, the iteration count of the proposed method is merely 12.5% that of traditional MBGD, while its BER performance exceeds that of conventional MMSE-IRC by approximately 0.5 dB, achieving a reduction in complexity from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O\left(N_{r x}^{3}\right)$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O\left(N_{t x} N_{r x}\right)$</tex>.