Discrete Diffusion for Port Selection in Multiuser Pinching-Antenna Systems

2026-09-26

AI Summary

This study addresses the large-scale non-convex mixed-integer challenge of jointly optimizing port selection and precoding in multi-user MIMO systems. We propose a solution framework based on discrete diffusion models that reformulates combinatorial optimization as sampling from a target distribution. The core innovation lies in introducing a novel potential function learning mechanism driven by local objective differences, integrated with the Metropolis-Hastings criterion to guide parallel sampling without requiring optimal label supervision. Experimental results demonstrate that the proposed method achieves a sum rate within merely 0.3% of exhaustive search while accelerating computation by 150 times. Furthermore, it operates 18.8 times faster than greedy algorithms, exhibiting significantly superior overall performance compared to existing state-of-the-art approaches.

Abstract

Pinching-antenna systems (PASS) reconfigure wireless channels by activating radiating elements at selected locations along dielectric waveguides. Port selection is a core issue in improving the performance of multiuser PASS systems. In this paper, we study joint port selection and precoder design for multiuser sum rate maximization, which leads to a large-scale nonconvex mixed-integer problem coupling discrete port selection with continuous precoder design. We propose a discrete diffusion method that does not require optimal or near-optimal port selection solutions as training labels and recasts the resulting optimization problem as sampling from a target distribution. Instead, we learn a potential function from local objective differences between neighboring feasible realizations. The learned potential is incorporated into a Metropolis--Hastings sampling rule to guide a parallel diffusion process while preserving the port selection constraints. Simulation results show that the proposed method approaches exhaustive search within a $0.30\%$ sum rate gap while achieving over $150\times$ speedup, and outperforms state-of-the-art methods including greedy and beam search. In particular, it also achieves an $18.8\times$ speedup over greedy search.

Problem

• Pinching-antenna systems
• Port selection
• Precoder design
• Sum rate maximization
• Mixed-integer optimization

Innovation

• Discrete Diffusion
• Pinching-Antenna Systems
• Port Selection
• Metropolis-Hastings Sampling
• Mixed-Integer Optimization