Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communications

TL;DR

This paper introduces a Maximum-Likelihood Estimation Inference framework based on diffusion models to improve signal detection in wireless communications, outperforming existing estimators in scenarios with unknown noise.

Contribution

It proposes a novel MLEI framework utilizing diffusion models' loss function to accurately infer maximum-likelihood points in discrete signal spaces.

Findings

The framework achieves higher detection accuracy than state-of-the-art methods.

Numerical results validate the effectiveness of the proposed approach in near-field communication.

The method handles unknown noise characteristics effectively.

Abstract

Generative Artificial Intelligence (GenAI) models, with their powerful feature learning capabilities, have been applied in many fields. In mobile wireless communications, GenAI can dynamically optimize the network to enhance the user experience. Especially in signal detection and channel estimation tasks, due to digital signals following a certain random distribution, GenAI models can fully utilize their distribution learning characteristics. For example, diffusion models (DMs) and normalized flow models have been applied to related tasks. However, since the DM cannot guarantee that the generated results are the maximum-likelihood estimation points of the distribution during the data generation process, the successful task completion rate is reduced. Based on this, this paper proposes a Maximum-Likelihood Estimation Inference (MLEI) framework. The framework uses the loss function in the forward diffusion process of the DM to infer the maximum-likelihood estimation points in the discrete space. Then, we present a signal detection task in near-field communication scenarios with unknown noise characteristics. In experiments, numerical results demonstrate that the proposed framework has better performance than state-of-the-art signal estimators.