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Communication and networking technologies for UAVs: A survey

Abhishek Sharma, Pankhuri Vanjani, Nikhil Paliwal, Chathuranga M. Wijerathna Basnayaka, Dushantha Nalin K. Jayakody, Hwang-Cheng Wang, P. Muthuchidambaranathane

arXiv:2009.02280v1eess.SPphysics.app-ph

TL;DR

UAV communication must support increasingly diverse and coordinated drone operations despite mobility, energy, computing, interference, and reliability challenges. This survey synthesizes emerging communication technologies, network architectures, antennas, resource platforms, AI, navigation, security, optimization, and applications. It concludes that communication remains challenging and that drone-to-drone and drone-to-device communications require further research.

  • Problem

    UAV communication faces challenges from high mobility, signal variation, inadequate energy and computing resources, interference, and application-specific network requirements.

  • Method

    The paper conducts a comprehensive literature survey of emerging UAV communication technologies, applications, architectures, antennas, resource platforms, AI, navigation, security, and optimization.

  • Results

    The survey identifies communication technologies, optimization methods, and AI techniques for UAV networking, while concluding that reliable and safe communication remains challenging.

  • Takeaways & Limitations

    Further research is needed on drone-to-drone and drone-to-device communications, alongside balanced designs for safety, reliability, long flying times, and low communication latency.

  • Takeaways & Limitations

    Selecting suitable AI techniques for multidimensional UAV communication systems remains difficult and requires further exploration.

Abstract

from arXiv · show

With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods. Encryption and optimization techniques for ensuring long lasting and secure communications, as well as for power management, are discussed. Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra reliable and low latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications. Our literature review reveals the need for more research work on drone to drone and drone to device communications.

1 Introduction

UAV communication is central to increasingly coordinated drone operations, but high mobility, changing signal conditions, energy constraints, interference, and application-specific requirements remain significant challenges. This survey reviews emerging communication technologies, network integration, AI, security, optimization, and applications, while identifying gaps in drone-to-drone and drone-to-device communication research.

  • Communication requirements: UAV communication mechanisms depend on application, ranging from line-of-sight links outdoors to satellite, cellular, Bluetooth, and point-to-point protocols.Satellite links are associated with security, defense, and extensive outreach, while cellular technologies are preferred for civil and personal applications.
  • Design considerations: Reliable UAV networks must address bandwidth, range, power, speed, compatibility, payload weight, cost, routing, resource handling, and antenna design.The paper also considers UAV integration with wireless sensor, vehicular, cellular, and Internet of Things networks.
  • Communication challenges: High UAV mobility, increased signal frequencies, Doppler effects, and changing antenna directions can cause wireless standards to produce high packet losses.These conditions complicate communication technology selection and network design.
  • Security and resources: Energy efficiency, computing limitations, aerial-network jamming, and communication failures constrain efficient, reliable, secure, and low-latency UAV communication.UAV networks are also being used for emergency communication infrastructure and surveillance.
  • Survey scope: The survey comprehensively reviews emerging UAV communication technologies, applications, challenges, and future directions across modules, antennas, resource platforms, and network architectures.Its scope includes cellular-connected UAVs, UAV-assisted wireless networks, IoT, URLLC, navigation, machine learning, and artificial intelligence.

2 Communication and Network Technologies for UAVs

UAV communication systems combine communication modules, antenna designs, resource-management platforms, and diverse networking technologies. The surveyed approaches target accuracy, stability, throughput, interference management, and efficient coordination across drone networks.

  • Communication modules: Communication modules, antenna design, network architecture, and resource-management platforms are identified as essential elements of UAV communication networks.The section compares algorithms and methods used in drone networks.
  • Wireless technologies: Existing wireless technologies including WiMAX, LTE, and ZigBee have been analyzed for improving UAV communication accuracy and stability.
  • Communication performance: MIMO-OFDM reconstructs transmitted data at the receiver with reduced overhead and computational complexity, while sum rate provides another improvement criterion.
  • Interference management: A tethered-balloon-assisted interference-alignment scheme uses half-duplex relaying to improve degrees of freedom and sum rate when high-altitude platforms lack channel state information.
  • Utility factors: Figure 3 links communication modules and development platforms to utility factors such as bandwidth, radio-control expansion, antenna security, and resource handling.

2.2 Antenna Design

UAV communication performance depends on antenna design and resource-handling architecture. The reviewed work includes bandwidth-oriented printed antennas, directional control antennas, security systems, decentralized platforms, and centralized drone-hive coordination.

  • Antenna design: Printed antenna designs, especially wrapped PIFA, are reported as effective when antenna performance is designed around bandwidth requirements.
  • Antenna design: A switch-beam circular-array antenna using two-beam-switching Yagi-Uda antennas at 2.4 GHz extends UAV-controller radio-control distance.
  • Security antennas: Dual-frequency PIFA, directional antennas, angle reflectors, RF jammers, and 3D MIMO radar support electronic-fence detection and protection against amateur drones.
  • Resource handling platforms: AuRoRA decentralizes ground-station processing by sending vehicle control signals separately, preventing overload of a single computer with flight data and control signals.
  • Resource handling platforms: Karma moves individual MAV coordination complexity to a central hive computer, simplifying hardware and software while making communication more feasible and efficient.

