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  • Sparse Bayesian Learning Assisted CFO Estimation Using Nonnegative Laplace Priors

    • 摘要:

      This correspondence paper aims at addressing the estimation of carrier frequency offset (CFO) for the uplink orthogonal frequency-division multiple access systems. Since the CFOs of the signals from different active users are sparsely distributed in the frequency domain, a sparse Bayesian learning (SBL) is tailored to determine the CFO in this paper, ending up with the SBL assisted CFO (SBL-CFO) estimator. In particular, the CFO estimation problem is first formulated as a sparse nonnegative least squares (S-NNLS) problem. Meanwhile, background noise and sampling errors are mitigated utilizing a selection matrix and a whitening filter, respectively. This enables us to exploit the SBL with nonnegative Laplace prior (SBL-NLP) to solve the S-NNLS problem. Furthermore, in order to make the convergence of the SBL-NLP algorithm faster and its estimation more accurate, the hyperprior inherent in the SBL-NLP algorithm is initialized by the traditional SBL with nonnegative Gaussian prior. Simulation results show that our proposed SBL-CFO estimator significantly outperforms the state-of-The-Art estimators in terms of estimation accuracy, especially when the CFOs and the number of active users are large.

    • 作者:

      Min Huang;Lei Huang;Weize Sun;为民 包;Jihong Zhang

    • 刊名:

      IEEE Transactions on Vehicular Technology

    • 在线出版时间:

      2019-6