PaperlayerView exact revision on arXiv

Source-linked AI summary

Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

Jing Li, Yong Niu, Hao Wu, Bo Ai, Sheng Chen, Zhiyong Feng, Zhangdui Zhong, Ning Wang

arXiv:2205.09214v1cs.ITmath.IT

TL;DR

Mobility support for mmWave faces challenges across diverse scenarios with distinct requirements. This survey reviews applications, mobility models, and existing solutions, and identifies open issues for mobility-aware mmWave communications.

  • Problem

    Mobility-support research spans diverse scenarios with differing requirements, and comprehensive analysis of mobility challenges and solutions remains needed.

    Source: §I · p. 2

  • Method

    The survey reviews mobile mmWave applications and mobility models, synthesizes challenges and existing solutions, and discusses open research issues.

    Source: §I · p. 2, §VI · p. 19

  • Results

    The survey finds that simple stochastic or random mobility models remain widely used, while hybrid models are drawing increasing attention for complex HetNets.

    Source: B. Applications and New Trends · p. 9

  • Takeaways & Limitations

    The survey identifies future heterogeneous networks, dynamic resource management, AI for mobility, geographical information, intelligent surfaces, reconfigurable antennas, and THz evolution as open issues.

    Source: §VI · p. 19

Abstract

from arXiv · show

Millimeter-wave (mmWave) communication technology offers a potential and promising solution to support 5G and B5G wireless networks in dynamic scenarios and applications. However, mobility introduces many challenges as well as opportunities to mmWave applications. To address these problems, we conduct a survey of the opportunities and technologies to support mmWave communications in mobile scenarios. Firstly, we summarize the mobile scenarios where mmWave communications are exploited, including indoor wireless local area network (WLAN) or wireless personal area network (WPAN), cellular access, vehicle-to-everything (V2X), high speed train (HST), unmanned aerial vehicle (UAV), and the new space-air-ground-sea communication scenarios. Then, to address users' mobility impact on the system performance in different application scenarios, we introduce several representative mobility models in mmWave systems, including human mobility, vehicular mobility, high speed train mobility and ship mobility. Next we survey the key challenges and existing solutions to mmWave applications, such as channel modeling, channel estimation, anti-blockage, and capacity improvement. Lastly, we discuss the open issues concerning mobility-aware mmWave communications that deserve further investigation. In particular, we highlight future heterogeneous mobile networks, dynamic resource management, artificial intelligence (AI) for mobility and integration of geographical information, deployment of large intelligent surface and reconfigurable antenna technology, and finally, the evolution to Terahertz (THz) communications.

I. INTRODUCTION

Mobile traffic growth and latency-sensitive applications increase demand for spectrum in dynamic settings, while mmWave offers abundant bandwidth but faces mobility-related reliability challenges. This survey reviews mobile scenarios, mobility models, existing solutions, and open issues.

I. INTRODUCTION

  • Mobile traffic growth and latency- and reliability-sensitive applications increase demand for spectrum in dynamic communication scenarios.The passage cites forecast mobile-data growth and intelligent-transportation applications as drivers.Source: §I · p. 1
  • MmWave offers abundant bandwidth, but high path loss makes reliable connectivity in highly dynamic scenarios challenging.Directional large-scale antenna arrays and adaptive beamforming support power concentration and beam alignment.Source: §I · p. 1
  • Existing surveys cover mmWave progress, but mobility support still needs categorized review across diverse scenarios and mobility levels.Requirements vary from low-speed WLANs to high-speed railway networks, including interference management, beamforming, and energy efficiency.Source: §I · p. 2
  • The survey reviews mobile scenarios and mobility models, summarizes challenges and solutions, and identifies open research issues.Its structure proceeds from applications and mobility models to mobility-support research and future issues.Source: §I · p. 2

II. APPLICATIONS OF MMWAVE COMMUNICATIONS IN MOBILE SCENARIOS

MmWave's abundant bandwidth and directional beamforming support applications across mobile scenarios. Different applications bring distinct design challenges, prompting research on communication technologies and solutions.

II. APPLICATIONS OF MMWAVE COMMUNICATIONS IN MOBILE SCENARIOS

  • Abundant bandwidth and directional beamforming support mmWave communications across a range of mobile scenarios.The passage links these capabilities to potential applications across different scenarios.Source: §II · p. 2
  • Different applications and scenarios create varied communication-system design challenges.The passage names channel modeling, beamforming design, energy efficiency, and standards development as research areas.Source: §II · p. 2
  • Research addresses scenario-specific challenges through key technologies, potential solutions, and examination of existing difficulties.The survey organizes these issues by scenario.Source: §II · p. 2

A. Indoor WLAN and WPAN

Indoor mmWave WLAN and WPAN use 60-GHz standards and abundant unlicensed bandwidth for high-rate applications. Research covers link performance, positioning, scheduling, and access-point placement.

