Name
Title | ||
|---|---|---|
Coarse Frequency Offset Estimation In Mimo Systems Using Neural Networks: A Solution With Higher Compatibility |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2937102 | IEEE ACCESS |
Keywords | DocType | Volume |
Coarse CFO estimation, MIMO, neural network, higher compatibility | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingda Zhou | 1 | 3 | 0.73 |
| Xin-ming Huang | 2 | 356 | 46.91 |
| Zhe Feng | 3 | 0 | 0.34 |
| Youjian Liu | 4 | 605 | 49.82 |