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Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

M. Mahdi Azari, Sourabh Solanki, Symeon Chatzinotas, Oltjon Kodheli, Hazem Sallouha, Achiel Colpaert, Jesus Fabian Mendoza Montoya, Sofie Pollin, Alireza Haqiqatnejad, Arsham Mostaani, Eva Lagunas, Bjorn Ottersten

arXiv:2107.06881v2cs.NIcs.ETeess.SP

TL;DR

NTNs are being extended beyond traditional applications because integrated terrestrial networks cannot provide ubiquitous coverage and emerging 5G/6G services require broader connectivity. The paper surveys integrated ground-air-space networks across architectures, technologies, higher layers, challenges, research, and industrial efforts. It concludes that NTNs are central to ubiquitous IoT connectivity while presenting trade-offs such as GEO delay and LEO Doppler.

  • Problem

    Terrestrial networks leave remote and unreachable areas without service, while IoT requires connectivity throughout the globe and satellite links impose extreme round-trip delay.

  • Method

    The paper provides an in-depth survey of partially and fully integrated GAS networks, covering architectures, use cases, enablers, higher layers, challenges, academic research, standardization, prototyping, and field trials.

  • Results

    The survey identifies NTNs as important to 5G and 6G integration and reports that satellites support ubiquitous IoT connectivity, with GEO offering global coverage and LEO offering better delay at the cost of increased Doppler.

  • Takeaways & Limitations

    NTNs can complement terrestrial infrastructure for continuous and ubiquitous coverage, while integrated networks must address channel characteristics, Doppler, handover, interference, and vertical integration challenges.

Abstract

from arXiv · show

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks.

I. INTRODUCTION

NTNs have expanded from traditional applications to integrated 5G/6G networks supporting connectivity, new services, and diverse architectures. This survey organizes the evolution, technologies, challenges, research, standardization, and industrial progress of integrated ground-air-space networks.

  • Motivation: Recent aerial and space technology advances and lower manufacturing and launching costs have enabled NTNs to support applications beyond traditional disaster management, navigation, broadcasting, and remote sensing.Integration with terrestrial networks targets remote, rural, desert, oceanic, and otherwise unserved areas.
  • Related Work: The survey covers satellite, UAV, and multi-segment networks, distinguishing its scope from prior surveys focused on narrower NTN components or specific design problems.It includes industrial efforts, higher-layer aspects, and a 6G vision alongside use cases, architectures, and challenges.
  • Survey Scope: Its time-evolution framework reviews NTN fundamentals, 5G integration, and 6G integration, including how NTNs assist 6G networks and benefit from 6G terrestrial networks.The review follows the progression from foundational features through use cases, enablers, challenges, proposed solutions, and selected 6G developments.
  • Coverage Dimensions: The survey examines integrated GAS architectures and use cases such as IoT and MEC, plus mmWave, THz, ML, network programmability, virtualization, and higher-layer MAC and NET advances.These topics are organized across ground-air, air-air, ground-space, space-space, air-space, and multi-segment connectivity.
  • Evidence Base: The paper reviews academic results and industry and academia efforts involving standardization, prototyping, and field trials across the NTN evolution toward 6G.Its organization also includes dedicated treatment of IoT, MEC, ML-powered communications, higher layers, industrial players, and the 6G vision.

II. NTNS INTEGRATION IN 5G ECOSYSTEM

NTNs integrate with terrestrial networks through satellite and airborne platforms that can serve as users, relays, base stations, or components of mixed architectures. Their integration expands coverage but introduces distinctive delay, Doppler, handover, interference, and space-weather challenges.

