This document discusses the advancements and challenges associated with 6G wireless technology, emphasizing the importance of machine learning in optimizing communication networks for applications such as holographic telepresence, e-health, and pervasive connectivity. It outlines the need for improved models that can handle the complexity and demands of future wireless systems, highlighting both the advantages of 6G, such as very high data rates and low latency, and the disadvantages like challenges in terahertz frequency communications. The integration of machine learning into various network layers is portrayed as essential for efficient operation, real-time analytics, and the overall performance of 6G networks.

1. Patent application form
Of
6G White Paper on Machine Learning
Field of technology and background of the invention
Technology such as holographic image telepresence, eHealth and well-being applications, pervasive
connectivity in intelligent environments, enterprise 4.zero and massive robotics, massive unmanned
mobilityinthree dimensions,augmentedtruth(AR),and digital truth(VR),to name a few,will shape
tomorrow's truth.
Each one is expectedtodemandmore effective andefficientwi-fi communicationsthaneverbefore,
and 6G wi-fi networks will need to provide broadband, near-instant,and stable connections to allow
massive dataexchange atvariousfrequenciesandthroughthe usage of a wide range of technologies.
Furthermore,technologicalevolution are movingtowardmore usefultoolsinsidethe internetof things
(IoT), which will necessitate more trustworthy, efficient, resilient, and consistent connectivity.
When the linked items grow more sensible, it will be more difficult to deal with their complexityby
using a static, basic, and inflexible communication community.
The same need may grow for other traditional services such as telecall smartphone calls or video
streaming,inwhichthe wi-fi communicationnetworkwillnotjustprovidealinkamongormore people
however will carry the want to nicely authenticate each parties, assure the safety of statistics fluxes
and recognizingviable bizarre behaviorsandevents.Datatrade may be,in practice,lotsgreater than
just natural statistics trade and turns into the trade of information, knowledge, experience, and
additionally past, present, and probable destiny homes of the statistics. What we will effortlessly
assume isthe realitythatlarge and large quantitiesof statisticsmaybe transferredvia the destinywi-
fi conversation networks and greater brought price programs and offerings will closely rely on such
statisticsexchanges.Machine learning(ML) will constitute asimple capabilitytoassure the efficiency
of destiny wi-fi conversation networks and, on the identical time,can constitute the allowing era for
numerous brought-price programs and offerings. ML at the wi-fi conversation nodes can allow
numerous superior offerings and first-rate of carrier functionalities for the proposed programs
Current wi-fi networks closely rely upon mathematical fashions that define the shape of the verbal
exchange system. Such mathematical fashions frequently do now no longer gift the structures
accurately.Moreover,there aren'tanymathematicalfashionsforanumberof the constructingblocks
of wi-fi networksandgadgetsandasa result,modelingof suchblockswillbecomechallenging.Onthe
opposite hand, the optimization of wi-fi networksadditionally callsfor heavy mathematical answers
which might be frequently now no longer efficientin phrases of computational time and complexity,

