Multi-Layer Distributed Bayesian Compressive Sensing Based Blind Carrier-Frequency Offset Estimation in Uplink OFDMA Systems

In orthogonal frequency-division multiplexing access (OFDMA) system, a distributed Bayesian compressive sensing (DBCS) based blind carrier frequency offset (CFO) estimator has been proposed, which offers a significant improvement on the performance of multiple-parameter estimation, compared with the existing subspace theory based method. However, the analysis for theoretical performance and computational complexity is absent. In this paper, we conduct a further study on this method, and derive the Cramer-Rao Bound to evaluate the performance. Then we highlight our work on the complexity reduction, for which a multi-layer DBCS based algorithm is presented. Additionally, we analyze the computational complexity of this new method, and derive an effective solution for determining the optimal layer number. Simulation results demonstrate our analysis, and show that our proposed algorithm can reduce the complexity by approximate two orders of magnitude with only a slight loss in performance at low signal-to-noise ratio (SNR).