A. Indoor WLAN and WPAN

  • IEEE 802.15.3c and IEEE 802.11ad define 60-GHz standards for indoor WLANs and WPANs, respectively.The passage says these standards directly enable indoor mmWave WLAN and WPAN applications.Source: A. Indoor WLAN and WPAN · p. 2
  • The 60-GHz band’s unlicensed bandwidth supports multi-gigabit-per-second rates and wideband multimedia applications.Examples include high-rate data transfer between cameras, pads, and personal computers.Source: A. Indoor WLAN and WPAN · p. 2
  • Indoor research examines link performance, NLoS alignment and positioning, WPAN scheduling, and multi-AP WLAN placement.The scheduling work includes interference probing, coexistence determination, and link scheduling.Source: A. Indoor WLAN and WPAN · p. 2
  • The survey characterizes mmWave systems as coexisting well with indoor WLAN and WPAN systems.The subsection’s research is summarized in Table I.Source: A. Indoor WLAN and WPAN · p. 2

B. Cellular Access

Cellular-access research adapts network models and system designs to mmWave propagation, blockages, and mobility. The survey reviews analytical frameworks and models addressing coverage, rate, handover, and other cellular challenges.

B. Cellular Access

  • Dense mmWave cellular networks were shown to outperform microwave cellular networks under realistic channel and blockage models in a stochastic-geometry framework.A related framework derived SIR-based coverage probability using an experimental mmWave channel model.Source: B. Cellular Access · p. 3
  • PPP-based analysis can estimate downlink performance in dense mmWave cellular networks, but may not represent clustered users in D2D and cognitive networks –.The cited work uses Poisson cluster process theory to represent multiple user clusters.Source: B. Cellular Access · p. 3
  • Cellular system models address propagation and blockage, human mobility, user-rate distributions, and handover –.The survey also describes work on beam scheduling and cellular coexistence with microwave cells,.Source: B. Cellular Access · p. 3
  • A simulation-based model found mmWave cellular networks to be noise-limited, with rate heavily dependent on base-station density.The survey summarizes cellular-access research in Table II.Source: B. Cellular Access · p. 3, Table II · p. 3

C. V2X Communications

V2X applications such as automated driving and in-vehicle infotainment require data rates estimated in gigabits per driving hour, beyond current V2V and V2I rates.

C. V2X Communications

  • V2X supports advanced vehicular applications including automated driving and in-vehicle infotainment.The passage estimates that these applications need transmission rates on the order of gigabits per driving hour.Source: C. V2X Communications · p. 3
  • Current V2V rates of 2-6 Mbps and V2I rates of 100 Mbps cannot satisfy the estimated demand.The passage identifies next-generation mmWave technology as the next step, but the supplied text ends before elaborating.Source: C. V2X Communications · p. 3
  • The estimated demand is on the order of gigabits per driving hour.Source: C. V2X Communications · p. 3

D. High Speed Train

High-speed trains, traveling at 350 km/h and above, face demanding communication conditions, especially in complex sections. mmWave HST systems require beam alignment and performance maintenance, while mature channel models remain undeveloped.

D. High Speed Train

  • At 350 km/h and above, HSTs face increasingly challenging communication environments, especially in complex sections.The section identifies disaster detection and improved onboard online experiences as motivating needs for future HST systems.Source: D. High Speed Train · p. 4
  • High path loss makes frequent realignment and beamforming necessary for mmWave HST communications.Short coherence time also makes conventional beam sweeping and wide beams inefficient.Source: D. High Speed Train · p. 4
  • Beam switching with an optimized beamwidth can achieve multi-Gbps throughput in HSTs.The passage also describes a distributed antenna system shown to provide Gbps data services.Source: D. High Speed Train · p. 4
  • Mature mmWave HST channel models remain undeveloped, and beam alignment and performance maintenance remain challenging at high speed.Research has examined tunnel and intra-wagon channel characteristics and train-to-trackside systems.Source: D. High Speed Train · pp. 4–5

E. Unmanned Aerial Vehicle

UAV communications use flexible aerial platforms to support high-rate data needs and extend cellular or broadband service. Their 3D propagation dynamics create channel and beam-management challenges, while clustered mmWave UAV research remains at an early stage.

