DM-SBL: Channel Estimation under Structured Interference
Yifan Wang, Chengjie Yu, Jiang Zhu, Fangyong Wang, Xingbin Tu, Yan Wei, Fengzhong Qu
TL;DR
DM-SBL tackles channel estimation under structured interference by integrating score-based diffusion models with a sparse Bayesian learning prior for the channel. It learns the interference score with a neural network while deriving an analytic prior for the sparse channel and updates prior hyperparameters via EM. The framework yields two practical variants, DM-SBL (DMPS) and DM-SBL (PiGDM), depending on the perturbation-likelihood approximation, and demonstrates significant NMSE gains over conventional methods, especially at low SIR. The approach offers a flexible, high-performance solution for linear inverse problems afflicted by structured interference and can extend to related domains beyond channel estimation.
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.