Discrete diffusion speeds up port selection in multiuser antenna systems
Discrete Diffusion for Port Selection in Multiuser Pinching-Antenna Systems
Information Theory
Summary
Picking the best antenna ports in wireless systems that share space among users is a complex challenge. The authors present a new approach called discrete diffusion that helps select ports more efficiently by treating the problem as a sampling process guided by a learned function. This method runs much faster than previous techniques and gets very close to the best possible performance. It also works well alongside signal precoding to improve overall wireless data rates.
What this means in practice
- •For wireless network designers: Improve multiuser antenna configurations to maximize data rates while significantly reducing computation time.
- •For wireless hardware engineers: Design antenna control algorithms that efficiently select ports along dielectric waveguides for better channel reconfiguration.
Authors
Wenxuan Sun, Mingjie Shao, Yanqing Xu, Ya-Feng Liu
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.