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Massive Machine-type Communications in 5G: Physical and MAC-layer solutions

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Machine-type communications (MTC) are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into "massive Machine-Type Communication" (mMTC) and "ultra-reliable Machine-Type Communication" (uMTC). While mMTC is about wireless connectivity to tens of billions of machine-type terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy efficient. In this article, we introduce the physical (PHY) and medium access control (MAC) layer solutions developed within METIS to address this challenge.
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1
Massive Machine‐type Communications
in5G:PhysicalandMAC‐layersolutions
Carsten Bockelmann*, Nuno Pratas+, Hosein Nikopour, Kelvin Au, Tommy Svensson, Cedomir
Stefanovic+,PetarPopovski+,ArminDekorsy*
* {bockelmann,dekorsy}@ant.uni‐bremen.de,UniversityofBremen,Departmentof
CommunicationsEngineering,Bremen,Germany
+ {nup,cs,petarp}@es.aau.dk,DepartmentofElectronicSystems,AalborgUniversity,Aalborg,
Denmark
 kelvin.au@huawei.com,HuaweiTechnologiesCanadaCo.,LTD.Ottawa,Ontario,Canada
 hosein.nikopour@intel.com,IntelLabs,WirelessCommunicationsResearch,SantaClara,CA,USA
 tommy.svensson@chalmers.se,ChalmersUniversityofTechnology,Gothenburg,Sweden
1 Abstract
Machine‐typecommunications(MTC)areexpectedtoplayanessentialrolewithinfuture5Gsystems.
IntheFP7projectMETIS,MTChasbeenfurtherclassifiedinto“massive Machine‐Type
Communication”(mMTC)and“ultra‐reliableMachine‐TypeCommunication”(uMTC).WhilemMTCis
aboutwireless connectivitytotensofbillionsofmachine‐typeterminals,uMTCisaboutavailability,
lowlatency,andhighreliability.ThemainchallengeinmMTCisscalableandefficientconnectivityfor
a massive number of devices sending very short packets, which is not done adequately in cellular
systems designed for human‐type communications. Furthermore, mMTC solutions need to enable
wideareacoverageanddeepindoorpenetrationwhilehavinglowcostandbeingenergyefficient.In
this article, we introduce the physical (PHY) and medium accesscontrol(MAC)layersolutions
developedwithinMETIStoaddressthischallenge.
2 Introduction
Inthepastyearsthedevelopmentofa5Gvisionledtotheconsensus that the latest generationof
cellularcommunicationsystemswillbedrivenbyanumberofnewlyemergingusecases[1],whereas
thepreviousgenerationsofcellular systemshavebeenmainlydesigned towards increased spectral
efficienciestoenablebandwidth‐hungryapplicationsforhumanusers.Indeed,theFP7ProjectMETIS
developeda5Gsystemconceptinwhichthiswell‐acceptedchaseforhigherdataratesissummarized
byaservicetermed“ExtremeMobileBroadband”(xMBB)[2].However,inadditiontoxMBB,newuse
cases and applications also necessitate fundamentally new services. The introduction of the new
paradigmofmachine‐typecommunications(MTC),inadditiontohuman‐typetraffic,posessignificant
challengestowardsaunifiedradiosolution.
Thedefinitionofmachine‐typecommunicationsissomewhatelusive,asithastoincludealargevariety
ofemergingconcepts,suchastheInternetofThings(IoT),InternetofEverything(IoE),Industry4.0,
SmartX,etc.Eachaddsnewscenarioswithdifferingassumptionsandrequirements,rangingfromlong‐
2
termenvironmentalobservationinvolvinglimitedenergyconsumption,oversmartcitieswithmillions
ofsensors,tofullywirelessfactorieswithverystrictrequirementsonlatenciesandreliabilitiesofthe
wirelessconnection.A5Gdesignneedstoconsiderallthisinordertoreallyfulfilltheroleofauniversal
enablerforemergingandfutureindustries.
TheMETISprojectadoptedtheterm“Machine‐TypeCommunication”(MTC)from3GPPtosummarize
all applications shaping the requirements on potential 5G solutions. Furthermore, MTC has been
differentiated according to the two major challenges: (i) “massive Machine‐Type Communication”
(mMTC) and (ii) “ultra‐reliable Machine‐Type Communication” (uMTC)  [2]. As the name suggests ,
mMTCisaboutmassiveaccessbyalargenumberofdevices,i.e.,aboutprovidingwirelessconnectivity
totensofbillionsofoftenlow‐complexitylow‐powermachine‐typedevices.ContrarytoxMBB,where
peakratesareprioritized,heretheaccentisonscalableconnectivity for an increasing number of
devices,wideareacoverageanddeepindoorpenetration.AtypicalexampleofmMTCisthecollection
ofthemeasurementsfromamassivenumberofsensors,suchassmartmetering.Ontheotherhand,
uMTC is about providing adequate wireless links for network services with rather stringent
requirementsonavailability,latencyandreliability.FortheconceptofuMTC,twoimportantexamples
are Vehicle‐to‐X (V2X) communications and industrial control applications. Accordingly, the METIS
MTCvisiondefinedin[2]revolvesaroundtechnologiesthatareabletosolvethesemainchallenges
andjointly addressfurther Key PerformanceIndicators (KPI),such asenergy efficiencyand cost to
achieveaflexibleandwidelyapplicablecellularsystem.
A wide range of technologies on different layers of the ISO/OSImodelareneededtoprovide
satisfactorysolutionsforfutureMTCapplications.Inthisarticle,weintroducethephysical(PHY)and
mediumaccesscontrol(MAC)layertechnologysolutionsdevelopedinMETIS.WhilemMTCinvolves
some challenges in downlink communication, e.g., efficient communication with actuator devices
underdutycyclesandsleepingpatterns,theuplinkrepresentsaprimarychallengeduetothemassive
numberofuncoordinatedconnectionsinmMTCandisthefocusofthepresentarticle.Weexplainthe
needfornewMACandPHYtechnologiesformassiveandenergyefficientaccessandpresentthePHY
andMACsolutionsdevelopedinMETIS.
3 RequirementsandDesignChallenges
ThedesignonthePHYandMAClayerisfundamentallyaquestionoftrade‐offs.Simplyspeaking,we
trade‐offperformance (e.g., throughput, errorrates, latency, etc.)vs.complexityandtherequired
overhead(e.g.,feedbackorcontrolsignaling).Intraditionalcellularsystemdesignupto4G,thefocus
hasbeenonthesupportofhighdataratedownlinkforhuman‐typecommunication,usinglargepacket
sizes.Thedemandtosupporteverincreasingdatarates,whilefacinglimitedspectrumresources,has
ledtothedevelopmentofverysophisticatedPHYandMAClayertechnologiesthatcanbetterexploit
thewirelesschannel(e.g.,linkadaptationwithchannelqualityfeedback,channelawarescheduling,
adaptivebeam‐forming,etc.)andcopewithtransmissionerrors(e.g.hybridARQ,strongturbocodes,
etc.).The large packetsand high data rates in downlink play a crucialrole: the controlsignaling is
feasibleonly whenitisnegligiblecomparedtothepayloadsize;andtheamountoffeedbackinthe
uplinkisfeasibleonlyifitbringssufficiently highspectralefficiencygainsforthedownlink,without
deterioratingtheuplinktrafficrequirements.
3
InLTE,startingfromthePHYoveralltheupperlayers,largeoverheadisrequiredtofacilitateaccess,
reliable transmission, authentication, security, etc. Furthermore, at the MAC layer, access and
schedulingprotocolsdemandreliablecontrolinformation,whichisoftenprotectedfromerrorsusing
resource inefficient approaches, such as low modulation schemes and high redundancy coding.
