Analyses for Signal Detection Algorithms and Corresponding VLSI Architectures Applied for Massive MIMO Systems

Abstract. In the field of 5G communications, the utilization of Massive MIMO has significantly improved the spectral efficiency. However, signal detection involves large-scale matrix inversion, which is computationally intensive. Traditional techniques for linear detection, such as Zero Forcing and Minimum Mean Square Error, struggle to effectively reduce computational complexity. Therefore, this paper introduces three methods to mitigate the complexity in signal detection: the Alternating Direction Method of Multipliers for conditions where the base stations quantities are nearly equivalent to the users quantities, the Neumann series expansion method that replaces matrix inversion, and iterative methods that approximate the true value through iterative updates. Future work should focus on optimizing the iterative expressions and incorporating tools such as deep learning to further reduce computational complexity and accurately retrieve the signal values.