  • NTN Components: NTNs comprise GEO, MEO, and LEO satellites plus HAPs and UAVs that can extend terrestrial connectivity to unreachable or remote areas.The platforms differ in altitude, orbit, and coverage characteristics.
  • Integration Approaches: NTN components can integrate at the PHY or NET layer, with the former using the same radio access technology as terrestrial networks.The integration point determines how the non-terrestrial component participates in the communication chain.
  • General Architecture Options: Architecture options position the NT platform as a user, relay, base station, or part of a mixed architecture combining multiple roles.Relays can provide backhaul or direct access, while regenerative payloads can incorporate base-station functions.
  • Key Features and Challenges: 270 ms round-trip latency can occur over GEO links, creating bottlenecks for ultra-low-latency services and affecting retransmissions, response time, CQI freshness, access, and synchronization.Longer propagation paths also increase path loss, while UAV and HAP links can remain closer to terrestrial ranges.
  • Key Features and Challenges: 48 kHz peak Doppler shift occurs for a 600 km LEO satellite at 2 GHz, requiring adaptation of OFDM subcarrier spacing and reference signals.The shift is reported as around ten times higher than for a user inside a high-speed train.
  • Key Features and Challenges: NGSO mobility requires frequent handovers for service continuity, while large NTN footprints increase interference risk and motivate spectrum coexistence.Orthogonal frequency allocation may be impractical under spectrum scarcity.
  • Key Features and Challenges: Space-weather radiation, particularly in MEO and GEO environments, can endanger spacecraft and motivates radiation-resistant satellite design.LEO orbits below 1500 km are mostly below the radiation belts.

B. Key Drivers for NTNs Integration in 5G

NTNs support 5G by extending coverage, improving reliability, enabling scalable delivery, and complementing terrestrial networks in underserved or disrupted areas. Their integration also introduces propagation, mobility, and coexistence challenges requiring new solutions.

  • NTNs improve 5G coverage in unserved, underserved, and disaster-hit regions by providing cost-effective ubiquitous services from space or sky.
  • NTNs improve reliability by supporting continuous service for IoT devices and passengers aboard ships, aircraft, and trains.
  • NTNs enable scalable broadcasting, streaming content across large areas, and offloading popular content to edge caches.
  • NTN access networks mainly support eMBB and mMTC, while UAV aerial base stations can also contribute to uRLLC use cases.Satellite propagation delay is a major barrier to uRLLC, whereas UAVs can contribute substantially.
  • 5G and beyond systems are expected to rely increasingly on NTNs for global services because they extend coverage and offload traffic in congested areas.Non-terrestrial channels also create technical challenges that require novel solutions.
  • mmWave NTN broadband integration attracts research because 5G NR uses 24.25–52.60 GHz spectrum, but it raises architecture, allocation, coexistence, and propagation challenges.mmWave requires re-investigating path loss, penetration loss, and shadow-fading models; mobility also demands beamforming, tracking, and handovers.

A. Satellite Operation in mmWave

mmWave satellite links offer high capacity and global coverage, but their integration with cellular networks must address delay, Doppler, moving cells, scarce resources, interference, and spectrum sharing.

  • Satellite mmWave links promise high intrinsic capacity and global coverage, motivating their integration into cellular broadband networks.3GPP Release 16 identified satellite support over mmWave and treated NR functionalities as a basis for NTN scenarios.
  • 3GPP identified propagation delay, Doppler effects, and moving cells as NTN issues requiring timing, synchronization, and PRACH enhancements.
  • Satellite mmWave architectures include ground-space networks, hybrid backhaul systems, SDN-based designs, self-organizing networks, and cloud-based multimedia platforms.
  • Large antenna arrays can counter severe Doppler shifts and attenuation in space-to-ground mmWave links while retaining small apertures due to short wavelengths.
  • 3) Resource Allocation: Satellite resource allocation must jointly consider channel conditions, traffic demands, inter-beam interference, frequency assignment, bandwidth, and carrier aggregation.Demand-dependent flexible channel allocation may be efficient but makes the allocation problem more challenging.
  • 4) Performance Evaluation: Rain-attenuation-based relay selection reduced computational complexity while improving outage performance in a hybrid ground-space network.The study also considered fixed- and variable-gain relaying protocols.
  • Coexistence with terrestrial mmWave cellular networks creates interference and spectrum-sharing challenges, particularly in the Ka-band used by older satellite technologies.The 26.5–40 GHz Ka-band overlaps with existing weather-forecasting and VSAT uses.

B. UAV Operation in mmWave

mmWave UAV networks offer high-bandwidth, low-interference aerial connectivity, but mobility, propagation, beam alignment, and handover management remain central challenges. Integrated multi-segment architectures extend coverage and capacity while requiring efficient coordination across layers.

