Adversarial Score-Based Generative Models for MMSE-achieving AmBC Channel Estimation

TL;DR

This paper introduces an adversarial score-based generative model that improves joint channel estimation in ambient backscatter networks, achieving near-MMSE performance for direct channels and surpassing it for cascaded channels.

Contribution

It presents a novel deep learning approach using adversarial score-based models within a probabilistic framework for joint AmBC channel estimation.

Findings

Achieves MMSE-like performance for direct channel estimation.

Outperforms standard LS estimation for cascaded channels.

Demonstrates significant improvement over traditional methods.

Abstract

This letter presents a pioneering method that employs deep learning within a probabilistic framework for the joint estimation of both direct and cascaded channels in an ambient backscatter (AmBC) network comprising multiple tags. In essence, we leverage an adversarial score-based generative model for training, enabling the acquisition of channel distributions. Subsequently, our channel estimation process involves sampling from the posterior distribution, facilitated by the annealed Langevin sampling technique. Notably, our method demonstrates substantial advancements over standard least square (LS) estimation techniques, achieving performance akin to that of the minimum mean square error (MMSE) estimator for the direct channel, and outperforming it for the cascaded channels.