DM-SBL: Channel Estimation under Structured Interference

TL;DR

DM-SBL introduces a neural network-based diffusion model approach for joint channel estimation and interference mitigation in complex communication scenarios with structured interference, outperforming traditional methods especially at low SIR.

Contribution

The paper proposes DM-SBL, a novel method combining diffusion models and sparse Bayesian learning for accurate channel estimation under structured interference.

Findings

Significantly outperforms conventional methods at low SIR.

Effectively estimates sparse channels with structured interference.

Demonstrates potential for other linear inverse problems.

Abstract

Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.