2. and, additionallydevournumerousenergy.The above statedmathematical fashionsandanswerswill
maximum probable fall brief in improving the potential and overall performance of wi-fi networks
which might be anticipated to satisfy the stringent necessities in order to be set through 6G
applications. ML, therefore, will play a essential function in 6G wi-fi networks as it's miles able to
modeling structures that can't be provided through a mathematical equation. Moreover, it's miles
anticipated that ML equipment may be used to update heuristic or brute-pressure algorithms to
optimize positive localized tasks. Meanwhile, it's miles anticipated that ML will allow real-time
evaluation and automatic 0 contact operation and manage in 6G networks.
Problem
The first level of development of a wireless modem typically is a software simulation of the physical
layer transmitter and receiver. The air interface is simulated with a channel version that tries to
recreate actual global situationswhichinclude noise,fading,multipath,Dopplerunfoldandpathloss.
Variouscomponentsof the receivermaybe carried out in an ANN that are pointedoutin thispaper.
Atthispoint,MLwill takelocationinANNswhereinthe varietyof nodes,layers,connections,activation
capabilities and again propagation loss capabilities all want to be bendy whilst the network trains.
Duringthispreliminarylevel,the numerousparametersandtraitsof the ANN will want tobe diagnosed
withtrade-offsamongoverall performanceandbodilysources.Eveneleventhoughtrainingof anANN
isnot performedinactual-time,overall performanceconsiderationsare stillimportantsince thereare
realistic boundaries how lengthy simulations can run. Offloading ML algorithms from a Central
Processing Unit (CPU) to a Graphics Processing Unit (GPU) can growth performance by 10 to 100
instances.InadditionspecificANN acceleratorscanimprove overall performance evenmore however
aren't constantly proper to helping again-propagation required for training. In order to educate an
ANN,manyuniquechannelfashionswanttobe hiredandrunina Monte-Carlofashionsimulationwith
more than one trials.Each trial runwitha unique randomseedcanbe prettycomplicatedtogenerate
and take hourstorun forthe reasonthat versionsimulatesimpairmentsatthe image rate.How nicely
the ANN will version actual global situations relies upon upon the quality and range of the channel
fashions. For illustrative purposes, if we've got 30 channel fashions, each is run 20 instances with
randomizedstatisticsandthe simulationtakeseighthourstorun that couldresulting2hundreddays
of run time. This suggests that those simulations could want to run in parallel on ahigh quit grid or
cloud primarily basedtotally engine. Also it's far apparent that we need to lessen simulation time by
offloadingthe ANN tospecializedhardware.One large task duringsimulationisto identifythe shape
and functionsof the neural network.If we needtoexamine overall performance of several activation
capabilitiesorrange the numbersof linkednodesineverylayer,we will see thatthecomputingsources
required withinside the simulation level is vast. A large a part of the layout with any ML set of rules
withinside the bodilylayeristodecide whatthe inputstothe ANN are.Filteredoutputswhichinclude
channel estimator statistics, FFT output, pilot symbols or possible uniquely filtered statistics are all
candidatesas inputsto the ANN.Raw I/Samplescouldprobablycrushany moderatelysizedANN and
motive convergence to take way too lengthy if at all possible. Hooks into the processing stack are
required to bring out any raw data this is required as enter to the ANN. Also outputs which include
BLER, BER, SINR and Revalidation will want to be fed again into the loss function

3. Invention
Althoughperformance,cost,andsize are alwaysconsideredwhenimplementingneural networks,they
are extremelyimportantwhenimplementingmachine learningalgorithmsatthe userequipment(UE)
or cell edge. When modeling and prototyping ML on UE, other considerations should also be
considered. Optimize the physical implementation of the project. Early in the design phase,
deploymentcan be software-centric.The onlyway to get the expectedbatterylife throughreal-time
calculations is to switch to a hardware-centric solution. And the expected requirements for artificial
neural networks (ANN) at these stages. It is expectedthat the learning will be carriedout during the
modeling and prototyping stage. For the final product, the end-to-end network is transferred to the
physical device,wherethe weightisstill definedbythe software,butotherattributescanbe captured
in the hardware design.
Technical implementation
Some of the mainstudiesregionsofML-pushedPHY-layerencompasschannelcoding,synchronization,
positioning, and channel estimations. Coping with these items, this section defines their definitions,
and gives their technical traits and prospects.
Advantages
➨It isdesignedtosupporthighernumberof mobile connectionsgreaterthanthe 5G capacitywhich
isabout 10 x 105
perKm2
.
➨6G will revolutionize the health-care sectorwhicheliminatestime andspace barriersthrough
remote surgeryandguaranteedhealth-care workflow optimizations.
➨Asmost of the mobile trafficisgeneratedindoors.Moreovercellularnetworkshave neverreally
beendesignedtotargetindoorcoverage efficiently.6Govercomesthese challengesusingfemtocells
or DistributedAntennaSystems(DASs).
➨6G usesTHz (Terahertz) frequencieswhichhasmanyadvantagesasfollows.THzwavescaneasily
absorbsmoisture inthe airhence it isuseful forhighspeedshortrange wirelesscommunications.
Terahertzoffersnarrowbeamandbetterdirectivityresultingintosecure communicationwhichis
achieveddue tostronganti interferencecapability.Highwirelessbandwidth(several tensof GHz)
from108 to 1013 GHz can deliverhighercommunicationrate inTb/sec.Inspace communication,
terahertzwavesare usedto possible lossless transmissionbetweensatellites.Beamformingand
massive MIMO multiplexinggainhelpstoovercome rainattenuationandfadingpropagationinorder
to meeturbancoverage requirements.Terahertzwave'sphotonenergyisverylow (about10-3
eV
whichoffershigherenergyefficiency.THzwavescanpenetrate substanceswithlessattenuation
whichcan be usedfor some special communicationmeans.
➨6G wirelessusesvisible lightswhichleveragebenefitsof LEDssuch as illuminationandhighspeed
data communication.VLCdoesnotproduce EM(electromagnetic) radiation.Hence itisnot
susceptibletoexternal EMinterference.VLCalsohelpsinbuildingnetworksecurity.
➨6G offersvery highdatarate (Tb/sec) andverylow latency(sub-ms).Hence manyapplicationscan
make use of 6G wirelessnetworks.
➨6G will virtualize additional components,suchasPHY layerand MAC layer.CurrentlyPHY/MAC
implementationsrequire dedicatedhardware implementations.The virtualizationwill decrease the

