Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility
Carrier frequency offset (CFO), which often occurs due to the mismatch between the local oscillators in transmitter and receiver, limits the performance of multiple-input multiple-output (MIMO) wireless communication systems. To recover the CFO, the first step is coarse CFO estimation. This paper pr...
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Veröffentlicht in: | IEEE access 2019, Vol.7, p.121565-121573 |
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Sprache: | eng |
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Zusammenfassung: | Carrier frequency offset (CFO), which often occurs due to the mismatch between the local oscillators in transmitter and receiver, limits the performance of multiple-input multiple-output (MIMO) wireless communication systems. To recover the CFO, the first step is coarse CFO estimation. This paper presents a neural network (NN) based coarse CFO estimator which has higher compatibility with a variety of MIMO systems, comparing with traditional CFO estimators. Instead of performing closed form calculation as some traditional estimators do, the proposed estimator transforms the estimation problem to a classification problem: classify the optimal coarse CFO estimate from a pool of coarse CFO candidates. Taking the advantage of neural networks, the proposed NN estimator can perform coarse CFO estimations for MIMO systems with different numbers of antennas and a variety of channel models. Meanwhile, the testing results show that the proposed NN estimator has promising performance and wide CFO acquisition range. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2019.2937102 |