E. Unmanned Aerial Vehicle

  • UAVs serve as base stations, aerial relays, and swarms, supporting sensor-data return and broadband coverage in remote areas.Their fast deployment and flexible reconfiguration can enhance existing cellular-network capacity and services.Source: E. Unmanned Aerial Vehicle · p. 5
  • 3D blockage, aircraft shadowing, and UAV fluctuation make mmWave UAV propagation distinct from terrestrial communications.The passage identifies channel characterization, fast tracking, beamforming training, trajectory prediction, and loading capacity as special issues.Source: E. Unmanned Aerial Vehicle · p. 5
  • Research on channel modeling, beam switching, and coverage analysis for clustered mmWave UAV networks is in its initial phase.Existing work includes outage analysis, 3D beamforming, and an empirical UAV-to-UAV propagation-loss model.Source: E. Unmanned Aerial Vehicle · p. 5

F. Space-Air-Ground-Sea

The paper describes a future space-air-ground-sea network spanning satellite, air, sea, and terrestrial communications, and reviews mobility models used to characterize movement patterns in mmWave systems.

F. Space-Air-Ground-Sea

  • AANET and OANET concepts extend connectivity above the clouds and ocean; the envisioned 6G network unifies satellite, air, sea, and terrestrial communications.The network is described as aiming for seamless coverage and stable broadband services.Source: F. Space-Air-Ground-Sea · pp. 5–6, Figure 3 · p. 5
  • SAGS applications generate data needs for ground, air, and sea links, while extensive dynamics in each layer complicate network planning.The passage identifies mmWave as a key enabling technology for this future network.Source: F. Space-Air-Ground-Sea · p. 6

III. MOBILITY MODELS

  • The reviewed mobility models cover human, vehicular, high-speed train, and ship movement, using factors such as speed, direction, congestion, and place preference to examine network performance.These models are presented as tools for characterizing mobile patterns and predicting mobility factors' influence on performance.Source: §III · p. 6, Figure 4 · p. 6

1) Human Mobility Model (HMM):

The survey reviews human, vehicular, high-speed-train, and ship mobility models relevant to mmWave communication. It emphasizes that mobility models should reflect the distinctive patterns and constraints of their application scenarios.

1) Human Mobility Model (HMM):

  • Random human mobility models are simple and widely used, but may show speed decay, fail to reach steady state, and miss environmental and social influences.Spatial and temporal dependencies are added to some random models to mitigate unrealistic behavior.Source: 1) Human Mobility Model (HMM) · p. 6
  • Social-aware models represent encounter timing and place preferences; a Markov-chain model uses clustered real data to represent location correlations.Community-based models also account for co-location and user influence, while social-aware models have been used for context offloading, caching, and network efficiency.Source: 1) Human Mobility Model (HMM) · pp. 6–7, Figure 5 · p. 7
  • Geographic-restriction and trace-based models capture routes, events, hotspots, obstacles, and observed mobility patterns; trace-based models can represent mmWave user mobility accurately.Trace-based models use properties such as visit frequency and popular places.Source: 1) Human Mobility Model (HMM) · p. 7

2) Vehicular Mobility Model (VMM):

  • Vehicular models include stochastic, behavioral, and trace-based approaches, with traces revealing contact-duration distributions, daily regularity, and connectivity patterns.Stochastic models capture traffic randomness but can inadequately represent complicated VANET applications; behavioral models represent human roles or social interaction.Source: 1) Human Mobility Model (HMM) · p. 7, 2) Vehicular Mobility Model (VMM) · p. 8
  • Self-adaptive vehicular models using big-data analysis, deep learning, and traffic simulators aim to represent fine-grained movement and diverse V2X requirements.The edge-assisted EVM allows vehicles to fine-tune customized prediction models through transfer learning.Source: 2) Vehicular Mobility Model (VMM) · p. 8

3) High Speed Train Mobility Model (HSTMM):

  • High-speed-train mobility models reflect predictable track-bound motion, passenger movement, and train-to-train links used for safety-critical train control.Existing work models train motion as a semi-Markov process, while frequent handovers and human travel patterns remain relevant modeling concerns.Source: 3) High Speed Train Mobility Model… · pp. 8–9
  • Although preliminary channel models exist for high-speed-train links, further investigation of mobility control is still required.Train-to-train links support collision avoidance for trains moving at high speed.Source: 3) High Speed Train Mobility Model… · p. 9

4) Ship Mobility Model:

  • Ship mobility models draw on large real-world trajectory datasets and account for sparse tracks and day–night variation in ship movement.The reviewed ship trajectory work offers guidance for ocean-network routing and studies long-term fine-grained prediction.Source: 4) Ship Mobility Model · p. 9

B. Applications and New Trends

The survey finds that simple stochastic or random models remain common in mmWave mobility research, even as hybrid models attract interest for complex heterogeneous networks. It then outlines major technical challenges for mobile mmWave communications.