  • Architecture: mmWave UAV networks support aerial access, aerial relaying, and flexible aerial backhaul for high-bandwidth communications.Reported advantages include peak data rates up to 10 Gbit/s, compact antenna arrays, and reduced interference from directive transmission.
  • Channel Modeling: UAV mmWave channel modeling must account for rapid mobility, temporal and spatial variation, airframe shadowing, Doppler effects, and three-dimensional blockages.These factors make UAV channel models a continuing research challenge.
  • Channel Modeling: Ray-tracing studies at 28 GHz and 60 GHz found that two-ray propagation can apply across urban, suburban, rural, and over-sea environments.Other models include random UAV vibrations, orientation fluctuations, and antenna directivity effects; increased directivity does not necessarily improve performance.
  • Beamforming: Directive beamforming improves security, reduces interference, and can provide aerial coverage up to 100 m and beyond, but narrower beams increase misalignment and tracking demands.Dense mmWave deployments also create frequent handover problems as cell sizes shrink, with drones experiencing at least one handover per minute.
  • Multi-Segment Operation in mmWave: NTN mmWave links can serve locations where terrestrial deployment is infeasible and deliver broadband access globally, including remote ocean regions.Satellite links still require solutions for propagation delay, Doppler effects, spectrum sharing, and mobility management.
  • Multi-Segment Operation in mmWave: UAVs provide on-demand dynamic infrastructure as base stations, relays, or mesh nodes, while blockage, wobbling-induced beam misalignment, mobility, and multi-layer management constrain performance.Efficient network management is needed for seamless vertical integration of multi-segment networks.

IV. NTNS INTEGRATION IN IOT

NTN integration extends IoT connectivity through satellites and UAVs, especially where terrestrial infrastructure is unavailable or damaged. The main design focus is adapting architectures and protocols to orbital impairments while balancing coverage, delay, Doppler, energy, latency, and reliability.

  • Satellite Integration in IoT: IoT connectivity must remain available globally, while UAVs and satellites address areas lacking or losing terrestrial infrastructure.UAVs offer swift, flexible access but have limited mission duration and coverage; GEO satellites provide global connectivity during temporary or permanent terrestrial outages.
  • Satellite Integration in IoT: LEO satellites provide lower communication delays and lower propagation losses than GEO satellites, but their high speed introduces stronger Doppler effects.The Doppler effect results from rapid satellite movement relative to users on Earth.
  • Satellite Integration in IoT: NTN-IoT architectures include GEO, MEO, and LEO options, with direct satellite access to terrestrial IoT users widely considered.The selected orbit determines the impairments imposed on the communication link.
  • Satellite Integration in IoT: Research and standardization adapt terrestrial IoT technologies to satellite channels by compensating added delay and Doppler effects so links appear more terrestrial-like.Other proposals include Turbo-FSK-based NB-IoT air interfaces and satellite-specific NB-IoT receiver architectures.
  • Satellite Integration in IoT: Satellites integrated into IoT networks are intended to provide service ubiquity, continuity, and scalability in unserved, disaster-hit, or terrestrial-outage regions.These services are positioned as cost-effective space-based connectivity for locations where terrestrial networks are unavailable or inapplicable.
  • UAV Integration in IoT: UAV-IoT integration enables connectivity in infrastructure-free areas and can extend UAV traffic-management coverage through large-scale IoT deployments.The integration introduces challenges requiring careful attention.
  • UAV Integration in IoT: UAV trajectory planning must jointly consider energy constraints, latency, and reliability requirements for the associated IoT network.Limited battery life makes trajectory planning essential for successful UAV missions.
  • UAV Integration in IoT: UAV data collection can be performed through direct access, relaying, or multi-UAV cooperation, with research addressing energy efficiency, trajectory, mission time, and throughput.Online scenarios include multiple UAV relays and UAV-enabled LPWAN designs for mobile IoT nodes.

2) UAV-Enabled WPT for IoT:

NTN-enabled IoT connectivity combines UAV-based wireless power transfer, localization, and multi-segment satellite–UAV–terrestrial networking. The surveyed studies optimize UAV placement, altitude, trajectory, power, and resource allocation to improve coverage and device support.

  • WPT: UAV antenna arrays can charge multiple IoT users simultaneously through multiple RF beams, subject to altitude, coverage, and charging-time constraints.The same line of work also considers trajectory and beam-pattern optimization.
  • WPT: UAVs can support simultaneous wireless information and power transfer by jointly optimizing trajectory and transmit power.Users divide received signal power between energy harvesting and information decoding through power splitting.
  • Localization: UAV anchors provide promising localization for GPS-free IoT devices while meeting their size and power constraints.RSS-based frameworks investigate multiple hovering UAVs and identify altitude as a localization design parameter.
  • Localization: Three UAVs at h = 1000 m achieve accuracy requiring 14 terrestrial anchors at h_TA = 50 m, demonstrating the effectiveness of UAV deployment for localization.An optimal altitude minimizes average localization error in both urban and suburban environments.
  • Multi-Segment Integration: Integrated GAS networks can combine satellite coverage with UAV relaying, while requiring careful optimization of UAV number, altitude, trajectory, waypoints, backhauling, and resource allocation.UAVs can relay battery-limited IoT traffic to satellites, and satellites can backhaul UAVs across large distributed networks.

V. NTNS INTEGRATION IN MEC NETWORKS

NTN integration extends mobile edge computing across ground, air, and space segments for offloading computation from resource-limited devices. Satellite MEC offers broader reach but can incur higher latency and architectural complexity, while UAV MEC introduces payload and energy constraints.

  • Background and Architecture: MEC brings cloud-computing facilities near network edges, and NTN MEC extends this model to ground-space and UAV-assisted computing.LEO satellites can host MEC servers for offloading tasks from densely distributed terrestrial devices.
  • Ground-Space MEC: Satellite-assisted MEC may have higher latency than ground-air MEC but can still improve latency relative to remote cloud computing.This trade-off is relevant when terrestrial access is limited.
  • Ground-Space MEC: Satellite MEC studies analyze offloading decisions, response time, energy consumption, propagation delay, queuing delay, packet errors, latency, and jitter.Approaches include threshold-based selection between terrestrial and LEO MEC servers and game-theoretic optimization.
  • UAV-Assisted MEC: UAVs can operate as MEC servers, but onboard processing requirements for applications such as disaster resilience and navigation can increase payload and power consumption.These demands may shorten UAV battery life.
  • Integrated GAS MEC: Integrated GAS MEC architectures can flexibly process IoT tasks locally at UAVs or through other network segments.Research considers binary offloading, trajectory scheduling, bit allocation, and joint offloading-resource optimization.

4) Resource Allocation:

NTN resource allocation spans computation, communication, and network-management decisions across heterogeneous ground, air, and space segments. The surveyed approaches target energy, completion time, computation rate, and system efficiency while accounting for constrained airborne and satellite resources.

  • Offloading: UAVs can execute delegated terminal tasks through partial or binary offloading to reduce device energy consumption.The surveyed schemes minimize total terminal-device energy under one-by-one or flying-base-station access designs.
  • GAS Resource Allocation: Integrated GAS architectures opportunistically access different network segments to provide flexible MEC offloading services for IoT applications.Wind-farm inspection is one example, with sensory data offloaded to ground stations or satellites while jointly optimizing trajectory and computation.
  • MEC Benefits and Constraints: MEC can reduce UAV energy consumption and extend operational lifetime by offloading heavy tasks to ground or space servers.Satellite MEC may be useful where terrestrial networks are inaccessible despite higher latency and architectural complexity.
  • Organization: Resource allocation for NTN MEC is organized alongside background, architecture, and related machine-learning material, with the topic summarized in the NTN integration table.The supplied table passage identifies the MEC integration table but provides no detailed entries.

4) Where to/not to apply machine learning:

Machine learning is surveyed as a tool for managing complex NTN communication and networking problems, including resource allocation, beam management, handover, channel modeling, and UAV control. Reinforcement learning is attractive for sequential decisions but faces poor training-phase performance in real-world applications.

  • Learning Limitations: A major limitation of reinforcement learning is poor performance during training, motivating research on limiting bad agent behavior in deployment-oriented settings.The passage identifies this as a central concern for real-world RL applications.
  • ML Architecture: ML applications in NTN architectures can be local, joint, or end-to-end depending on which network entities participate in the learning operation.Examples include local channel-coding optimization and joint user–base-station optimization.
  • Satellite Applications: Satellite ML research targets spectrum sharing, resource allocation, beam hopping, offloading, battery-aware power allocation, handover, and channel-parameter estimation.Deep learning can estimate path-loss and shadowing parameters from two-dimensional satellite images without a 3D model.
  • UAV Applications: UAV networking studies apply multi-armed bandits, Q-learning, actor-critic methods, and deep reinforcement learning to association, trajectory, power, and subchannel decisions.These methods are discussed together with their respective advantages and disadvantages.

