Performance evaluation of symbol detection algorithms in massive MIMO communication systems

Massive MIMO (mMIMO) is the essential technique for attaining the exponential increase in the data rate needed for future communication systems. These advancements have been significantly supported by progress in VLSI technology, which enables the integration of an enormous number of antennas and the complex signal processing essential for massive MIMO systems on a single chip. This work conducts a comprehensive analysis of the intricacy of seven matrix decomposition techniques for symbol detection in future mMIMO communication systems: ADMM-based infinity norm (ADMIN), Neumann series (NS), Newton iteration (NI), Jacobi iteration (Ja), improved Gauss-Seidel (IGS), conjugate gradient (CG), and QR decomposition (QR), against linear and near-optimal minimum mean square error (MMSE). QR, GS, Ja, and CG belong to linear algebraic methods based on matrix decomposition. MMSE and ADMIN are nonlinear optimization methods; NS and NI are iterative methods. The analysis examines into the complexity of these detection algorithms, considering the symbol error rate, the convergence rate, the initial solution vector, and the correlation factor. Performance evaluations are conducted on 8 and 16-user mMIMO systems with 64 and 128 base station antennas, modulation schemes (32-QAM and 64-QAM), iteration counts (p = 1, 2, 3, 4), correlation factors (α = 0.2, 0.4, and 0.6), and initial solution vectors (zero vector, D⁻¹ and W₂⁻¹ y_MF). The result clearly shows that if the initial solution and number of iterations are chosen properly, the IGS-based linear detector achieves near-optimal performance with a lower computational complexity of O(K²) compared to the nonlinear MMSE-based detector with a computational complexity of O(K³).