Sparse Bayesian Generative Modeling for Joint Parameter and Channel Estimation

TL;DR

This paper introduces a low-complexity sparse Bayesian generative model that jointly estimates wireless channels and physical parameters, enhancing sensing and communication performance without extra computational cost.

Contribution

The work develops a physics-informed, conditionally Gaussian sparse Bayesian model for simultaneous channel and environment parameter estimation, reducing complexity.

Findings

Effective joint estimation of channels and physical parameters

Low computational complexity compared to existing methods

Improved accuracy in sensing and communication tasks

Abstract

Leveraging the inherent connection between sensing systems and wireless communications can improve their overall performance and is the core objective of joint communications and sensing. For effective communications, one has to frequently estimate the channel. Sensing, on the other hand, infers properties of the environment mostly based on estimated physical channel parameters, such as directions of arrival or delays. This work presents a low-complexity generative modeling approach that simultaneously estimates the wireless channel and its physical parameters without additional computational overhead. To this end, we leverage a recently proposed physics-informed generative model for wireless channels based on sparse Bayesian generative modeling and exploit the feature of conditionally Gaussian generative models to approximate the conditional mean estimator.