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.

翻译：共轭梯度（CG）方法在大规模MIMO检测中的实现面临计算挑战，尤其是在用户数量众多且信道相关的情况下。本文从有限精度视角提出一种低计算复杂度的CG检测方法。首先，我们开发了一种有限精度共轭梯度（FP-CG）检测方法，以缓解每次CG迭代的计算瓶颈，并提供其可达精度、收敛性和计算复杂度分析，揭示有限精度算术的影响。同时给出一种实用的启发式方法以选择合适的精度。其次，为进一步减少迭代次数，我们提出一种联合有限精度与块雅可比预条件的CG（FP-BJ-CG）检测方法，并提供了相应的性能分析。最后，仿真结果验证了理论洞察，并展示了所提方法的优越性。