Gibbs Sampling Based Sparse Bayesian Learning for Direction-of-Arrival Estimation with Impulse Noise Towards 6G
Mingfeng Cheng, Wei Peng · 2023
Integrated communication and localization presents a highly promising prospect in the sixth-generation (6G) mobile communication systems. As its core element, direction-of-arrival (DOA) estimation is confronted with a challenging environment characterized by impulse noise. Conventional DOA estimation techniques fail to fully account for the negative impact of this detrimental noise, leading to a significant decline in estimation accuracy. A promising approach to solving this problem is sparse Bayesian learning (SBL). However, the application of SBL with large-scale antenna arrays is hindered by the high computational complexity. To address this challenge, an optimized Gibbs sampling based sparse Bayesian learning (GSSBL) algorithm is proposed by introducing an inverse-free high-dimensional sampler, by which the computational complexity with large-scale antennas can be alleviated. Simulation results demonstrate that the proposed GSSBL approach can achieve accurate DOA estimation with lower complexity comparing with other state-of-the-art methods.