Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions
We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and...
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| Veröffentlicht in: | IEEE communications letters 2011-05, Vol.15 (5), p.476-478 |
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| container_end_page | 478 |
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| container_issue | 5 |
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| container_title | IEEE communications letters |
| container_volume | 15 |
| creator | Urriza, P Rebeiz, E Pawelczak, P Cabric, D |
| description | We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances. |
| doi_str_mv | 10.1109/LCOMM.2011.032811.110316 |
| format | Article |
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The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.</description><identifier>ISSN: 1089-7798</identifier><identifier>EISSN: 1558-2558</identifier><identifier>DOI: 10.1109/LCOMM.2011.032811.110316</identifier><identifier>CODEN: ICLEF6</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Accuracy ; Applied sciences ; Classification ; Complexity theory ; Computational efficiency ; Computer simulation ; distribution distance ; Exact sciences and technology ; Information, signal and communications theory ; Jitter ; Kuiper test ; Mathematical analysis ; Mathematical models ; Measurement ; Modulation ; Modulation classification ; Modulation, demodulation ; Signal and communications theory ; Signal to noise ratio ; Sorting ; State of the art ; Telecommunications and information theory</subject><ispartof>IEEE communications letters, 2011-05, Vol.15 (5), p.476-478</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-e22b45db7c6170e36d9551fbb8d1e18500a452757008db64c2fb76ddcf6c89963</citedby><cites>FETCH-LOGICAL-c368t-e22b45db7c6170e36d9551fbb8d1e18500a452757008db64c2fb76ddcf6c89963</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5741766$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5741766$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24154103$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Urriza, P</creatorcontrib><creatorcontrib>Rebeiz, E</creatorcontrib><creatorcontrib>Pawelczak, P</creatorcontrib><creatorcontrib>Cabric, D</creatorcontrib><title>Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions</title><title>IEEE communications letters</title><addtitle>COML</addtitle><description>We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.</description><subject>Accuracy</subject><subject>Applied sciences</subject><subject>Classification</subject><subject>Complexity theory</subject><subject>Computational efficiency</subject><subject>Computer simulation</subject><subject>distribution distance</subject><subject>Exact sciences and technology</subject><subject>Information, signal and communications theory</subject><subject>Jitter</subject><subject>Kuiper test</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Modulation</subject><subject>Modulation classification</subject><subject>Modulation, demodulation</subject><subject>Signal and communications theory</subject><subject>Signal to noise ratio</subject><subject>Sorting</subject><subject>State of the art</subject><subject>Telecommunications and information theory</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkT1PwzAQhiMEEqXwC1giJMSUYjvx1wihBaRUZYDZsh1HcuUmxU6Q-u9xGtSBxXe-e-6VfW-SpBAsIAT8sSo36_UCAQgXIEcshljOITlLZhBjlqF4nMccMJ5RytllchXCFgDAEIazpC273X7oZW-7Vjp3SJdNY7U1bZ-uu3pwx0ZamR_j0tLJEGxsT8VnGUydxuTDd0oq62x_SF9s6L1Vw5EYL7LVJl0NrR4r4Tq5aKQL5uYvzpOv1fKzfMuqzet7-VRlOieszwxCqsC1oppACkxOao4xbJRiNTSQYQBkgRHFNH6jVqTQqFGU1LVuiGack3yePEy6e999Dyb0YmeDNs7J1nRDEIzxAjIKWCTv_pHbbvBxFxEiJOeAFaMcmyDtuxC8acTe2530BwGBGG0QRxvEaIOYbBCTDXH0_k9fBi1d4-NCbDjNowLiIpKRu504a4w5tTEtII3P-AVLEpKo</recordid><startdate>20110501</startdate><enddate>20110501</enddate><creator>Urriza, P</creator><creator>Rebeiz, E</creator><creator>Pawelczak, P</creator><creator>Cabric, D</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/LCOMM.2011.032811.110316</doi><tpages>3</tpages></addata></record> |
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| ispartof | IEEE communications letters, 2011-05, Vol.15 (5), p.476-478 |
| issn | 1089-7798 1558-2558 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Applied sciences Classification Complexity theory Computational efficiency Computer simulation distribution distance Exact sciences and technology Information, signal and communications theory Jitter Kuiper test Mathematical analysis Mathematical models Measurement Modulation Modulation classification Modulation, demodulation Signal and communications theory Signal to noise ratio Sorting State of the art Telecommunications and information theory |
| title | Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions |
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