ConsidertheextremeexampleofanMTCdevicethatwantstocommunicate a single byte of
information. Already on the PHY layer, channel estimation requires pilots and link adaptation
proceduresrequirefeedbackinformationconsiderablyexceedingthepayloadsize.Therefore,afuture
5GairinterfaceformMTCshouldtargetveryleancontrolsignaling approaches and PHY/MAC
technologieswithminimaloverhead.Toenablealeansignaling/overheadapproachwithconstantor
evenreducedMTCtransmittercomplexity(similartodevicecomplexityreductioneffortspursuedin
3GPPforLTE‐MandNarrowBandIoT[3])anincreaseofthereceivercomplexityatthenetworksideis
expected,asshownlater.
Changinganyofthestandardassumptions–fromlargetosmallpackets,fromhightolowdatarates
orfromadownlinkfocusedtoanuplinkfocusedcommunication–resultsinafundamentalchangeof
thedesignproblem.Hence,mMTCfocusingontheuplinkcommunicationofamassivenumberoflow‐
rate devices, requires a completely different set of technologies than the ones designed to serve
human‐centriccommunications,suchastheonepresentinLTEandpreviouscellulargenerations.To
narrowthe design space of potential mMTC technologieswe haveto lookat typicalrequirements.
StandardliteratureassumptionsformMTCare[4]:
smallpacketspotentiallygoingdowntoafewbytes;
largenumberofusers,e.g.upto300.000devicesinasinglecell;
uplink‐dominatedtransmissions;
lowuserdatarates,e.g.around10kb/speruser;
sporadicuseractivity,e.g.,mixedtrafficmodelswithperiodandeventdriventraffic;
lowcomplexityandbatteryconstrained(lowenergy)MTCdevices.
Giventheserequirementswecannowanalyzepotentialdesignchoicesandoutlineguidelinesforan
mMTC5Gairinterface.Wefocusonthefollowingoverarchingquestions associatedtoprovisionof
accessforamassivenumberofdevicesintheuplink:
Shouldwechooseorthogonalornon‐orthogonalmediumaccess?
Isgrant‐freeorgrant‐basedaccesscontrolbettersuitedformMTC?
HowtoenableenergyefficientMTCdevicesthroughPHYandMACtechnologies?
First, orthogonal medium access tightly couples the number of available reso urces to the number
supportable users, whereas non‐orthogonal medium access enablesacertaindegreeofresource
overloadingatthecostofalgorithmiccomplexityatthereceiver.Thelatterseemsfavorabletosupport
alargenumberofuplinkusersinaresource‐efficientmannerduetorelaxedcomplexityconstraintsat
thebasestationcomparedtotheMTCdevice.
Second, grant‐based access control requires a good prediction of the uplink requests, as well as
additionalcontrolsignalingormessageexchangestofacilitatethegrantingofresources.InMTC,traffic
patternsarepartlyunpredictableandsporadicdue to uncoordinatedsleepingcycles,makinggrant‐
basedscheduledaccessdesigndifficultandpotentiallyinefficient.Ontheotherhand,grant‐freeaccess
requiresonlyverylowcontroloverhead,butoftensuffersfromcollisionsandlowefficiency.However,
4
withanappropriatecollision resolutionmechanism,grant‐free accesscanbe made highlyefficient,
butagain at the cost ofincreased basestation complexity.Consequently, grant‐free accesscontrol
seemsfavorableformMTC.
Finally,theenergyefficiencyofMTCdevicesisstronglyaffectedbytheamountofoverheadseenin
the exchange of messages required before the data payload is transmitted successfully. Simply
speaking,lessfrequentandshortertransmissionspreserveenergy.Therefore,lowsignalingoverhead
MACprotocolsareoneenablerforenergyefficientMTCdevices.Thesetypeofprotocols,coupledwith
efficientPHYapproaches,canthenenabledeviceswithlongbatterylives.
Notethatthesmallpackettransmissions,associatedwithMTC,leadtootherchallenges,inaddition
tothediscussedoverhead:(i)demandforhigherresourcegranularityand(ii)channelcodingforshort
block lengths. Even with a non‐orthogonal grant‐free access protocol in place, the frame‐based
organizationofresources,asemployedinLTE,maybetoolimitedintermsofresourcegranularityto
adapttoveryshortpackets.Theredesignofthecurrentframestructure,withthepurposetoincrease
theresourcegranularityandassignmentflexibilitywithverylowoverhead, is essential to support a
massivenumber ofdevices andenable lowlatencycommunications.Finally,thechannelcodesthat
arecurrentlyusedincellularnetworks,aredesignedtoapproachthechannelcapacityforlongpackets.
Unfortunately, these coding techniques are not suitable for short packets, due to significantly
degradedperformance.Thus,novelchannelcodesdesignedforshortpacketlengthsareanessential
requirementforMTC;however,thedetailedtreatmentofthisissuehereisoutofscope.
Inthefollowing,wefirstdiscussphysicallayerapproachesthat(i)enablemassiveaccess,whichare
SparseCodeMultipleAccess(SCMA)andCompressedSensingbasedMulti‐UserDetection(CS‐MUD)
and(ii)fosterenergyefficiencyandlowcost,whichisContinuousPhaseModulation(CPM).Thenwe
introducetwonovelMACapproachesformMTC,whicharebasedonSCMAandCS‐MUD,respectively.
4 PhysicalLayerSolutions
4.1 CompressedSensingbasedMultiUserDetection(CSMUD)
Figure 1 – (a) MTCuplink communication of N users to a base station (BS). A slotted ALOHA‐based access protocol is
assumed. Activity of multiple users in one slot leads to interferenceintermsofthephysicallayer.FromtheMAC
perspectiveusuallyanunresolvablecollisionisassumedinsuchacase.(b)QualitativeerrorrateperformanceofCS‐MUD
comparedtotraditionaldetectionwithknownactivity.
5
Asdiscussedin Section 3,random access protocolsarestrongly affectedby collisions, i.e.,multiple
users access a resource concurrently and thus cannot be successfully detected and decoded. The
outcome of collisions, though, is strongly dependent on the specific physical layer technologies:
advancedreceiversusinginterferencecancellationcanresolveacertainamountofcollisions,whereas
a combination with non‐orthogonal medium access methods, like CDMAor SCMA (cf. section4.2)
boosts exploitation of collisions further. Consequently, Compressed Sensing based Multi‐User
Detection(CS‐MUD)targetsenhancedresourceefficiencyandnumberofservedusersthroughnon‐
orthogonalrandomaccessincombinationwithajointdetectionofuseractivityanddata.
Figure1aillustratesanuplinkscenariowithNusersandasinglebasestation.Weassumeaslotted
ALOHA‐basedaccessschemesuchthatusersarerandomlyactiveintheresultingcontentionperiod.
Duetotherandomactivityineachslot,onlyasubsetofusersisactivelysendingdata.FromaPHY
perspective,thisleadstoestimationproblemswithsparsity,asthesubsetofactiveusersisunknown
and has to be estimated jointly with the user data. Compressive sensing (CS) focuses on similar
estimationproblemswithsparsityconstraints,whicharewidelyappliedinimagingandradarandhave
beenonly recently considered in thecontext of communications. Most importantly, CSprovides a
sound theoretical basis for novel advanced multi‐user detection algorithms exploiting sparsity.