B. Applications and New Trends

  • Simple stochastic or random mobility models remain widely used in mmWave research, often without fully representing behavior, traces, social relationships, or other object-specific features.The reviewed applications span different mobility-model categories, frequencies, scenarios, and speeds.Source: B. Applications and New Trends · p. 9
  • Hybrid mobility models are drawing increasing attention because a single movement pattern or model cannot describe complex mobility modes in heterogeneous networks.The survey points to examples of hybrid applications, [142].Source: B. Applications and New Trends · p. 9

IV. KEY CHALLENGES AND EXISTING SOLUTIONS

  • Mobile mmWave communications face challenges including channel measurement, channel estimation, and anti-blockage methods.The survey introduces these issues and existing solutions as technical challenges for 5G-era and later systems.Source: §IV · p. 9

1) Channel Measurements:

The survey reviews mobility-aware channel measurements, channel-modeling approaches, and channel-estimation methods for mobile mmWave systems. It contrasts model trade-offs and identifies gaps in measurement coverage and ongoing estimation needs.

1) Channel Measurements:

  • Many mmWave measurement campaigns were quasi-static and therefore did not characterize mobile channels; newer campaigns include mono-, dual-, and multi-mobility configurations.Measurements have mainly covered HST and V2X, MIMO measurements were rare, and further campaigns are needed in scenarios such as agriculture, IIoT, UAV, and SAGS.Source: 1) Channel Measurements · pp. 9–10

2) Channel Modeling:

  • Stochastic channel models include geometry-based models with geometric propagation structure and nongeometrical models such as tapped-delay-line models.Geometry-based models cover statistical characteristics across frequency ranges but face limits from measurement bandwidth and quasi-static data; tapped-delay-line models offer low complexity and acceptable accuracy for non-WSSUS V2V channels.Source: 2) Channel Modeling · pp. 10–11
  • Deterministic models, including ray tracing and map-based models, capture spatial propagation characteristics but can be computationally intensive and scenario-dependent.Ray tracing can model multipath and provide high spatial resolution; measurement campaigns for time-varying and MIMO channels also face practical constraints.Source: 2) Channel Modeling · p. 11
  • Quasi-deterministic models combine deterministic strong rays with stochastic weak and flashing rays to balance model accuracy and scenario dependence.The survey identifies MiWEBA and IEEE 802.11ay as outdoor 60 GHz examples.Source: 2) Channel Modeling · p. 11

B. Channel Estimation

  • Channel-estimation methods use compressive sensing, hybrid precoding, and angle- and delay-domain sparsity to estimate mobile mmWave MIMO channels with reduced complexity or training overhead.The closed-loop sparse scheme in is reported to provide better estimation performance and lower computational complexity than the cited state-of-the-art compressive-sensing schemes.Source: B. Channel Estimation · p. 11
  • Machine-learning channel estimation and temporal channel tracking are reviewed for time-varying channels, while correlated sparse MIMO channel structure remains a topic for further investigation.Reviewed approaches include learning-based estimation and tracking, beam tracking, and data-aided or geometry-based methods.Source: B. Channel Estimation · pp. 11–12

C. Anti-Blockage

Dynamic blockage can interrupt mobile mmWave links and sharply reduce received signal strength. The survey reviews multi-connectivity, beamforming, and relay assistance as connection-maintenance approaches.

C. Anti-Blockage

  • Dynamic blockage can interrupt links and cause received-signal-strength drops of up to 30–40 dB.In highway scenarios, average blockage duration can range from 100 ms to several seconds.Source: C. Anti-Blockage · p. 12
  • Three main approaches for maintaining reliable mobile connections are multi-connectivity, beamforming, and relay assistance.The survey illustrates these categories as connection-maintenance techniques in Fig. 8.Source: C. Anti-Blockage · p. 12, Figure 8 · p. 12

1) Multi-Connectivity:

This section surveys mobility-support techniques for maintaining reliable mmWave links and enhancing capacity, alongside unresolved design trade-offs and future needs. It also outlines directions for mobility-aware mmWave communications, including heterogeneous networks, security, resource management, AI, geographical-information integration, controllable communication environments, and evolution toward THz.

