DM-SBL: Channel Estimation under Structured Interference
This addresses a critical problem for communication systems operating in environments with structured interference, offering a novel solution that improves estimation accuracy, though it appears incremental as it builds on existing techniques like SBL and diffusion models.
The paper tackles channel estimation in communication systems with both AWGN and structured interference, such as in shared bandwidth scenarios, by proposing DM-SBL, which combines diffusion models and sparse Bayesian learning to jointly estimate the sparse channel and interference. Numerical simulations show it significantly outperforms conventional methods, especially under low SIR conditions.
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.