Nonetheless,knownCSalgorithmsrequirerefinementtoincorporatePHYlayerassumptions,suchas
finitemodulationalphabets,channelcodinganderrorcontrolofactivitydetection.Especiallythelatter
isofhighimportance for activityestimation that isreliablein termsofmissed detectionsandfalse
alarmsinordertosuittheneedsofMACprocessing[5].Solutionstotheresultingrequirementshave
beenintensivelystudiedwithinMETIS,leadingtoanovelclass of detection algorithms named
“CompressiveSensingbasedMulti‐UserDetection”(CS‐MUD)[6,7].Thesealgorithmsenableresource
efficient,highlyreliablerandomaccessincombinationwithnon‐orthogonalmediumaccessschemes.
Figure1bdepictsaqualitativeresultofCS‐MUDcomparedtoanestimationproblemwithknownuser
activity,which reverts tothe standarddata detectiontask. Thevariable gap betweenCS‐MUD and
known activity results shows the flexibility of the scheme. Depending on algorithmic complexity,
differentlevelsofsideinformation such asframestructure,finitemodulationalphabets or channel
codingcanbe exploitedtovastlyimprove theperformance of CS‐MUD.Clearly,thepriceforalow
signalingsolutionwithnearlythesameperformanceasbeforeisalgorithmiccomplexityatthebase
station.Ontheonehand,carefuldesigncandecreasethiscomplexity(cf.SCMA);ontheotherhand,
exploitingmore sideinformation usuallyleads to more complex algorithms. The best trade‐off will
dependontheactualsystemandthespecificsoftheMAClayerprocessing.
To summarize, CS‐MUD provides advanced collision resolution to serve a large number of users
through random access with low overhead, but with performance comparable to grant‐based
schemes.CS‐MUDrequiresahigher algorithmic complexityat thebasestation,comparedtosimple
receiveroftenemployedinscheduledsystems,andisbasedontheassumptionofsporadictraffic
patternsandnon‐orthogonalmediumaccess.
6
4.2 SparseCodeMultipleAccess(SCMA)
(a) (b)
Figure2–(a)6SCMAmultiplexedlayerscarriedover4OFDMAtoneswith150%overloading.Codewordsof6layersare
selectedfrom6layer‐specificcodebooks.Duetothesparsity,only3outof6layerscollideovereachOFDMAtone,(b)an
SCMAencoderwithamulti‐dimensionalcodebookincomparisonwithaQAMmapperwithaQAMconstellation.
Sparsecodemultipleaccess(SCMA)[8]isanon‐orthogonalmultiple‐accessschemeincodedomain
thatcan supportmassiveconnectivity.AsshowninFigure2a,anSCMAencoderdirectlymapsincoming
bitsof a data stream to SCMA codewords selected froma layer‐specific codebook.Comparing the
SCMAencodertoaQAMmodulatorasinFigure2b,anSCMAencoderhasthesamefunctionality,but
withmulti‐dimensionalconstellationpointsratherthantraditionalQAMconstellations.Codewordsof
multiple SCMA layers are combined either at atransmit point in downlink or over the air and are
carried over orthogonal channel resources such as Orthogonal Frequency Division Multiple Access
(OFDMA)tones.
ThemainadvantageofSCMAisachievedbypropercodebookdesign.ComparingSCMAtotraditional
multi‐carrierCDMA(MC‐CDMA),wecanseetwodifferences:(i)standardMC‐CDMAisequivalenttoa
repetitioncodingofQAMsymbols;and(ii)QAMconstellationsexhibitagaptotheShannoncapacity.
InSCMA,these twopoints are addressedthrough multi‐dimensional codeword design[9].A multi‐
dimensionallatticeprovidesfurtherdegreesoffreedomtobetterutilizethespaceovermultipletones
andimprovethe distance spectrumofSCMA constellations.Thus,we achieve acodingas wellasa
shapinggainoverCDMArepetitionandOFDMAQAM constellations,respectively.Itisimportantto
notethat Non‐OrthogonalMultiple Access (NOMA),although havingsimilar featuresasSCMA, itis
unabletoreachthesamelevelofuseroverloadanditisnotsuitableformassiveuplinkMTCscenarios.
Furthermore,thecomplexmulti‐dimensionalcodewordsofSCMAallowanon‐orthogonaldesignand
henceoverloading.Overloadingincreasesthenumberofavailablelinks,whichisbeneficialformassive
connectivity, but optimal multi‐layer detection is challenging for practical systems. However, as
opposedtonon‐orthogonalCDMA, near‐optimal ML‐likedetection can bemadepractically feasible
throughproperdesignoftheSCMAcodewordsanditerativemessagepassingalgorithm(MPA)[8].
Therefore,SCMAcodewordsaredesignedtobesparse[9].Sparsityofcodewordsalongwiththelow
projection property helps the MPA receiver to exponentially reduce the detection complexity.
Referringto Figure2a,thesystemis150%overloadedwith6non‐orthogonallayers,butdueto the
sparsityofthecodewordsonly3ofthemcollideovereachOFDMAtone.Asanexample,eachlayer
carriescodewordsofalayerspecific16pointcodebook.Ifthe codebooks are not sparse (which is
equivalenttoCDMAwith16‐QAMconstellation),thetotalnumberofpossiblecombinationsovereach
toneis16
,butthankstothesparsitythiscanbereducedto16
asonly3outof6codewordscollide
overeachtone.Asreportedin[9]theeffectivesizeofthecodebooksovereachnon‐zerotonecanbe
QAM Ma pper
Codedbits QAMsymbol
QAMconstellatio n 0001
11 10
SCMA Encoder
Codedbits
SCMA
codeword
SCMAcodebook
000
001
011
010
110
111
101
100
7
reducedfrom16to9.ItconsequentlyreducesthecomplexityofMPAdetectionto9
.Therefore,by
combiningsparsityandlowprojectiontechniquestheoriginalcomplexityof detectionisextensively
reducedbyafactorof

~23014forthisparticularscenario[9].
In summary, the sparsity of codebooks, low projection techniques and blind detection with SCMA
receiver make SCMA an attractive solution for uplink contention‐based grant‐free transmission in
mMTCwithamoderatelyhighercomplexitythanaconventionallinearreceiver(Section5.1).
4.3 ContinuousPhaseModulation(CPM)
Many mMTC devices will be limited by their battery due to cost/space constraints, and should
preferablyuselowcostamplifiers;therefore,energyefficiencyofmMTCterminalsandgoodcoverage
aremore importantthanspectralefficiency.Constantenvelopesignalsofferthepossibilitytousea
non‐linearcost‐effectiveandpower‐efficientHigh‐PowerAmplifier(HPA)atthetransmitter,because
the HPA can be operated close to saturation without adding distortion. For this reason, constant
envelopecoded‐modulationsystemshavetraditionallybeenextensivelyusedin,e.g.,satellitelinks,
earlywirelessstandards(GSM),Bluetooth,andlowratelongdistancemicrowaveradiolinksforcellular
backhauling.
ToillustratethepotentialHPAefficiencygains,Figure3(left)showshowpeak‐to‐averagepowerratio
(PAPR)andtheRawCubicMetric(RCM)(similarto3GPPCubicMetric)relatetotheoverallefficiency
ofaclassELDMOSHighPowerAmplifier(HPA)forasetofsignalshavingdifferentenvelopeproperties
[10].Ascanbeseen,theoverallefficiencyoftheHPAforaCPM‐likesignalissubstantiallybetterthan
forSingle‐CarrierFrequencyDivisionMultipleAccess(SC‐FDMA)signalsusedintheuplinkof3GPPLTE,
e.g.,66%overallefficiencyforCPMcomparedto40‐45%forSCFDMA with a 0.1% clipping signal
distortion constraint of the input samples (solid red curve). ThereareevenlargergainsofCPM
comparedtoTDMA‐OFDM(66%vs35%).

However,thereisatrade‐off.Thelowertheenvelopevariations,themorecompactthesignalspace,
leadingtolesssensitive receiverfor a givenreceiver SNR.Thus,constrained envelopeCPM(ceCPM)
[11]hasbeenproposedanddevelopedbasedonalineardescriptionofCPM.Thisapproachallowsthe
receiversensitivitytobemonotonicallyincreasedwiththeallowedenvelopevariationenergy,using
Figure3–Left: MaximumoverallHPA efficiencyversus meanPAPR and RCMof variousmodulatedsignalswith1% and
0.1%clippinglevel[10].Right:BlockdiagramandpowerspectraldensityofanillustrativeCPM‐SC‐FDMAscheme[12].
8
thesamereceiverarchitectureasinCPM.Forexample,itispossibletofindaceCPMschemewithan
SNRgainof2.5dBinanAWGNchanneloverCPMatsymbolerrorprobability10−3,underthesame
requirementonspectralconfinementandspectralefficiencyasforthebestCPMscheme,withaPAPR
penaltyofabout1.5dB.TheceCPMschemeisdoubleasspectralefficientasGaussianMinimumShift
Keying(GMSK)andcanbedetectedwithalowcomplexityViterbidetector(using8‐16states).
Analternativeapproachforfrequencyselectivewidebandmultipathfadingchannelsisillustratedin
Figure3(right).Thisschemeusessub‐sampledCPMasprecoderofSC‐FDMAwithInterleavedFDMA
(I‐FDMA)subcarriermapping,treatingthesamplesasgeneralizeddatasymbolsinSC‐FDMA.Thus,a
regularOFDMAtransceiveriscomplementedwithaprecoderonthetransmitterside,andaViterbi
decoderaftersoftsymbolestimationonthereceiverside.Inthisway,aconstrainedenvelopesignal
can be conveyed using a spread spectrum approach over a frequency selective channel. Schemes
obtainedin[12]demonstratethatPAPRcanbeaslowasafractionofadB.Theusersarefrequency
multiplexed in the I‐FDMA fashion, thus harvesting frequency diversity towards fading and
narrowband interference. In [12] it is shown that CPM‐SC‐FDMA can outperform convolutional
encodedQPSKbasedSC‐FDMAbyupto4dBinend‐to‐endpowerefficiency,takingHPApowerbackoff
intoaccount(basebandpowerconsumptionnotevaluated).
Thus,thesepromisingconstrainedenvelopecodedmodulationschemesexhibitusefulpropertiesfor
mMTC in both traditional narrowband single‐carrier and wideband multi‐carrier channelization
scenarios,atthecostofmorecomplexbasebandforwaveformprocessing.
5 AccessLayerSolutions
5.1 UplinkSCMAContentionBasedGrantfreeTransmission
Figure4–UplinkSCMArandomaccess
AsdiscussedinSection3,randomaccessisanattractivestrategyto(i)removeorreducethedynamic
resourceallocationoverhead,and(ii)achievelatencyreduction.Acombinationofrandomaccesswith
SCMA (cf. Section 4.2) is appealing in terms of overloading and scalability. The principle idea is
illustratedinFigure4.Acontentiontransmissionunit(CTU) is thebasicradioresourcedefinedasa
combinationofacontentionregion(timeandfrequencyresource),anSCMAcodebookandapilot
sequence. When a UE has data to transmit, it occupies the entire contention region to transmit
codewordsofoneSCMAlayer.TwodifferentCTUsmaysharethesamecodebookbuttheirpilot
sequencesareunique.Codebookreuseis allowed amongUEsduetotheirstatistically independent
t
SCMAlayer
f
Pilot1
PilotK
Pilot2
...
...
MultipleUEsaremappedtoacontentiontransmissionunit.Thisis
definedasacombinationofacontentionregion,anSCMAcodebook
andapilotsequence.Thesizeofacontentionregiondependsonthe
supportedpacketsizeandcodewordlength.
Contentionregion
9
randomchannels on the uplink. Therefore,two users are collidingonly if they have identical pilot
sequences.ApilotcollisionpreventsthereceivertoestimatechannelsofcollidingUEsforcoherent
MPAdetection.Toalleviatethis,SCMAprovideslargenumberoflinkswithhighreliability,firstdueto
overloadingwithsparsecodebookdesignasdiscussedinSection4.2,andsecondlyduetocodebook
reusetogeneratealargepoolofCTUsleadingtolowchanceofcollision.Ifthenetworkstillcontains
toomanyusers,theyneedtocontendforCTUsandcollisioncanberesolvedwithconventionalcollision
resolutionprocedures.
Toadjusttodynamic changes, thenumberofcontention regions and,hence,CTUscanbeadapted
basedonthenumberofdevicesandcollisionstatistics.Furthermore,SCMAoffersscalabilityinterms
ofthenumberofcodewordsofanSCMAcodebook,numberofcodebooks,codewordlength,andthe
sparsitypatternofcodebooks.Thesecanbeconfiguredsemistaticallybasedonthenumberof
supportedusers, trafficvolume ofusers, complexity ofdetection, coverage,reliability oflinks, and
systemoutage.Forexample,longercodewordsoflength8withlargernumberofnon‐zeroelements
canprovidebettercodinggainandhencebettercoverage,whereassparsercodewords(e.g.oflength
4with1or2non‐zeroelements)cantoleratefurtheroverloadingtoenablemassiveconnectivitywith
feasiblecomplexityofdetection[13].
AcrucialcomponentofuplinkSCMAgrant‐freetransmissionisblinddetection.AregularMPAreceiver,
as discussed in Section 4.2, requires prior knowledge regarding the number of layers and their
correspondingcodebooks. However, inmMTC scenarios withrandomaccess,itisuptotheSCMA
receiverto recognize active layers andtheir data.This requires amodification ofthe original MPA
receivertoprovidejointactivityanddatadetectionjustlikeCS‐MUDdoes(cf.Section4.1).Byadding
anall‐zerocodewordtomodelaninactivelayer,amodifiedMPAreceivercandetectinactiveandactive
layers.InthiscasetheactivityanddataofalayerarejointlydetectedbyMPA[14].
Tosummarize,systemlevelsimulationsshowedthatupto3times more devices compared to
contention‐basedOFDMAsystemcanbesupportedfordelaysensitivesmallpackets at thecost of
higher base station processing [13]. Moreover, SCMA can benefitfromadvancesinCSMUD(see
Section4.1)tofurtherenhancethesystem’sabilitytotoleratemorecollisions.
10
5.2 CodedRandomAccessandCSMUD
Aspreviouslydiscussed,theperformanceofrandomaccessprotocols,suchasslottedALOHA(SA),is
limitedby the occurrenceof collisions.In Coded RandomAccess (CRA)thetheory andthe tools of
erasure‐correctingcodesareappliedtoenhanceSA,drawingfromtheanalogiesbetweenSuccessive
Interference Cancellation (SIC) and iterative belief‐propagation erasure‐decoding. We consider a
variantofCRA,denotedasFramelessALOHA[15],inwhich:(1)theuserscontendonaslotbasis,using
predefinedslotaccessprobabilities,andwhere(2)thelengthofthecontentionperiod(innumberof
slots)isnotapriorifixed,butdeterminedon‐the‐flysotomaximizethethroughput.Thissolutionis
focusedontheefficient support ofmMTC,beingoptimizedforefficientresource use andtoahigh
volumeofservedusers.
