A Variational Bayesian Approach for Channel Estimation in Pilot-Contaminated User-Centric C-RAN System

In fifth-generation and beyond (5GB) wireless communication systems, Cloud Radio Access Network (C-RAN) is recognized as a vital technology. In this endeavor, User-Centric C-RAN (UC-RAN) promises a significant reduction in the channel training overhead because only the intra-cluster channel state information (CSI) is necessary for successful high data rate transmission. However, the network performance may be degraded by inter-cluster interference. Furthermore, such networks are susceptible to the pilot contamination effect, which leads to a major restriction on overall system performance. To tackle this issue, we introduce a channel estimation (CE) approach based on iterative variational Bayesian inference (IVBI)- called the least square IVBI (L-IVBI) scheme. This method consists of two stages: initialization and iteration, and is designed for the UC-RAN system. The initialization stage includes a coarse channel estimate, further refined in the iteration stage. We follow the alternative minimization method to estimate the desired channel in the iteration stage. Extensive simulation results for the UC-RAN system validate the proposed algorithms. We also provide the derivation of the Bayesian Cramer-Rao bound (BCRB) for the proposed estimator. The novel approach significantly outperforms the state-of-the-art in terms of normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER).