International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072
Intelligent 6G: Enabling AI-Driven Augmented Reality with Ultra-Low Latency
Mahesh Devdatta Telang
Embedded
Software Engineer, Meta
Abstract - The advent of 6G wireless technology promises transformative connectivity, characterized by ultra-low latency, exceptionally high terabit-class data rates, and ubiquitousseamlesscoverage.Thisnext-generationnetworkis essential for enabling demanding applications such as ArtificialIntelligence(AI)-drivenAugmentedReality(AR)and immersive Extended Reality (XR), which require stringent performance metrics alongside optimized energy consumption. This paper examines the fundamental requirements of 6G networks, highlighting the vital role of adaptive AI models in achieving intelligent connectivity management. We propose a hierarchical AI architecture comprising a lightweight model for real-time, granular network monitoring and a more complex large AI model for proactive,long-termpredictiveoptimization.TheseAI-driven mechanismsarekeytofacilitatingseamlesshandoversacross diverse network environments and intelligent Radio Access Technology (RAT) transitions. This approach aims to guarantee sustained terabit-per-second connectivity and significantlyimproveenergyefficiency,crucialforimmersive ARexperiences.Wefurtherexplorethenecessaryarchitectural innovations, key technical challenges, and a potential roadmap for realizing AI-enhanced connectivity in the forthcoming6Gera.
Key Words: 6G, 5G, Augmented Reality (AR), Artificial Intelligence(AI),HierarchicalAI,SeamlessHandovers,UltraLow Latency, Radio Access Technology (RAT) Adaptation, PowerOptimization,NetworkSlicing,ExtendedReality(XR), EdgeComputing.
1.INTRODUCTION
The evolution of wireless communication consistently pushes connectivity boundaries, with each generation unlocking new capabilities. 6G wireless technology is envisionedasatransformativeleap,designedtoovercome the limitations of current 5G networks and establish unprecedented benchmarks for performance, including ultra-lowlatencycommunicationpotentiallytargetingendto-end latencies below one millisecond [1]. Unlike its predecessors, 6G must inherently prioritize real-time adaptability and cognitive functions to effectively support theburgeoningecosystemofnext-generationapplications, particularly AI-powered Augmented Reality (AR) and immersiveXR.WiththeglobalARmarketprojectedtoreach $340billionby2028,reflectingacompoundannualgrowth rate(CAGR)exceeding40%[2],thereisanurgentneedfor
network infrastructure capable of delivering the uninterrupted, high-speed, context-aware, and intelligent connectivity that these immersive experiences demand. Consequently,arobust6Gframeworkwithdeeplyintegrated AI [3] is essential to unlock the full potential of these applications.
This paper articulates the core capabilities 6G networks must possess to meet these demanding requirements. Specifically,weemphasizethenecessityof embeddingAIdriven decision-making mechanisms directly within the networkarchitecture.Thisintegrationallowsforproactive optimization of network transitions, intelligent resource management, and drastic latency minimization. By incorporating intelligence into every facet of network operation,6Gaimstoguaranteeseamlesshandoversacross heterogeneousnetworktechnologieswhilesimultaneously achievingultra-lowpowerconsumption acriticalfactorfor thewidespreadadoptionofportableARdevices.
2. ESSENTIAL REQUIREMENTS OF 6G NETWORKS
Toeffectivelycatertothestringentconnectivitydemands ofARandotherreal-time,latency-sensitiveapplications,6G networksmustintegrateasophisticatedtwo-tierAImodel system:
2.1 Lightweight AI Model (Real-time Network Intelligence):
DeployedpotentiallyattheRadioAccessNetwork(RAN) edge or within access points, this AI model will perform continuous, granular monitoring of prevailing network conditions.Itsresponsibilitiesincludereal-timeevaluation ofkeyperformanceindicators(KPIs)likedatarates(aiming for peaks over 1 Terabit per second [4]), signal strength, interference levels, and user mobility prediction. Furthermore, it must understand the specific Quality of Service(QoS)needsofindividual AR applications,suchas renderingcomplexity,requiredframerates(e.g.,90-120fps forimmersion),andacceptablelatencythresholds(ideally under10millisecondsmotion-to-photonlatencyforAR[5]). Based on this real-time analysis, the lightweight AI model mustproactivelypredicttheneedforseamlesshandovers betweenbasestations(gNBs)withinthesameRadioAccess Technology (RAT) or anticipate the necessity for a RAT change to maintain optimal performance and user experience. Techniques like reinforcement learning could
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072
enable adaptive decision-making based on immediate networkfeedback.
Advanced AI Model (Predictive Network Optimization): Residing likely within the core network, this more computationallyintensivemodelwillanalysevastamounts of historical network data to identify long-term trends in usage,trafficpatterns,andapplicationdemands.Employing advanced machine learning algorithms like deep learning andtimeseriesanalysis,thismodelwillcontinuouslyrefine predictiveconnectivityoptimizationstrategies.Itsfunctions include optimizing network-wide resource allocation, predicting potential congestion in specific areas or times, andproactivelyadjustingnetworkparametersforlong-term efficiency, reliability, and resilience. For instance, it could learn user behaviour patterns to pre-configure network resourcesalongprobabletrajectories,minimizinghandover latencybeforeitbecomescritical.
By leveraging these two AI tiers synergistically, 6G networks can dynamically and intelligently adapt to constantly changing network conditions and the diverse needsofARapplications.Thisadaptivecapabilityiscrucial for ensuring fluid, responsive, and immersive AR experiences,freefromconnectivitydisruptionsorexcessive devicepowerconsumption.
2.2
Seamless Handovers and Intelligent RAT Management:
Achievingtrulyuninterruptedconnectivity,acornerstone forcompellingAR,requiressignificantadvancementsin6G handovermechanismsandRATmanagement:
ProactiveContextualNetworkEvaluation:Movingbeyond reactive triggers like signal strength thresholds, 6G networks, guided by the lightweight AI model, must continuouslymonitormultiplecontextualparameters.This encompassesnotonlysignalqualitybutalsonetworkload, latency, available bandwidth, and the specific QoS requirements of the active AR application. By anticipating potentialdisruptionsbeforetheyimpactuserexperience,6G caninitiatehandoverprocedurespreemptively.
IntelligentRATSelectionBasedonReal-timeNeeds:6Gis expectedtoutilizeadiversemixofRATs,includingenhanced 5GNewRadio(NR),unlicensedspectrumtechnologies(like Wi-Fi7,offeringmulti-gigabitspeedsandlowerlatency[6]), andpotentiallyTerahertz(THz)communicationsforultrahigh bandwidth short-range applications [7]. Informed by theAImodels,theintelligentRATmanagementsystemmust dynamicallyselectthemostsuitableRATbasedontheAR application'sreal-timedemands.Forexample,anoutdoor, high-mobility scenario might necessitate switching to a robustcellularRAT,whereasanindoor,high-bandwidthtask couldtriggeratransitiontoanadvancedWi-Finetwork.
PowerOptimizationviaEfficientHandovers:Traditional handoversofteninvolvesignificantsignalingoverheadand can cause temporary throughput drops, increasing device powerconsumption.6Gmustimplementhighlyoptimized handover mechanisms that minimize this overhead and ensure rapid, efficient transitions. Furthermore, the AI models should learn to avoid unnecessary "ping-pong" handovers between closely located base stations by consideringhistoricaldataandpredictingfuturemovement, therebyconservingvaluablebatterylifeonARdevices.
Ultimately, 6G networks must strive to eliminate perceptible latency spikes during transitions between different RATs, aiming for near-instantaneous handovers (ideally within milliseconds). This will ensure AI-driven applications, particularly AR, function without noticeable performance degradation, providing a truly seamless and immersiveuserexperience.
3. POWER OPTIMIZATION AS A CRITICAL REQUIREMENT FOR 6G
While 5G focused heavily on achieving ultra-high speeds, notablythroughmillimeterwave(mmWave)technology,6G must elevate power efficiency to a paramount design principle.AlthoughmmWaveoffersmulti-gigabitdatarates beneficial for demanding AR applications, it presents inherentchallenges:
High Power Consumption: mmWave technology typically requires significant power to overcome path loss and operateeffectively.Thishighdemandisamajorlimitation for battery-powered devices like AR/VR devices and wearables, where battery size and weight are critical constraints[8].
Line-of-Sight(LoS)Limitations:mmWavesignalsareeasily attenuated by obstacles (walls, trees, people), often requiringadirectLoSpathbetweenthedeviceandthebase station (gNB). This complicates seamless mobility for AR applicationsneedingconstantconnectivityinclutteredrealworldenvironments.
ComplexAntennaDesign:Compensatingforhighpathloss necessitatescomplexmulti-dimensionalantennaarrayswith sophisticatedbeamforming inboth basestationsanduser devices.Theseadvanceddesignsincreasecomplexity,cost, andpowerrequirements,impactingdevicebatterylifeand formfactor.
Thesechallengesareparticularlyacutefornext-generation ARdevicesandwearables,wherecompactsize,lightweight design,andextendedbatterylifearevitalforusercomfort, usability, and adoption. Unlike smartphones, AR devices must prioritize power efficiency for prolonged operation withoutcompromisingcomfortoraesthetics.Therefore,6G developmentmustfundamentally:
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072
DevelopNewRATsBalancingSpeedandEfficiency:Beyond enhancingexistingtechnologies,6Gresearchexploresnovel RATsinbandslikesub-Terahertz(sub-THz)(e.g.,100GHz-1 THz). These bands potentially offer a compelling balance betweenhighdatarates(tenstohundredsofGbps[9])and improvedpropagationcharacteristicscomparedtommWave, potentiallyyieldingbetterpowerefficiencyandcoverage.
Optimize Handover Mechanisms for Energy Savings: As previously discussed, intelligent, AI-driven handover mechanisms are crucial for minimizing energy spent on network transitions. Predicting user movement and application needs allows 6G to optimize handover timing andtargetcellselection,reducingunnecessarytransitions andassociatedsignallingoverhead.
IntegrateAI-DrivenPowerManagement:6Gnetworksneed sophisticatedAI-poweredstrategiesthatdynamicallyadapt deviceconnectivitysettingsbasedonreal-timeapplication demands and network conditions. For instance, if an AR applicationtemporarilyrequireslessbandwidth,thenetwork could intelligently instruct the device to reduce transmit powerorswitchtoamorepower-efficientRAT.Techniques likedynamicfrequencyscaling,adaptivebeamforming,and intelligent sleep mode scheduling, guided by AI, will be essentialforsignificantpowersavings.
Bystronglyemphasizingpoweroptimization,6Gnetworks candeliverthehigh-performanceconnectivityrequiredfor immersive AR while ensuring next-generation wearable devicesarepractical,comfortable,andcapableofextended use, paving the way for their widespread integration into dailylife.
4. CHALLENGES AND FUTURE DIRECTIONS
While AI-driven adaptability in 6G offers significant advantages for applications like AR, several critical challengesmustbeaddressedforsuccessfulrealizationand deployment:
ComputationalEfficiency:ImplementingsophisticatedAI models for real-time monitoring and prediction demands substantialcomputationalresources.Thesemodelsmustbe optimized to run efficiently not only on network infrastructure(basestations,edgeservers)butpotentially also on resource-constrained edge devices like AR/VR devices. Excessive processing overhead could increase power consumption and latency, counteracting intended benefits. Future work must focus on lightweight, energyefficient AI algorithms, specialized edge AI hardware accelerators,anddistributedAIarchitectureslikefederated learningtobalancecomputationandprivacy.
Security and Privacy: Embedding AI into network management introduces new security vulnerabilities and privacyconcerns.AIdecision-makingmustberobustagainst adversarialattacksthatcouldcausenetworkdisruptionor
security breaches. Furthermore, collecting and analyzing network data for AI training must adhere to strict data protection regulations and privacy-preserving principles. Research should focus on robust AI security frameworks, techniqueslikedifferentialprivacyandsecuremulti-party computation,andclearethicalguidelinesforAIinnetwork management.
Interoperability and Standardization: Successful 6G deploymentrequiresuniversalprotocolsandstandardized interfaces for AI-driven connectivity management across differentvendors,operators,andregions.Interoperabilityis crucial for seamless roaming and consistent AR user experiences. Standardization efforts (e.g., via IEEE, 3GPP) must cover AI model exchange formats, data sharing protocols, and common APIs for network control. Collaboration among industry, research institutions, and standardsbodiesisessential.
Future research should prioritize developing novel, energy-efficient AI models tailored for wireless network management,refiningpredictiveanalyticsforaccuracyand responsiveness, and enhancing AI security and privacy frameworkstoensuretrustworthy6Gnetworks.Exploring emerging technologies like quantum computing for potentially accelerating AI training and network optimizationcouldalsobeapromisingresearchavenue.
5. CONCLUSIONS
6Gnetworksare poisedto usherin a new era of wireless connectivity, characterized by ultra-low latency, seamless mobility,andintelligentadaptability,specificallydesignedto supportdemandingnext-generationapplicationssuchasAIpoweredAugmentedReality.Byintegratinga hierarchical two-tier AI model system, 6G networks will be capable of real-timenetworkoptimizations,facilitatinguninterrupted transitions between diverse RATs while simultaneously optimizingpowerconsumptiononuserdevices.Recognizing the critical importance of power efficiency, 6G must prioritize this as a fundamental design consideration, ensuring that compact and lightweight AR devices and wearables can function optimally for extended periods withoutbeingconstrainedbybatterylimitations.Thispaper hashighlightedtheessentialcapabilities,keychallenges,and promisingfuturedirectionsfor6Gnetworks,pavingtheway for an intelligent, seamlessly adaptive, high-performance wirelessfuturethatwillrevolutionizehowweinteractwith the digital world through immersive experiences like AugmentedReality.
REFERENCES
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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072
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