Figure5 –CodedRandom Accessoverview:a) frameless structuredelimitedbybeacons; b) iterativebeliefpropagation
withexecutionofSIConagraphattheinter‐slotlevel;andc)intra‐slotandinter‐slotprocessingdoneatthebasestation.
OneimportantCRAenablerisaphysicallayercapableofperformingMulti‐UserDetection(MUD)and
SIC,suchasCS‐MUDpresentedinSection4.1.ThecombinationofCRAandCS‐MUDusesauniquedata
processing flow to successfully decode users over all received slots. The scheme starts with the
transmissionofabeacon,asdepictedinFigure5a,whichinitiatesaMACcontentionperiod.TheUEs
sendreplicasofthesamepacketinrandomlyselectedslots,asshowninFigure5b,usingtheslotaccess
probabilityprovidedbythebeacon.ThecontentionperiodendsoncetheBSsendsanewbeacon.In
eachslot,asdepictedinFigure5c,thedataprocessingtakesplaceasfollows:First,thePHYprocessing
is facilitated by CS‐MUD and standard FEC decoding to recover the UE packet, where a Cyclic
Redundancy Check (CRC) check ensures the correct packet reception. If a UE’s packet is correctly
decoded,itwillimmediatelybesubtractedfromthereceivedsignalatthecurrentslotand the PHY
processingmayberepeateduntilnonewUEsaresuccessfullyrecovered.AllsuccessfullydecodedUE
packetsarethenstoredforinter‐slotinterferencecancellation,i.e.,cancellingofreplicas,whichtakes
placeonceprocessinginthenextslotstartsandisperformedbasedonbelief‐propagationonagraph,
asshowninFigure5b.InthiswayPHYandMACprocessingworkhandinhandtorecoverasmanyUE
packets as possible, maximizing the throughput. This joint approachshowspromisingresultsin
comparison to the LTE Rel. 11 baseline. Up to 10x the number of machine type devices can be
supportedforshortpackets,asshownin[7].
Tosummarize,thecombinationofCS‐MUDandCRAprovidesanefficientPHYandMACsolutionto
solvesporadicmMTCallowingforasynchronous,sporadicmMTCuplinkcommunications,wherethe
11
activeusers,delaysandchannelsaregenerallyunknown.However,toenabletheprocessingdepicted
inFigure5c,thealgorithmiccomplexityandtheneedfordatabuffering at the base station is
significantlyincreased.
6 Conclusions
Thedesignof5G faces manychallengestoprovidetheservicesofthefuture.Wehavepresenteda
selection of candidate technologies developed in the FP7 project METIS for massive machine‐type
communication (mMTC). The main motivation of all presented solutions is the need for fresh
approachestosolvethemassiveaccessprobleminenergyefficientandlowcostways.Ofcourse,these
are just a first step towards a complete solution and require further support in many ways. For
example,theoverallairinterfacedesignshouldallowfordynamicadaptationtodifferentmMTCcell
loads,whilebeingwellintegratedwithotherserviceslikexMBB.Currently,flexiblewaveformdesign
enablingin‐bandmMTCchannelsseemstobeapromisingcandidateinthatrespect.Also,higherlayer
considerationsplayaveryimportantroletoensureleansignaling,e.g.,byintroductionof
connectionless, one‐shot transmission modes to enable longer sleep cycles, or traffic prediction
approachestoefficientlysupportquasi‐periodictraffic.
7 Acknowledgement
PartofthisworkhasbeenperformedintheframeworkoftheFP7projectICT‐317669METIS,which
ispartlyfundedbytheEuropeanUnion.Theauthorswouldliketoacknowledgethecontributionsof
theircolleaguesinMETIS,althoughtheviewsexpressedarethoseoftheauthorsanddonotnecessarily
representtheproject.
TheworkofČ.StefanovićwassupportedbytheDanishCouncilforIndependentResearch,grantno.
DFF‐4005‐00281.
8 References
[1]NGMN,“5GWhitePaper,”2015.
[2] METISD6.6,"FinalreportontheMETISsystemconceptandtechnologyroadmap,"2015.
[3] 3GPP,TR45.820‐Cellularsystemsupportforultra‐lowcomplexityandlowthroughputInternet
ofThings(CIoT),2015.
[4] F. Boccardi, R. Heath, A. Lozano, T. Marzetta and P. Popovski, "Five disruptive technology
directionsfor5G,"IEEECommunicationsMagazine,pp.74,80,22014.
[5] F. Monsees, C. Bockelmann and A. Dekorsy, "Compressed Sensing Neyman‐Pearson Based
ActivityDetectionforSparseMultiuserCommunications,"in10thInternationalITGConference
onSystems,CommunicationsandCoding(SCC2015),Hamburg,Germany,February2015.
[6] C.Bockelmann,H.SchepkerandA.Dekorsy,"CompressiveSensingbasedMulti‐UserDetection
for Machine‐to‐Machine Communication," Transactions on Emerging Telecommunications
12
Technologies:SpecialIssueonMachine‐to‐Machine:Anemergingcommunicationparadigm,vol.
24,no.4,pp.389‐400,June2013.
[7] METISD2.4,"Proposedsolutionsfornewradioaccess,"2015.
[8] H.NikopourandH.Baligh,"Sparsecodemultipleaccess,"inIEEE24thPIMRC,2013.
[9] M.Taherzadeh,H.Nikopour,A.BayestehandH.Baligh,"SCMAcodebookdesign,"inIEEEVTC
f
all,Vancouver,2014.
[10]T.SvenssonandT.Eriksson,"OnPowerAmplifierEfficiencywithModulatedSignals,"in71stIEEE
VehicularTechnologyConference(VTC2010‐Spring),May2010.
[11]T.SvenssonandA.Svensson,"Constrainedenvelopecontinuousphasemodulation,"inThe57th
IEEEVehicularTechnologyConference(VTC2003‐Spring2003),April2003.
[12]M.Wylie‐Green,E.PerrinsandT.Svensson,"IntroductiontoCPM‐SC‐FDMA‐ANovelMultiple‐
AccessPower‐EfficientTransmissionScheme,"IEEETransactionsonCommunications,vol.7,pp.
1904‐1915,2011.
[13]K.Au,L.Zhang,H.Nikopour,E.Yi,A.Bayesteh,U.Vilaipornsawai,J.MaandP.Zhu,"Uplink
contentionbasedSCMAfor5Gradioaccess,"inGlobecomWorkshops(GCWkshps),2014.
[14]A. Bayesteh, E. Yi, H. Nikopour and H. Baligh, "Blind detection of SCMA for uplink grant‐free
multiple‐access,"inISWCS,Barcelona,Spain,2014.
[15]C.StefanovicandP.Popovski,"ALOHARandomAccessthatOperatesasaRatelessCode,"IEEE
TransactionsonCommunications,November2013.
9 Biographies
Dr.‐Ing.CarstenBockelmannnreceivedhisDipl.‐Ing.(M.Sc.)degreein2006andhisPhDdegree2012
bothinelectricalengineeringandfromtheUniversityofBremen,Germany.Since2012heisworking
asa post doctoral researcher at the University of Bremen coordinatingresearch activities regarding
theapplication ofcompressive sensing/sampling to communication problems. His current research
interests include compressive sensing and its application in communications contexts, as well as
channelcodingandtransceiverdesign.
