Finite-Precision Conjugate Gradient Method for Massive MIMO Detection
This work addresses the computational bottleneck of CG-based MIMO detection for large-scale systems, offering practical low-complexity solutions.
The authors propose finite-precision CG and preconditioned CG detectors for massive MIMO that reduce computational complexity while maintaining detection accuracy, with analysis of convergence and precision selection.
The implementation of the conjugate gradient (CG) method for massive MIMO detection is computationally challenging, especially for a large number of users and correlated channels. In this paper, we propose a low computational complexity CG detection from a finite-precision perspective. First, we develop a finite-precision CG (FP-CG) detection to mitigate the computational bottleneck of each CG iteration and provide the attainable accuracy, convergence, and computational complexity analysis to reveal the impact of finite-precision arithmetic. A practical heuristic is presented to select suitable precisions. Then, to further reduce the number of iterations, we propose a joint finite-precision and block-Jacobi preconditioned CG (FP-BJ-CG) detection. The corresponding performance analysis is also provided. Finally, simulation results validate the theoretical insights and demonstrate the superiority of the proposed detection.