期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 68, 期 6, 页码 6151-6155出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2907608
关键词
Orthogonal frequency-division multiple access (OFDMA); carrier frequency offset; sparse Bayesian learning
资金
- Natural Science Foundation of China [U1713217, U1501253, 61601304, 61801297, 61801302]
- Foundation of Shenzhen [JCYJ20170302142545828]
- Foundation of Shenzhen University [2016057]
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.
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