2) HO and Interference Management:

NTN research addresses UAV connectivity challenges including altitude-dependent interference, frequent handovers, beam misalignment, and complex optimization. Proposed approaches use ML and reinforcement learning for beam steering, trajectory design, resource allocation, and MEC coordination.

  • HO and Interference Management: Higher UAV altitudes increase co-channel line-of-sight interference and produce denser cell-association boundaries, causing more frequent handovers.These effects make target data rates harder to achieve and complicate mobility management.
  • HO and Interference Management: Hybrid beamforming and mean field game optimization jointly address beamforming and beam-steering in multi-UAV, multi-antenna networks.The joint sum-rate problem is decoupled into beamforming and beam-steering sub-problems.
  • HO and Interference Management: LSTM-based recurrent neural networks predict future sequential angles to establish UAV–UE links promptly and adapt beam-steering vectors.The approach exploits temporal angle data from previous time slots.
  • HO and Interference Management: DDQN and deep reinforcement learning support UAV path planning, trajectory design, transmission scheduling, and resource allocation under energy, freshness, obstacle, and processing constraints.Applications include IoT data collection, multiple UAV base stations, and UAV-enabled MEC.
  • HO and Interference Management: Joint MEC and communication-policy design remains difficult because multi-agent learning may assume perfect inter-node communication despite dynamic topologies and imperfect links.The limitation motivates joint source/channel coding and computation-process design.

6) Channel Modeling:

NTN research applies ML to channel modeling, network control, resource allocation, and higher-layer integration. The survey emphasizes that highly dynamic topologies and radio channels require flexible, reconfigurable architectures and methods adapted to NTN constraints.

  • Channel Modeling: A generative neural network models UAV mmWave channels by first predicting LoS, NLoS, or outage states, then estimating propagation characteristics.The second stage obtains path loss, delay, and angles of arrival and departure for different paths.
  • Channel Modeling: ML techniques address routing, latency, energy consumption, resource allocation, path planning, interference, beam management, mobility, user association, and channel modeling in NTNs.The survey summarizes these applications across aerial and space networks.
  • Channel Modeling: Unreliable and dynamic ground-air-space links motivate storage and flexible computation offloading through integrated architectures for distributed learning.The reported system offloads tasks to UAVs and HAPs acting as MEC servers and improves retrieval and offloading delay under unreliable conditions.
  • Channel Modeling: NTN integration requires high reconfiguration flexibility because NTN topologies and radio channels vary dynamically.This motivates architectural approaches such as SDN, while satellite, HAP, and UAV differences require distinct implementation strategies.
  • Channel Modeling: The survey expects NTN–TN integration to occur first at the architecture level and later at the physical level, with SDN supporting dynamic integration.Network virtualization and slicing provide related mechanisms for managing shared infrastructure and isolated logical networks.

2) C-RAN Architecture and Cloud/Edge Computing:

C-RAN, SDN, NFV, multipath transport, and gateway diversity are presented as architectural mechanisms for integrating satellite and aerial segments with terrestrial networks. Industrial, academic, and experimental efforts demonstrate progress toward flexible, multi-connectivity NTN services.

