Discrete Diffusion-Based Sampling for Massive MIMO Detection

In this paper, we study a sampling-based detection strategy for massive multiple-input multiple-output (MIMO) systems, driven by a modified discrete diffusion model formulated as an analytical, non-learning sampling process. Built upon this framework, the proposed discrete diffusion-based sampling (DDS) algorithm improves decoding performance by leveraging residual-dependent sampling, compared to the independent randomized successive interference cancellation (SIC). Specifically, the modified diffusion model incorporates a shortcut perturbation toward the SIC solution, a forward diffusion step to enhance diversity, and step-wise alignment with the perturbed received signal. Within this framework, the DDS algorithm further adopts one-dimensional discrete Gaussian distribution, involving a reformulated discrete Gaussian noise and an explicitly characterized sampling range, but retains computational complexity amenable to practical deployment. Moreover, we theoretically demonstrate an improved expected decoding radius over randomized SIC. Finally, simulation results based on massive MIMO detection are presented to confirm performance gain of the proposed DDS algorithm.