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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| creator | Zhou, Mingda Huang, Xinming Feng, Zhe Liu, Youjian |
| description | 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. |
| doi_str_mv | 10.1109/ACCESS.2019.2937102 |
| format | Article |
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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. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-6693c31fc7ae0a526cc4f9f8de637ad0771d029270649e3ad38d8cb4c1f7ddb13</citedby><cites>FETCH-LOGICAL-c408t-6693c31fc7ae0a526cc4f9f8de637ad0771d029270649e3ad38d8cb4c1f7ddb13</cites><orcidid>0000-0003-0584-3448 ; 0000-0002-3410-5471</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8811488$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2100,4022,27632,27922,27923,27924,54932</link.rule.ids></links><search><creatorcontrib>Zhou, Mingda</creatorcontrib><creatorcontrib>Huang, Xinming</creatorcontrib><creatorcontrib>Feng, Zhe</creatorcontrib><creatorcontrib>Liu, Youjian</creatorcontrib><title>Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility</title><title>IEEE access</title><addtitle>Access</addtitle><description>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.</description><subject>Artificial neural networks</subject><subject>Carrier frequencies</subject><subject>Coarse CFO estimation</subject><subject>Compatibility</subject><subject>Estimation</subject><subject>Estimators</subject><subject>higher compatibility</subject><subject>MIMO</subject><subject>MIMO communication</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Oscillators</subject><subject>Receiving antennas</subject><subject>Transmitting antennas</subject><subject>Wireless communication</subject><subject>Wireless communication systems</subject><subject>Wireless communications</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1rGzEQXUoDDWl-QS6Cnu1KK60-ejOL0xiS-uCEHoUsjRy565UryQT_-yrZEDqXGR7z3ny8prkheE4IVt8Xfb_cbOYtJmreKioIbj81ly3hakY7yj__V39prnPe4xqyQp24bMY-mpQB3Sb4e4LRntHa-wwFLXMJB1NCHFEY0cPqYY0251zgkNFTDuMO_YJTMkNN5SWmP_kHWqBNHE5vjN-hPKO7sHuGhPp4OFadbRhCOX9tLrwZMly_56vm6Xb52N_N7tc_V_3ifmYZlmXGuaKWEm-FAWy6llvLvPLSAafCOCwEcbhVrcCcKaDGUemk3TJLvHBuS-hVs5p0XTR7fUz1lHTW0QT9BsS00yaVYAfQTEhHus4wThWTnm25I4wTUBQUCOer1rdJ65hi_VEueh9Paazr65Z1HadECVq76NRlU8w5gf-YSrB-9UlPPulXn_S7T5V1M7ECAHwwpCSESUn_AYHpjoQ</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Zhou, Mingda</creator><creator>Huang, Xinming</creator><creator>Feng, Zhe</creator><creator>Liu, Youjian</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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. 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| subjects | Artificial neural networks Carrier frequencies Coarse CFO estimation Compatibility Estimation Estimators higher compatibility MIMO MIMO communication neural network Neural networks Oscillators Receiving antennas Transmitting antennas Wireless communication Wireless communication systems Wireless communications |
| title | Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility |
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