  • C-RAN Architecture and Cloud/Edge Computing: SDN and NFV enable terrestrial C-RAN concepts to extend toward aerial and satellite networks despite integration difficulties from incompatible interfaces and management systems.The approach decouples network functions from expensive ground components.
  • C-RAN Architecture and Cloud/Edge Computing: MP-TCP can select and use multiple radio interfaces and data paths simultaneously to improve resource usage and user experience in aerial-supported networks.Dynamic path selection is presented as important for exploiting multiple connectivity options.
  • C-RAN Architecture and Cloud/Edge Computing: Gateway management must adapt quickly to outages because maintaining at least one gateway within every aerial device’s coverage area is not always feasible.Remote, oceanic, and security-sensitive regions motivate aerial links, which require onboard routing and network management for mobile systems and dynamic traffic.
  • C-RAN Architecture and Cloud/Edge Computing: Industry and academia are developing satellite–5G convergence through 5G NR extensions, multi-connectivity, dynamic spectrum allocation, air-interface aggregation, and multi-radio slicing.Projects include 5G METEORS, 5GENESIS, and 5G-ALLSTAR.
  • C-RAN Architecture and Cloud/Edge Computing: Experimental studies demonstrate dynamic integrated satellite-terrestrial backhaul and satellite operation with a 3GPP Release 15 5G core network.Reported capabilities include topology reconfiguration, frequency reuse, and edge content delivery.

B. Aerial Networks

Aerial networks are progressing through industrial projects, consortia, testbeds, and field trials that examine coverage, cellular connectivity, mobility, localization, relaying, trajectory planning, and ad hoc networking. In 6G, aerial and satellite systems are envisioned to complement terrestrial networks and support ubiquitous connectivity and distributed computing.

  • B. Aerial Networks: Aerial-network development remains progressive, with industry and academia continuing to investigate its applications and capabilities.The survey presents both large industrial initiatives and smaller experimental efforts.
  • B. Aerial Networks: Aquila, Loon, and Nokia’s F-cell explored high-altitude coverage and flexible small-cell deployment using solar power, wind currents, and wireless backhaul concepts.These efforts targeted remote-area coverage and reduced dependence on costly backhaul wires.
  • B. Aerial Networks: Industry–academic consortia evaluated integrated cellular-satellite command-and-control systems and airspace sharing for safe and reliable unmanned-aircraft operation.DroC2om used real drone measurements and modeling for its evaluation.
  • B. Aerial Networks: Academic field trials and testbeds examined UAV relay mobility control, energy-aware path planning, flying ad hoc networks, and trajectory control using real flight measurements.Implementations included 802.11 links and Raspberry Pi modules on multiple UAVs.
  • B. Aerial Networks: 6G NTN integration is framed around enhanced eMBB, mMTC, and uRLLC together with new spectrum and AI capabilities.The survey organizes the vision around prospective use cases, architectures, enablers, and higher-layer aspects.
  • B. Aerial Networks: UAVs and satellites can support terrestrial edge sites through computation and coordination, while satellites can distribute intensive computation loads across network locations.Interconnected LEO satellites and aerial access points are also envisioned to extend Internet services to oceans, deserts, aircraft, and ships.

3) Pervasive Intelligence:

Integrated GAS networks combine global data availability, communication, localization, sensing, and distributed architectures to support pervasive intelligence and new aerial applications. Their deployment requires architectures adapted to multi-segment connectivity and the distinct requirements of aerial and space nodes.

  • Pervasive Intelligence:: Satellite and aerial platforms can provide globally available data that improves the effectiveness and parameter adjustment of ML-based wireless-network solutions.The passage links integrated GAS networks and satellite availability to holistic data integration and globally informed ML parameter adjustment.
  • Pervasive Intelligence:: Communication, localization, and sensing are envisioned to share time-frequency-spatial resources, enabling spectrum-efficiency gains and mutual service benefits.Dense communication devices can support SLAM, while location information can provide context for communication and sensing.
  • Pervasive Intelligence:: NTN context-awareness can select terrestrial, aerial, and space elements according to application requirements.The passage presents this selection as an example of fine-grained context-awareness enabled by NTN integration.
  • Pervasive Intelligence:: Safe beyond-visual-line-of-sight UAV operation can be supported by effective UTM using terrestrial or satellite communication.The passage identifies UAV command and control beyond visual sight as a limiting factor and connects UTM with safe BVLoS operation.
  • Pervasive Intelligence:: Integrated GAS architectures must address multi-segment terminals, heterogeneous link budgets and speeds, and uninterrupted intra- and inter-layer connectivity.The architecture must support terminals on the ground, in the air, and in space, including low-mass, low-power antennas for nanosats.
  • Pervasive Intelligence:: Cell-free aerial access points offer opportunities for interference-robust communication and have shown superior performance to traditional massive MIMO for UAV users.The cited passage attributes these opportunities to eliminating cell boundaries and reports the performance comparison for UAV users.

