Discrete Diffusion for Codebook-Based Beam Candidate Generation

TL;DR

This paper introduces a history-conditioned discrete diffusion model for generating beam candidates in limited-probing mmWave systems, improving performance under mobility and noisy feedback.

Contribution

It develops a novel diffusion-based approach that learns from probing histories to better generate beam candidates in constrained environments.

Findings

Achieves higher SNR and lower beam-miss probability.

Outperforms baselines especially in low-probing regimes.

Reduces conditional probe regret.

Abstract

Millimeter-wave (mmWave) communication enables high data rates through large bandwidths and highly directional beamforming, but its sensitivity to blockage and mobility makes reliable beam alignment a central challenge. Limited-probing beam management is a fundamental problem in codebook-based mmWave systems, where only a small subset of beams can be evaluated simultaneously, and the serving decision is restricted to the probed set. Under mobility and noisy feedback, this leads to a sequential and partially observable decision problem in which performance depends critically on the quality of the proposed beam candidates. In this paper, we consider limited-probing beam management and develop a history-conditioned discrete denoising diffusion probabilistic model for beam candidate generation. The proposed method learns from logged probing histories a conditional distribution over promising beam indices, which is then used to construct probing candidates online. Numerical analysis shows that the proposed approach consistently achieves better signal-to-noise ratio, beam-miss probability, and conditional probe regret under tight probing budgets compared with strong learning-based and discriminative baselines. The gains are especially pronounced in low-probing regimes, where accurate candidate generation is most critical.