1) Multi-Connectivity:

  • Multi-connectivity maintains transmission when blockage interrupts one base-station link and can improve capacity and outage probability, but its complexity–performance trade-off and non-urban scenarios need further study.The survey also identifies scenario-specific upper-layer protocol evaluation as a research need.Source: 1) Multi-Connectivity · pp. 12–13

2) Beamforming:

  • Hybrid beamforming combines analog-array gain with digital multi-stream precoding using few RF chains, reducing energy and complexity; fast channel variation, beam alignment, and handover remain challenges.Full-digital beamforming is impractical for large arrays because it requires one RF chain per antenna element.Source: 2) Beamforming · p. 13

3) Relay:

  • Relay-aided links can bypass obstacles, extend coverage, save transmit power, and improve data rates, but mobility-driven handovers make relay selection, placement, and scheduling interdependent challenges.Prior approaches include dynamic relay positioning for throughput [219], joint relay selection and power allocation for power savings and robustness, and obstacle-aware relay decisions,.Source: 3) Relay · pp. 13–14

D. Capacity Enhancement

  • Mobile capacity enhancement addresses D2D interference and scheduling uncertainty, while energy-efficiency designs optimize power, association, precoding, and UAV placement; mobility means short routes do not guarantee energy savings.Concurrent transmission and spatial reuse can reduce power while maintaining the same or higher throughput than TDMA; mobility-aware energy use remains an area for improvement.Source: 1) Throughput Optimization · pp. 14–15, 2) Energy Efficiency · p. 15

A. MmWave Enabling HetNets

  • Future mmWave HetNets span aerial, maritime, and terrestrial links, requiring more demanding channel measurement, modeling, frequency planning, interference management, and mobility solutions for complex 3D movement.Mobility models should reflect application-specific trajectories and constraints, alongside collision avoidance, delay, and handover control.Source: A. MmWave Enabling HetNets · p. 16

B. Network Security

C. Performance Optimization

D. AI Integration

  • AI can support adaptive beam alignment and network optimization, but intelligent beam-maintenance research remains limited and focused mainly on V2X, with high-speed mobility still requiring further work.Cloud-edge-collaborated AI can distribute learning and computation across mobile, network, and cloud layers, while future research must improve scheduling, big-data processing, and mobility prediction.Source: D. AI Integration · p. 17, 1) Beam Alignment Maintenance · p. 17, 2) AI-enabled Network · pp. 17–18

E. Integration of Geographical Information

  • Geographical information can support aerial mobility management, help UAVs escape blockage to establish LoS links, and enable mobility-pattern analysis from trajectory data.Proposed uses also include satellite or aerial-node positions for measurement reporting and cell selection, studying correlations among user preference, mobility, social connections, and time-space features, and analyzing urban dynamics,,,.Source: E. Integration of Geographical… · p. 18

F. Smart and Controllable Communication Environment

1) Deployment of Large Intelligent Surface:

  • LIS can forward beams to expand coverage, combine with massive MIMO for spatial multiplexing gains, and flexibly integrate with infrastructure, but key design challenges remain.Further work is needed on CSI acquisition, passive information transfer, low-complexity robust design, dynamic LIS, edge intelligence, and physical-layer security,.Source: 1) Deployment of Large Intelligent… · p. 18

2) Deployment of Media-Based Modulation:

  • MBM encodes information through channel-fade realizations created by reconfigurable antenna RF mirrors; one antenna with N mirrors supports 2^N realizations carrying N bits, versus log2(N) bits for spatial modulation with N antennas.Combining MBM with massive MIMO can support high-throughput machine-type communications, while TI-MBM selects time slots and mirrors to maximize transmission rate; research needs include CSI acquisition and reliable antenna design,,,,,.Source: 2) Deployment of Media-Based Modulation · pp. 18–19

G. Evolution to THz

  • THz promises Tbps rates and reduced Doppler impact, but mobility faces shorter range from propagation and molecular absorption losses, wideband transceiver design, and computationally complex beam-switching codebooks.Mobility management, device design, and beam tracking remain open needs; integrating THz with mmWave and sub-6 GHz could support universal coverage and mobility,,,,,.Source: G. Evolution to THz · p. 19

VI. CONCLUSIONS

  • The survey organizes mobility-aware mmWave research around mobile applications and mobility models, existing challenges and solutions, and open issues for network planning and optimization.It covers channel measurement and modeling, channel estimation, anti-blockage, and capacity enhancement.Source: §VI · p. 19
Loading 2205.09214v1…