Anti-Jamming Underwater Transmission With Mobility and Learning
In this letter, we present an anti-jamming underwater transmission framework that applies reinforcement learning to control the transmit power and uses the transducer mobility to address jamming in underwater acoustic networks. The deep Q-networks-based transmission scheme can achieve the optimal po...
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| Veröffentlicht in: | IEEE communications letters 2018-03, Vol.22 (3), p.542-545 |
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| container_title | IEEE communications letters |
| container_volume | 22 |
| creator | Xiao, Liang Donghua Jiang Wan, Xiaoyue Su, Wei Tang, Yuliang |
| description | In this letter, we present an anti-jamming underwater transmission framework that applies reinforcement learning to control the transmit power and uses the transducer mobility to address jamming in underwater acoustic networks. The deep Q-networks-based transmission scheme can achieve the optimal power and node mobility control without knowing the jamming model and the underwater channel model in the dynamic game. Experiments performed with transducers in a non-anechoic pool show that our proposed scheme can reduce the bit error rate of the underwater transmission against reactive jamming compared with the Q-learning based scheme. |
| doi_str_mv | 10.1109/LCOMM.2018.2792015 |
| format | Article |
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Experiments performed with transducers in a non-anechoic pool show that our proposed scheme can reduce the bit error rate of the underwater transmission against reactive jamming compared with the Q-learning based scheme.</description><subject>Acoustics</subject><subject>deep Q-networks</subject><subject>Interference</subject><subject>Jamming</subject><subject>Learning (artificial intelligence)</subject><subject>Power control</subject><subject>reinforcement learning</subject><subject>Signal to noise ratio</subject><subject>Transducers</subject><subject>underwater transmission</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwA7DJDzj4Ecf2ClURTyXqphVLy7EdMGocZEdC_XtcWrGZO5tzNXMAuMWoxBjJ-7ZZd11JEBYl4TInOwMLzJiAJI_zvCMhIedSXIKrlL4QQoIwvAAPqzB7-KbH0YePYhusiz96drHYRB3S6FPyUyje_fxZdFPvd37eFzrYonU6hoxcg4tB75K7OeUSbJ8eN80LbNfPr82qhYbUfIbGEoYs5aKWQhjJGKryQcb0QmJCbU8J6hmv-l6z2mGK68FioZnmjg_UIkGXgBx7TZxSim5Q39GPOu4VRuqgQP0pUAcF6qQgQ3dHyDvn_oH8eEVrQn8BfXRXlw</recordid><startdate>201803</startdate><enddate>201803</enddate><creator>Xiao, Liang</creator><creator>Donghua</creator><creator>Jiang</creator><creator>Wan, Xiaoyue</creator><creator>Su, Wei</creator><creator>Tang, Yuliang</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-4808-7443</orcidid><orcidid>https://orcid.org/0000-0003-1430-2555</orcidid><orcidid>https://orcid.org/0000-0003-2402-611X</orcidid></search><sort><creationdate>201803</creationdate><title>Anti-Jamming Underwater Transmission With Mobility and Learning</title><author>Xiao, Liang ; Donghua ; Jiang ; Wan, Xiaoyue ; Su, Wei ; Tang, Yuliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c267t-cd250d3786988c95504089ccb89123db320b574bba56e1316fd18a5a7e7f3d083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acoustics</topic><topic>deep Q-networks</topic><topic>Interference</topic><topic>Jamming</topic><topic>Learning (artificial intelligence)</topic><topic>Power control</topic><topic>reinforcement learning</topic><topic>Signal to noise ratio</topic><topic>Transducers</topic><topic>underwater transmission</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xiao, Liang</creatorcontrib><creatorcontrib>Donghua</creatorcontrib><creatorcontrib>Jiang</creatorcontrib><creatorcontrib>Wan, Xiaoyue</creatorcontrib><creatorcontrib>Su, Wei</creatorcontrib><creatorcontrib>Tang, Yuliang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiao, Liang</au><au>Donghua</au><au>Jiang</au><au>Wan, Xiaoyue</au><au>Su, Wei</au><au>Tang, Yuliang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Anti-Jamming Underwater Transmission With Mobility and Learning</atitle><jtitle>IEEE communications letters</jtitle><stitle>COML</stitle><date>2018-03</date><risdate>2018</risdate><volume>22</volume><issue>3</issue><spage>542</spage><epage>545</epage><pages>542-545</pages><issn>1089-7798</issn><eissn>1558-2558</eissn><coden>ICLEF6</coden><abstract>In this letter, we present an anti-jamming underwater transmission framework that applies reinforcement learning to control the transmit power and uses the transducer mobility to address jamming in underwater acoustic networks. 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| ispartof | IEEE communications letters, 2018-03, Vol.22 (3), p.542-545 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Acoustics deep Q-networks Interference Jamming Learning (artificial intelligence) Power control reinforcement learning Signal to noise ratio Transducers underwater transmission |
| title | Anti-Jamming Underwater Transmission With Mobility and Learning |
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