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IRS-Assisted Ambient Backscatter Communications Utilizing Deep Reinforcement Learning

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dc.contributor.authorJia, Xiaolun
dc.contributor.authorZhou, Xiangyun
dc.date.accessioned2024-01-14T23:03:15Z
dc.date.issued2021
dc.date.updated2022-09-25T08:17:01Z
dc.description.abstractWe consider an ambient backscatter communication (AmBC) system aided by an intelligent reflecting surface (IRS). The optimization of the IRS to assist AmBC is extremely difficult when there is no prior channel knowledge, for which no design solutions are currently available. We utilize a deep reinforcement learning-based framework to jointly optimize the IRS and reader beamforming, with no knowledge of the channels or ambient signal. We show that the proposed framework can facilitate effective AmBC communication with a detection performance comparable to several benchmarks under full channel knowledge.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2162-2337en_AU
dc.identifier.urihttp://hdl.handle.net/1885/311404
dc.language.isoen_AUen_AU
dc.publisherIEEE Communications Societyen_AU
dc.rights© 2021 IEEE.en_AU
dc.sourceIEEE Wireless Communications Lettersen_AU
dc.subjectAmbient backscatter communicationen_AU
dc.subjectintelligent reflecting surfaceen_AU
dc.subjectdeep reinforcement learningen_AU
dc.titleIRS-Assisted Ambient Backscatter Communications Utilizing Deep Reinforcement Learningen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue11en_AU
local.bibliographicCitation.lastpage2378en_AU
local.bibliographicCitation.startpage2374en_AU
local.contributor.affiliationJia, Xiaolun, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationZhou, Xiangyun, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoruidJia, Xiaolun, u5589193en_AU
local.contributor.authoruidZhou, Xiangyun, u2586105en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor400600 - Communications engineeringen_AU
local.identifier.ariespublicationa383154xPUB21644en_AU
local.identifier.citationvolume10en_AU
local.identifier.doi10.1109/LWC.2021.3100901en_AU
local.identifier.scopusID2-s2.0-85110950020
local.identifier.thomsonIDWOS:000716695600012
local.publisher.urlhttps://www.ieee.org/en_AU
local.type.statusPublished Versionen_AU

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