As the size of base station antenna arrays continues to grow, even with linear processing algorithms, the computational complexity and power consumption required for massive MIMO equalization or precoding (EoP) matrix computation increase exponentially. This paper proposes an inverse matrix minibatch gradient descent (IM-MBGD) method for MIMO EoP. The proposed method leverages minibatch gradient descent to compute the pseudo-inverse of the Gram matrix instead of directly calculating the EoP matrix. Since the pseudo-inverse matrix has significantly lower dimensions compared to the EoP matrix, this approach effectively reduces computational complexity and accelerates convergence. Based on our convergence analysis, to further minimize the iteration time and improve the accuracy of the EoP matrix, we optimize the IM-MBGD method using a finite-alphabet neighborhood search. The finite-alphabet search performs neighborhood optimization to achieve the optimal solution. Compared to state-of-the-art alphabet-based equalizers, our proposed method achieves a 14-fold reduction in computational complexity for EoP matrix calculation. Furthermore, in terms of storage and the overall equalization process, our method reduces overhead by 80% compared to linear equalizers.

Matrix multiplication serves as the primary operation in massive multiple-input multiple-output (MIMO). However, with the continuous advancement of MIMO technology, the escalating computational complexity and the necessity for adaptable matrix multiplication in MIMO communication pose a formidable challenge. In this paper, we propose the Joint Serial-Parallel Dataflow (JSPD) mapping method for dynamic precision matrix multiplication, which relies on the utilization of Spatio-Temporal Transforms (STT) and Principal-Auxiliary Matrices (PAM). The data is initially processed serially and is mapped onto Processing Element (PE) arrays through STT transforms. Subsequently, both high-precision and low-precision components of the data are computed selectively in parallel, employing a combination of principal and auxiliary matrices. The matrix multiplication process is mapped onto the PE arrays, incorporating output-stationary and output-flow modes. Compared with the traditional fixed structure PE arrays, the JSPD mapping method improves the computational flexibility while increasing the PE cell work share ratio by 7.57% ∼ 21.86%. Meanwhile, JSPD reduces the hardware overhead by a maximum of 68% and improves hardware efficiency by 35.77%.