4) Mega LEO Constellation:

Mega LEO constellations expand 6G coverage, capacity, and connectivity while increasing interference, Doppler, mobility, spectrum-sharing, and network-management challenges. The section surveys enabling technologies and communication designs for coordinating heterogeneous satellite and terrestrial segments.

  • Mega LEO Constellation:: Satellite technology advances have reduced building, launching, and operating costs while enabling faster, more flexible deployment of large LEO constellations.These changes support the expected vision of global broadband coverage and more effective space integration into 6G networks.
  • Mega LEO Constellation:: Communication design in GAS networks is inherently coupled with asset control because platform attitude changes antenna orientation and the experienced channel.The coupling follows from antennas being firmly attached to flying assets.
  • Mega LEO Constellation:: Joint sensing and communication can exploit high RF spatial resolution; a 10 GHz radar centered at 145 GHz enables 30 mm range resolution.The passage also notes that cell-free spatial resolution can support sensing with lower bandwidths.
  • Mega LEO Constellation:: Aerial IRSs can provide line-of-sight links while passive operation reduces UAV energy costs when payload and endurance constrain active transceivers.UAVs may be unable to carry heavy RF equipment, and active nodes can reduce operational lifetime.
  • Mega LEO Constellation:: Multi-connectivity across heterogeneous radios and frequencies can extend cell boundaries toward cell-free operation among ground and aerial access points.The passage connects this opportunity with 6G devices supporting multiple radios over a range of frequencies.
  • Mega LEO Constellation:: Higher-altitude integration increases interference-management demands, while dynamic spectrum sharing is proposed for LEO and GEO satellite coexistence with terrestrial networks.Interference from and to aerial platforms increases with altitude before signals become very weak at extreme altitude.
  • Mega LEO Constellation:: THz communication promises Tbps links but remains immature because THz propagation channel modeling is not yet fully understood.The passage characterizes THz communications as requiring dedicated research before practical realization.
  • Mega LEO Constellation:: Task-oriented inter-satellite communication can optimize machine-to-machine exchanges for coordinated observation, remote sensing, handover, and load-balancing tasks.It does so by considering the value of communicated bits while maintaining constellation task performance.

8) Quantum Satellite Networks:

Quantum satellite networks extend secure key-distribution links beyond terrestrial optical-network limitations, while integrated GAS networks require new higher-layer architectures and programmable management. The survey concludes that NTN integration broadens coverage and services but remains constrained by channel, mobility, interference, and scalability challenges.

  • Quantum Satellite Networks:: Satellite-assisted QKD can overcome long-distance attenuation and intercontinental-communication difficulties in terrestrial optical networks by combining fibre and free-space links.The passage describes quantum communication as secure information sharing between distant parties and cites study cases for integrated links.
  • Quantum Satellite Networks:: The heterogeneous technologies of upcoming 6G networks create higher-layer management challenges for integrated NTNs.The survey identifies this heterogeneity as a direct challenge for network management.
  • Quantum Satellite Networks:: Fully integrated NTNs require architectures that harmonize network elements and execute strategies and protocols across terrestrial, aerial, and space domains.The passage specifically highlights the need to extend terrestrial services efficiently into aerial and space domains.
  • Quantum Satellite Networks:: QUIC reduces connection latency through transport-crypto integration and avoids head-of-line blocking through independent multiplexed streams.The passage presents QUIC as an alternative to TCP with TCP-like properties over UDP.
  • Quantum Satellite Networks:: NTN virtualization must adapt virtual-network configurations to highly dynamic networks and obtain detailed real-time state information for embedding decisions.Relevant state includes traffic load, processing capacity, energy consumption, topology, and capacity in dynamic satellite constellations.
  • Quantum Satellite Networks:: Edge computing addresses low-latency services, while 6G requires decentralized computing, storage, and network intelligence across nodes.The passage contrasts this requirement with cloud-RAN congestion caused by substantial traffic traversing the core.
  • Quantum Satellite Networks:: The survey identifies integrated NTN–TN networks as important for 6G, with applications including reliable BVLoS drone control and globally accessible connectivity.It also emphasizes ML, THz spectrum, and greater network programmability for challenges involving complexity, localization, coexistence, and integrated services.
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