Distributed Bayesian Compressive Sensing Based Blind Carrier-Frequency Offset Estimation for Interleaved OFDMA Uplink

Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme.