Post on 23-Aug-2020
hume@vt.eduwww.hume.vt.edu
TheFutureofRadioLearningEfficientSignalProcessingSystems
TimO’Shea,oshea@vt.eduFounder/ChiefScientist,DEEPSIGInc
ResearchFaculty,VirginiaTechhttps://www.deepsig.io/
DeepLearningTrends
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• LargeNeuralNetworksareDisruptingSignalProcessing
• Biggerchangethanmostpeoplerealize• FeatureLearning• End-to-endlearning• Widelyapplicabletomanydomains
• Featureengineeringisbecomingirrelevant• Experttransformsunnecessarytoachievestateoftheartperformance
• Engineeredfeaturescreatedbarrierstolearninganyway
• Engineeredalgorithmscreatedworkinoptimizingdisparatealgorithms
• Justembraceeverythingasadensemultiplyaccumulatewithsomearbitrarysetofweights
DeepLearningTrends
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• Thingsthataren’tthatexcitinganymore• ComputerVision(ObjectRecognition)
• Selfdrivingcars(Tesla,Comma.ai,etc)• VoiceRecognition(Siri/GoogleAssistant)
• Itscleardeeplearningcan(has)destroythestateoftheartinthesefields• Customsiliconcandrasticallybringdownpowercostsofthesenetworks• Apple“Bionic”processor,GoogleTPU,etc
• Theseshipsarealreadysailingwholesale
Whatisstillbrewing…
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• Probablyeverysignalprocessingalgorithmonearthcouldbereconsidered/improved• Especiallylearnedrepresentations…
• Simpleconstructoftheautoencoder• Learnsentirelynewrepresentationsofinformation• Basedonreconstructionlossorotherlossfunctions
Minimizeencoderanddecoderloss
OnlyforrelevantdistributionofX!
What'sinteresting… andcomingnext…
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• ImageCompressionSchemes• Contextawarecompression:both0.08bits/pixel• NewJPEGStandards
• VideoCompression• Netflix/MITadoptionnow
• Encryption• Simplyamin/maxreconstructionoptimization
BetterEstimatorsunderimpairedchannels
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• Re-considerestimatorsandrepresentations• Inthecontextofactualdistributioninformation• Betterestimationunderimpairments!• Especiallygoodforshort-timewindows
EfficientApproximateDecoders
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• Learningdecodersfor‘nearoptimal’errorcorrectioncodes!(Partitioningtoscale)• FECdecoding/detectioncurrentlythe#1powerconsumingoperationinradiobasebanddevices
• WorkfromCammerer,Gruber,Hoydis,tenBrink(UniversityofStuttgart/Nokia-BellLabs)
• Showsnear-optimalpolarcodedecodingperformancewithpartitionedneuralnetworksatlowercomplexitythansuccessivecancellationorBeliefPropagation!
• Potentiallyamajoradvanceinerrorcorrection• Learnapproximatedecodersoncode-wordsets• Lowlatencyone-shotdecodingathigherefficiency
• “ScalingDeepLearning-basedDecodingofPolarCodesviaPartitioning”https://arxiv.org/abs/1702.06901
RethinkApproachtoCommunications
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“Reproducingatonepointeitherexactlyorapproximatelyamessageselectedatanotherpoint”
• C.E.Shannon,“Amathematicaltheoryofcommunication,”1948
• Allcommunicationsystemsneedtodoisoptimizeforreconstructionloss• Everythingelseisasecondarysub-task• Letsnotgethunguponminutia
• Thisactuallyworksreallywell• Matchescodedmodulationbaselinesimmediately
RadioMethodsforSaliency
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• Howcanwereducethesearchspace?• Leveragethingsweknowaboutpropagationphysics?
• Introducedomainawareattentionmechanismsintherightway–• Decompositionofreceiver
• Learnedestimationmodules(Attentionmodel)• Experttransformationmodulestomatchphysicalworldpropagationmodels/effects
• Learneddemapping/representationmodules• Jointlearningofencoder/modulator,synchronizer,decoder/demodulator,andovertheairrepresentation• Lowercomplexitylearningproblem
• Convergesfaster,lessoverfitting• Onlyimpartspropagationmediumexpertknowledge
(thingswecan’tchange)• Learneverythingelseend-to-end
Whatifwehavetosharethechannel?
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• Caneasilyextendthismethodtomulti-accesschannel• Learnabettersolutionthanorthogonality• Samebasicprincipals• Comesupwithimmediatelyinterpretableresults
(a)(1,1)(b)(2,2)(bits,symbols)
SynthesizingComplexMulti-userPHYs
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• Comparisonwithexistingmethods• Comparethemulti-useraccesschannel• Orthogonal(Time-slicing(TS))vslearnedmethod• LearnsnewneverbeforeseenPHYscheme• Infinitenumberofpossiblewaveforms!• Inthiscasepseudo-orthogonalsuperpositioncode
(a)(1,1)(b)(2,2)(c)(4,4)(d)(4,8)(bits,symbols)
ComplexPHYLearning
SynthesizingComplexMulti-AntennaPHYs
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• Extendthistechniquetomulti-antenna• Samebasicprincipals• ComplexMIMOchanneleffects
• IncorporateCSIfeedback• EntirelynewMIMOscheme
SynthesizingComplexMulti-AntennaPHYs
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• Canlearnincrediblycomplexjointsolutions• Softjoint-modulation-codingschemes• Outperformcurrentbaselines(zeroforcingMIMO)
• Enormouspotentialfordistributedwireless• MIMOsystemperformance• Secrecyandprivacy
Non-standardMIMOQAMModesComplexMIMOPHYLearning
Learned2x2Constellations
1. Transmitted2. Diag Rx3. UniformRx
Thanks!Questions?
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MLDrivenRFSystemsarecomingFASTComeandworkwithus/talktous!
Nextgenerationradiosensingandcommunicationssystems
AppliedResearchMatureCapabilities
UnrestrictedFundamentalEnablingResearch
info@deepsig.io oshea@vt.edu