4. costs of networking equipment’s.Thismakesmassivelydensedeploymentin6G economically
feasible.
Disadvantages
➨6G uses cell-less architecture and multi-connectivity. Hence seamless mobility and
integration of different kinds of links (THz, VLC, mmwave, sub-6GHz) need perfect
scheduling. In cell-less architecture, UE connects to the RAN and not to a single cell. The
challenge here is to design new network architecture.
➨6G uses THz (Terahertz) frequencies for part of its communications, hence drawbacks of
THz can be considered as drawbacks of 6G wireless technology. The terahertz frequency
refers to the spectrum between 0.1 to 10 THz EM (electromagnetic) wave with wavelength of
30 to 3000 micrometers. Terahertz waves can be widely used in the space communications
particularly suitable for use between the satellite. THz signal is very sensitive to the shadows
which has great impact on coverage. Moreover lower frequency terahertz frequency incurs
larger free space fading. Ultra-large-scale antenna is a major challenge in THz which requires
high bandwidth and massive quantitative high resolution. Processing power is major
challenge in designing low power and low cost 6G devices.
➨6G uses visible light frequencies for part of its communications, hence drawbacks of VLC
can be considered as drawbacks of 6G wireless technology. Visible light uses wavelength
from 390-700 nm.
➨In order to manage large number of terminals and networking equipment’s more efficient
and less energy consuming 6G system is a must. In order to fulfill this, network and terminal
equipment’s circuitry and the communication protocol stack design is a challenge. The energy
harvesting strategies are adopted to handle this requirement.
Figures
Figure 1: The role of ML in 6G networks

5. Figure 2: Impact and uncertainty regarding deep learning-driven PHY-layer technologies
Figure 3: Research direction regarding deep learning-driven PHY-layer technologies.

6. Figure 5: Opportunistic data transfer in vehicular networks
List of abbreviations and definitions
(ML) Machine learning
(RKHS) ReproducingkernelHilbertspace
(LDPC) Low-densityparity-check
(FFT) fastFouriertransform
(ITU) International TelecommunicationUnion
(SINR) signal-to-interference-plus-noiseratio
(ANN) Artificial Neural Network
(FPGA)FieldProgrammableGate Array
(FPGA) fieldprogrammable gate array
(BER) Bit Error Ratio
(BLER) Block Error Result
(DPU) DeepLearningProcessorUnit
(LTE) long-termevolution
(FEC) Federal ElectionCommission
(LMMSE) Least MinimumMeanSquare Error
(ASIC) ApplicationSpecificIntegratedCircuit
(ITU) International TelecommunicationUnion
(MOLO) Managementof landfill operations
(SOLO) Stonehearthopenlearningopportunities
(L0) Level zero

7. (ORAN) Optical Regional AdvancedNetwork
(MOLA) Multi-OfflineLearningandAdaptation
(NR) Newradio
(SS) Schutzstaffel,orProtectionSquads
(UE) userequipment
(UE) EuropeanUnion
(ANN) artificial neural networks
(CPU) Central ProcessingUnit
(GPU) Graphics ProcessingUnit
(DPU) DeepLearningProcessorUnit
(RAM) RandomAccess Memory
(DSP) Digital Signal Processing