Joint CFO and Channel Estimation in OFDM Systems Using Sparse Bayesian Learning

In this article, we study the problem of joint carrier frequency offset (CFO) and sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) communication systems, from the perspective of sparse Bayesian learning (SBL) framework. We first consider the problem in the compressed sensing (CS) context and reformulate it as the problem of recovering a sparse vector from the received signal when CFO and noise variance are unknown parameters that should also be estimated. A novel SBL-based scheme has then been designed to iteratively estimate the CFO, channel impulse response (CIR), and variance of the noise jointly, using the expectation-maximization (EM) algorithm. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing methods while requiring a lower computational cost.

Automated summary

In this article, the problem of joint carrier frequency offset (CFO) and sparse channel estimation in OFDM communication systems is studied using the sparse Bayesian learning (SBL) framework. A novel SBL-based scheme is designed to iteratively estimate the CFO, channel impulse response (CIR), and noise variance jointly, outperforming existing methods with lower computational cost as confirmed by theoretical analysis and simulation results.