NunoK.PratasisanAssistantProfessoronWirelessCommunicationsattheDepartmentofElectronic
Systems,AalborgUniversity.Hehasbeenawardedtwicethebeststudentconferencepaperawardand
hasbeenrecognizedasanexemplaryreviewerforIEEETransactiononCommunications.Hiscurrent
researchinterestsareonwirelesscommunications,networksanddevelopmentofanalysistoolsfor
Machine‐to‐MachineandDevice‐to‐Deviceapplications.
AftertwoyearsofMIMOresearchatUniversityofWaterloo,Ontario,Canadaasapostdoctoralfellow,
Dr.Hosein Nikopourjoined Nortel Networks,Ontario, Canada in 2006. Hewas involved in WiMAX
physicallayerdesignaswellasIEEE16mandLTEstandardization.In2009,hejoinedHuaweiCanada
13
wherehedeliveredwirelesssolutionsforLTEand5Gcellularstandards.Dr.NikopourjoinedIntelLabs,
SantaClara,CAinJuly2015asaresearchscientist.
KelvinAuiscurrentlywithHuaweiTechnologiesCanadaCo.Ltd.workingonairinterfaceresearchand
standardization. Hereceived B.A.Sc.(1998) in Engineering Scienceand M.A.Sc. (2000)in Electrical
EngineeringfromtheUniversityofToronto,Canada.HejoinedNortelNetworksin2000focusingon
PHY/MACandradioresourcemanagementdesignforMIMO‐OFDMsystems.From2008to2011,he
wasresponsiblefordevelopingradiosoftwareinBlackBerryandwasinvolvedinseveralnewproduct
launches
TOMMYSVENSSON(S'98‐‐M'03‐‐SM'10)isAssociateProfessorinCommunicationSystemsatChalmers
UniversityofTechnologyinGothenburg,Sweden,whereheisleadingtheresearchonairinterfaceand
wireless backhaul networking technologies for future wireless systems. He received a Ph.D. in
InformationtheoryfromChalmersin2003,andhehasworkedatEricssonABwithaccess,microwave
radioandcorenetworks.Hehasco‐authoredthreebooksandmorethan120journalandconference
papers.
ČedomirStefanovićreceivedtheDipl.‐Ing.,Mr.‐Ing.,andPh.D.degreesinelectricalengineeringfrom
theUniversityofNoviSad,Serbia.Heiscurrentlyanassociate professor at the Department of
ElectronicSystems,AalborgUniversity,Denmark.In2014hewasawardedaindividualpostdocgrant
bytheDanishCouncilforIndependentResearch(DetFrieForskningsråd).Hisresearchinterestsinclude
codingtheory,communicationtheory,andwirelesscommunications.
PetarPopovskiisaProfessoratAalborgUniversity,Denmark.HereceivedDipl.‐Ing.(1997)/Magister
Ing. (2000) in communication engineering from Sts. Cyril and Methodius University, Skopje,
Macedonia,andPh.D.fromAalborgUniversity(2004).HeisaFellowofIEEE.Hecurrentlyservesasan
AreaEditorinIEEETransactionsonWirelessCommunicationsandaSteeringCommitteememberfor
IEEEInternetofThingsJournal.Hisresearchinterestsareinwirelesscommunications/networksand
communicationtheory.
Prof.ArminDekorsyistheheadoftheDepartmentofCommunications Engineering, University of
Bremen.HereceivedDipl.‐Ing.(FH)(1992)fromFachhochschuleKonstanz,Germany,Dipl.‐Ing.(1996)
fromUniversityofPaderborn,GermanyandPh.D.(2000)fromUniversityofBremen,Germany,allin
communicationengineering.Hespentmorethan10yearsinindustryatDeutscheTelekomAG,Bell
LabsEurope(LucentTechnologies)andQualcomminleadingresearchpositions.Heservesasmember
forETSI,IEEE,VDE/ITG,andrepresentstheUniversityofBremenatNetWorld2020ETP.
... 2) Existing Challenges/Limitations: The existing communication technologies [43] focus only on some specific applications. They are not able to meet the latency and reliability requirements of applications such as connected cars, automated vehicles and industrial automation. ...
... Based on the priorities given, efficiency of data and computing services are also optimized. Bockelmann et al. [43] have designed a FP7 METIS project using 5G system concept in which well-suitable chase for high data rates are summarized by the term "Extreme Mobile Broadband" (xMBB). The overhead seen in exchange of messages required before the transmission of data payload affects the energy efficiency of MTC devices. ...
... 1) Resource scheduling problem is solved using puncturing approach 2) To maintain the latency constraint, URLLC are considered inside the minislot of each eMBB time slot 3) To ensure the reliability, guard zones are deployed around the vehicle. 4) 5G can make instantaneous communication with URLLC 5) The time delay using 5G is 1 to 5 millisecond when compared to 20 milliseconds in 4G 6) Reduced time delay in AV provides users with safety information 7) One slice in network slicing can provide high reliability and security for URLLC 1) Resource scheduling should be considered for both uplink and downlink scenario for URLLC 2) Guard zone based URLLC scheduling policy is compared with the baseline policy where the guard zone receiver is not applied mMTC/ V2V communication/ V2X communication [43], [43]- [51] 1) A common framework for Machine type communication must be available 2) Current link adaption mechanisms are not suitable for MTC 3) Resource allocation and channel coding schemes are unsuitable for small packets 1) optimization algorithms are used for trajectory planning which in turn minimizes the power consumption of MTC 2) Extreme mobile broadband is used for high data rates 3) less frequent and shorter transmissions preserves energy. So MAC protocols with physical layer approaches ensure device with long battery life 4) NOMA architecture is used to reduce the latency and reliability requirements in V2X 1) As the number of devices increases rapidly, lack of coordination between base station and the machine causes inter-carrier interference. ...
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With the advent of 5G technology and rise of autonomous vehicles (AVs), road safety is going to get more secure with less human errors. However, integration of 5G in AVs is still at its infant stage with several research challenges that needs to be addressed. Therefore, this survey discusses the current advancements of integrating 5G technology in AVs, impact of 5G and B5G technologies on AVs along with security concerns in AVs. We also provide recent developments in terms of standardisation activities on 5G autonomous vehicle technology. The article is finally concluded with future research directions and challenges.
... To meet the dramatically increasing demand for wireless connectivity of Internet-of-Things (IoT), machine-type communications (MTC) have been recognized as a new paradigm in the fifth-generation and beyond wireless systems. Different from the traditional human-to-human communications, MTC scenarios commonly involve a large number of IoT devices connecting to the network, but only a small portion of the devices are active at any given time due to the sporadic traffics [1]- [3]. ...
... Based on these settings, the SNR at the BS varies from 2.19 dB to 14.19 dB as p max increases from 11 dBm to 23 dBm. The received signal is generated according to (1), where the activity of each device is generated from Bernoulli distribution and used as the groundtruth label for the training samples. The hyper-parameters of the proposed heterogeneous transformer are summarized in Table I. ...
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Full-text available
To support modern machine-type communications, a crucial task during the random access phase is device activity detection, which is to identify the active devices from a large number of potential devices based on the received signal at the access point. By utilizing the statistical properties of the channel, state-of-the-art covariance based methods have been demonstrated to achieve better activity detection performance than compressed sensing based methods. However, covariance based methods require to solve a high dimensional nonconvex optimization problem by updating the estimate of the activity status of each device sequentially. Since the number of updates is proportional to the device number, the computational complexity and delay make the iterative updates difficult for real-time implementation especially when the device number scales up. Inspired by the success of deep learning for real-time inference, this paper proposes a learning based method with a customized heterogeneous transformer architecture for device activity detection. By adopting an attention mechanism in the architecture design, the proposed method is able to extract features reflecting relevance among device pilots and received signal, permutation equivariant with respect to devices, and its training parameter number is independent of the device number. Simulation results demonstrate that the proposed method achieves better activity detection performance with much shorter computation time than state-of-the-art covariance approach, and generalizes well to different numbers of devices and BS-antennas, different pilot lengths, transmit powers, and cell radii.
... IoT and mMTC systems are characterized by sporadic UE activity, uplink-dominated transmissions, and small packets [3], [5], [9]. The design of access techniques to support the huge numbers of such devices is still an open challenge [10]. ...
Article
Full-text available
Massive multiple-input multiple-output (mMIMO) technology is a way to increase spectral efficiency and provide access to the Internet of things (IoT) and machine-type communication (MTC) devices. To exploit the benefits of large antenna arrays, accurate channel estimation through pilot signals is needed. Massive IoT and MTC systems cannot avoid pilot reuse because of the enormous numbers of connected devices. We propose a pilot reuse algorithm based on channel charting (CC) to mitigate pilot contamination in a multi-sector single-cell mMIMO system having spatially correlated channels. We show that after creating an interference map via CC, a simple strategy to allocate the pilot sequences can be implemented. The simulation results show that the CC-based pilot reuse strategy improves channel estimation accuracy, which subsequently improves the symbol detection performance and increases the spectral efficiency compared to other existing schemes. Moreover, the performance of the CC pilot assignment method approaches that of exhaustive search pilot assignment for small network setups.
... The authors [6] in their investigation define the metrics of quality of service for the aspects such as energy consumption and consumption of time for M2M service. The investigators in [7] discuss on physical and MAC layer technology solutions for energy efficiency of MTC devices. ...
Preprint
Full-text available
5G wireless network carry different type of traffic generated by various applications running on the hosts. Some of the traffic carried by 5G broadband network is human-type communication (HTC) and machine-type communication (MTC) along with conventional data traffic due to HTTP, FTP, and video streaming applications. MTC wireless communication is made up of sensors, actuators, and other devices called massive machine-type communication (mMTC), not directly operated by humans. These devices are connecting the base station at any given time that leads to randomness in the traffic flows. Thus, the data traffic generated by MTC devices can be periodic or event-triggered [1]. In this research paper, tried to extend the work proposed in [1] by considering two types of traffic, (i) periodic traffic generated by MTC and (ii) network responsive traffic generated by transmission control protocol (TCP). Traffic management at the base station is made by a RED router. A model based traffic performance analysis has been conducted to study the dynamics of sender window, rtt delay, queue dynamics, probability of packet losses and the effect of MTC load on TCP at the ingress of the router. This helps in understanding the performance of 5G wireless network, tuning the router parameters and in providing guaranteed quality of service (QoS) by optimising the network resources. Graphical and statistical analysis has been presented using Matlab programming.
... In an ad hoc network, a single channel needs to be shared by several nodes, and the medium access control (MAC) protocol [8][9][10] is responsible for the coordination between nodes. With advances in technological development, the physical (PHY) layer is vastly improved with higher data rates and a better quality of service (QoS) [11][12][13][14]. Although IEEE 802.11 allows the use of multiple channels, IEEE 802.11 ...
Article
Full-text available
Medium access control (MAC) protocols in ad hoc networks have evolved from single-channel independent transmission mechanisms to multi-channel concurrent mechanisms to efficiently manage the demands placed on modern networks. The primary aim of this study is to compare the performance of popular multi-channel MAC (MMAC) protocols under saturated network traffic conditions and propose improvements to the protocols under these conditions. A novel, dynamically adaptive MMAC protocol was devised to take advantage of the performance capabilities of the evaluated protocols in changing wireless ad hoc network conditions. A simulation of the familiar MAC protocols was developed based on a validated simulation of the IEEE 802.11 standard. Further, the behaviors and performances of these protocols are compared against the proposed MMAC protocols with a varying number of ad hoc stations and concurrent wireless channels in terms of throughput, Jain’s fairness index, and channel access delay. The results show that the proposed MMAC protocol, labeled E-SA-MMAC, outperforms the existing protocols in throughput by up to 11.9% under a constrained number of channels and in channel access delays by up to 18.3%. It can be asserted from these observations that the proposed approach provides performance benefits against its peers under saturated traffic conditions and other factors, such as the number of available wireless channels, and is suitable for dynamic ad hoc network deployments.
... Introduction: Spectrum scarcity means that next-generation (xG) multiple access technologies should accommodate many users on a single channel to empower massive connectivity as required by 5G and beyond mobile communications [1,2]. One emerging technology that could fit the bill is fluid antenna [3]. ...
Article
Full-text available
Fluid antenna multiple access (FAMA) is a new way of accommodating a large number of users on a single channel for massive connectivity, with slow FAMA (s-FAMA) being the practical version for achieving this. The impressive performance is understood to be achievable if the users have independent Rayleigh fading envelopes. With mobile networks vamping up the operating frequencies in 5G and beyond, nevertheless, the channel will have less multipath and become more directional. It is unclear if s-FAMA still performs well and how its performance is affected by different channel parameters. To address this, this letter first develops a multipath fading channel model capable of modelling a mixture of directional line-of-sight (LoS) and non-LoS paths over the ports of a fluid antenna system. The results indicate that a large number of paths in the channel is essential to the performance and a large Rice factor will degrade the performance rapidly. Also, contrast to the initial belief, the size of the fluid antenna plays a more important role than the number of ports or resolution of the fluid antenna. To restore the gain of s-FAMA, it is proposed to employ extra-large multiple-input multiple-output (XL-MIMO) at the base station (BS) to scramble the channel and create artificial multipath so that the users' envelopes can become independent Rayleigh again. The results confirm that XL-MIMO enabling s-FAMA is an effective technique for massive connectivity in the directional millimeter-wave (mmWave) bands.
... Massive machine-type communication (mMTC) is an important application scenario in the fifth-generation (5G) and beyond cellular systems [1]. A main challenge in mMTC is massive random access, in which a large number of devices are connected to the network, but the device activities are sporadic [2]. ...
Preprint
Full-text available
This paper studies the covariance based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, where the active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. The scaling law of covariance based activity detection in the single-cell scenario has been thoroughly analyzed in the literature. This paper aims to analyze the scaling law of covariance based activity detection in the multi-cell massive MIMO system. In particular, this paper shows a quadratic scaling law in the multi-cell system under the assumption that the exponent in the classical path-loss model is greater than 2, which demonstrates that in the multi-cell MIMO system the maximum number of active devices that can be correctly detected in each cell increases quadratically as the length of the signature sequence and decreases logarithmically as the number of cells (as the number of antennas tends to infinity). Moreover, this paper also characterizes the distribution of the estimation error in the multi-cell scenario.
Article
This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Article
Full-text available
New research directions will lead to fundamental changes in the design of future fifth generation (5G) cellular networks. This article describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter wave, massive MIMO, smarter devices, and native support for machine-to-machine communications. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.
Article
Full-text available
With the expected growth of machine-to-machine communication, new requirements for future communication systems have to be considered. More specifically, the sporadic nature of machine-to-machine communication, low data rates, small packets and a large number of nodes necessitate low overhead communication schemes that do not require extended control signaling for resource allocation and management. Assuming a star topology with a central aggregation node that processes all sensor information, one possibility to reduce control signaling is the estimation of sensor node activity.In this paper, we discuss the application of greedy algorithms from the field of compressive sensing in a channel coded code division multiple access context to facilitate a joint detection of sensor node activity and transmitted data. To this end, a short introduction to compressive sensing theory and algorithms will be given. The main focus, however, will be on implications of this new approach. Especially, we consider the activity detection, which strongly determines the performance of the overall system. We show that the performance on a system level is dominated by the missed detection rate in comparison with the false alarm rate. Furthermore, we will discuss the incorporation of activity-aware channel coding into this setup to extend the physical layer detection capabilities to code-aided joint detection of data and activity. Copyright © 2013 John Wiley & Sons, Ltd.
Article
Full-text available
Various applications of wireless Machine-to-Machine (M2M) communications have rekindled the research interest in random access protocols, suitable to support a large number of connected devices. Slotted ALOHA and its derivatives represent a simple solution for distributed random access in wireless networks. Recently, a framed version of slotted ALOHA gained renewed interest due to the incorporation of successive interference cancellation (SIC) in the scheme, which resulted in substantially higher throughputs. Based on similar principles and inspired by the rateless coding paradigm, a frameless approach for distributed random access in slotted ALOHA framework is described in this paper. The proposed approach shares an operational analogy with rateless coding, expressed both through the user access strategy and the adaptive length of the contention period, with the objective to end the contention when the instantaneous throughput is maximized. The paper presents the related analysis, providing heuristic criteria for terminating the contention period and showing that very high throughputs can be achieved, even for a low number for contending users. The demonstrated results potentially have more direct practical implications compared to the approaches for coded random access that lead to high throughputs only asymptotically.
Conference Paper
Sparse code multiple access (SCMA) is a new frequency domain non-orthogonal multiple-access technique which can enable massive connectivity and grant-free transmission in wireless radio access. With SCMA, different incoming data streams are directly mapped to codewords of different multi-dimensional cookbooks, where each codeword represents a spread transmission layer. Multiple SCMA layers share the same time-frequency resources of OFDMA. The sparsity of codewords allows low complexity of multi-layer detection for excessive codeword overloading which is the key feature to enable massive connectivity. In this paper, a blind detection solution is introduced and analyzed to support massive connectivity in a SCMA-based UL grant-free multiple access. The proposed solution is based on two major components: i) blind detection of active pilots/users with reasonable complexity, and ii) blind decoding of active users' data with no knowledge of active codebook set. Different activity detection algorithms and schemes are proposed, described, and analyzed. Simulation results are provided to evaluate the performance of the proposed schemes in various scenarios. Our analysis and performance evaluation confirm the proposed SCMA-based blind reception solution is a promising technology to enable massive connectivity for grant-free multiple-access transmission mode in future wireless networks.
Article
Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.
Article
Fifth generation (5G) wireless networks are expected to support very diverse applications and terminals. Massive connectivity with a large number of devices is an important requirement for 5G networks. Current LTE system is not able to efficiently support massive connectivity, especially on the uplink (UL). Among the issues arise due to massive connectivity is the cost of signaling overhead and latency. In this paper, an uplink contention-based sparse code multiple access (SCMA) design is proposed as a solution. First, the system design aspects of the proposed multiple-access scheme are described. The SCMA parameters can be adjusted to provide different levels of overloading, thus suitable to meet the diverse traffic connectivity requirements. In addition, the system-level evaluations of a small packet application scenario are provided for contention-based UL SCMA. SCMA is compared to OFDMA in terms of connectivity and drop rate under a tight latency requirement. The simulation results demonstrate that contention-based SCMA can provide around 2.8 times gain over contention-based OFDMA in terms of supported active users. The uplink contention-based SCMA scheme can be a promising technology for 5G wireless networks for data transmission with low signaling overhead, low delay, and support of massive connectivity.
Conference Paper
Multicarrier CDMA is a multiplexing approach in which modulated QAM symbols are spread over multiple OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequence allowing us to take advantage of a near optimal ML receiver with practically feasible complexity. In this paper, we propose a new multiple access scheme so called sparse code multiple access (SCMA) which still enjoys the low complexity reception technique but with better performance compared to LDS. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to a multidimensional codeword of an SCMA codebook set. Each layer or user has its dedicated codebook. Shaping gain of a multidimensional constellation is the main source of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. In general, SCMA codebook design is an optimization problem. A systematic sub-optimal approach is proposed here for SCMA codebook design.
Article
This paper presents a novel multiple-access modulation scheme which combines key characteristics of single carrier frequency division multiple access (SC-FDMA) with continuous phase modulation (CPM) in order to generate a power efficient waveform. CPM-SC-FDMA is developed based upon the observation that the samples from a CPM waveform may be treated as "data symbols" taken from a constant-envelope encoder. As with any encoder output, these samples may be precoded using the Discrete Fourier Transform and transmitted using SC-FDMA. Having originated from a constant envelope CPM waveform, CPM-SC-FDMA can potentially retain much of the power efficiency of CPM-thus resulting in a lower peak-to-average power ratio (PAPR) than conventional SC-FDMA. In this paper, we account for the information rate, memory, power efficiency, bit error rate (BER) performance and spectral occupancy of CPM-SC-FDMA. In addition, we investigate the impact of amplifier nonlinearity on BER performance as the number of users increases. Finally, we provide a detailed numerical comparison with a commensurate convolutionally coded QPSK-SC-FDMA scheme (CC-QPSK-SC-FDMA). We show a CPM-SC-FDMA scheme that provides an overall gain of up to 4 dB relative to the CC-QPSK-SC-FDMA scheme over a frequency-selective channel.
Conference Paper
We have investigated the performance in terms of overall efficiency and maximum output power of a real high power amplifier with various modulated signals, ranging from constant envelope signals to orthogonal frequency division multiplexing based signals having large envelope variations. We show that there are large differences on the overall amplifier efficiency as well as its maximum output power depending on the modulated signal and also on the operation mode. In particular, we show that for high overall efficiency it is important to operate the amplifier close to the maximum output power level as often as possible, and we show that this has consequences for the resource allocation in especially packet based multicarrier systems. We also show that the amplifier performance with various Single-Carrier Frequency Division Multiple Access signals is very similar, and that the peak-to-average-power ratio can be used to predict the overall efficiency for the traditional amplifier drive method. Finally, we show that with advanced, today infeasible, amplifier operation methods the overall amplifier efficiency depends less on the modulation scheme, thus motivating further research in that domain.
Article
This paper presents a novel multiple-access modulation scheme which combines key characteristics of single carrier frequency division multiple access (SC-FDMA) with continuous phase modulation (CPM) in order to generate a power efficient waveform. CPM-SC-FDMA is developed based upon the observation that the samples from a CPM waveform may be treated as "data symbols" taken from a constant-envelope encoder. As with any encoder output, these samples may be precoded using the Discrete Fourier Transform and transmitted using SC-FDMA. Having originated from a constant envelope CPM waveform, CPM-SC-FDMA can potentially retain much of the power efficiency of CPM-thus resulting in a lower peak-to-average power ratio (PAPR) than conventional SC-FDMA. In this paper, we account for the information rate, memory, power efficiency, bit error rate (BER) performance and spectral occupancy of CPM-SC-FDMA. In addition, we investigate the impact of amplifier nonlinearity on BER performance as the number of users increases. Finally, we provide a detailed numerical comparison with a commensurate convolutionally coded QPSK-SC-FDMA scheme (CC-QPSK-SC-FDMA). We show a CPM-SC-FDMA scheme that provides an overall gain of up to 4 dB relative to the CC-QPSK-SC-FDMA scheme over a frequency-selective channel.