2.4 Networking Technologies for UAV Communication System

UAV networking research spans technologies, routing protocols, movable relays, resource allocation, path planning, and infrastructure-based or ad-hoc swarm architectures. These approaches improve flexibility, interference avoidance, operational range, recovery, and communication in isolated areas.

  • Networking technologies: WiMAX, ZigBee, WiFi, and XBee have been studied as wireless technologies for UAV communication networks.
  • Networking technologies: RMICN uses flying routers and relay nodes to connect disjointed networks, improving networking flexibility and efficiency through physical node movement.
  • Routing and resource allocation: IACO supports path planning for groups of mobile robots, while frequency-band allocation seeks to maximize simultaneous drone use while avoiding interference.
  • Swarm architectures: UAV swarm demonstrations use infrastructure-based or ad-hoc network-based architectures, including FANET relay technology for drones disconnected from ground control.
  • Network recovery: Return-to-next-hop schemes and self-recovery networks using AFW nodes, DTN, and NDN support network recovery and communication in isolated areas.

2.5 UAV-Assisted Wireless Sensor Networks and UAV-Assisted Vehicular Communication Systems

UAVs support sensor-network data collection, disaster response, and vehicular communication when terrestrial connectivity is inadequate. Cloud, IoT, and relay-based architectures extend UAV communication and computing capabilities.

  • UAV-Assisted Wireless Sensor Networks: UAV incorporation into dense wireless sensor networks is challenging because sensors are distributed across large areas.
  • UAV-Assisted Wireless Sensor Networks: UAV-assisted sensor-network approaches address disaster preparedness, assessment, response, and recovery, including mobile data collection and RSCA routing.
  • UAV-Assisted Vehicular Communication Systems: UAV relays can provide ad-hoc vehicular-network connectivity when ground communication is poor or vehicle density is too low for routing packets.
  • IoT- and Cloud-Enabled UAV Systems: Cloud and IoT integration is proposed to overcome drones' limited processing and storage, while cloudlets and computational offloading conserve energy.

2.7 UAV-Enabled Mobile Edge Computing

UAV-enabled mobile edge computing combines UAV communication and near-user processing to improve computing efficiency and reduce execution latency.

  • UAV-enabled MEC networks provide communication and near-user processing while addressing limitations of fixed-base-station MEC networks.
  • UAVs can operate as relay edge-computing nodes within mobile edge-computing networks.

2.8 URLLC-Enabled UAV Communication System

URLLC is important for mission-critical UAV control, where operator-to-drone links require strict latency and reliability. Analysis indicates that UAV positioning can enhance network rates and help meet URLLC requirements.

  • URLLC supports mission-critical wireless applications, while UAV control links impose strict latency and reliability requirements for safety functions.
  • High-rate performance can be further enhanced through optimal UAV positioning, helping meet URLLC requirements.

2.9 Integrating UAVs into Cellular Networks

Integrating UAVs into cellular networks offers mobile connectivity but introduces interference, antenna-coverage, measurement, and mobility challenges. Researchers investigate network- and user-equipment-based solutions, including advanced antennas and multiple-access techniques.

  • Integrating UAVs into Cellular Networks: Cellular-connected UAV integration has progressed from early GPRS prototypes to LTE-UAV field results and analysis of drone application requirements.
  • High Line of Sight Interference: Cellular-connected UAVs experience stronger downlink interference because high line-of-sight propagation exposes them to more neighboring cells.
  • High Line of Sight Interference: Downward-tilted base-station antennas may serve high-altitude UAVs through sidelobes, potentially making distant base stations stronger than nearby ones.
  • High Mobility: High UAV mobility causes frequent handovers and time-varying backhaul links, worsening mobility performance relative to terrestrial users.
  • Cellular Network Solutions: Proposed cellular solutions are categorized as network-based or user-equipment-based approaches.
  • Advanced Cellular Technologies: FD-MIMO uses more antennas to provide scalable, stable throughput and mitigate interference in UAV communication systems.
  • Multiple Access Techniques: Cellular-connected UAVs can use NOMA, TDMA, OMA, or BDMA; NOMA assigns different power levels to users sharing a resource block.

UAVs

The survey presents UAV communication as a promising but still developing area spanning beamforming, antenna design, cellular networking, IoT integration, and highly reliable connectivity. Cellular networks can serve UAVs, but interference, mobility, implementation, and open research issues remain.

  • Beamforming and antennas: Directional antennas reduce downlink interference from broad angles and can limit throughput impacts on terrestrial users even at high UAV density.Their effectiveness depends on implementation and antenna tracking or alignment with the line-of-sight direction.
  • Beamforming and antennas: Beamforming controls transmitted or received signal amplitude and phase, while LCMV, RSB, and hybrid schemes are being investigated for cellular-connected UAVs.Highly mobile network elements make beamforming challenging in UAV networks.
  • Cellular networking: Cellular networks are capable of serving UAVs, but interference and mobility require further implementation-based solutions and specification enhancements.The survey identifies this as an ongoing challenge rather than a resolved deployment pathway.
  • Architecture and research directions: UAV communication-network architecture is affected by antenna configuration and resource-handling platforms, making antenna design, including 3D MIMO, an important research direction.The survey also reports that researchers have investigated varied cellular-connected UAV use cases while both fields remain young.
  • Open issues: Highly reliable, time-critical UAV connectivity remains challenging, while Mobile Edge Computing System approaches still face implementation difficulties but may improve network QoS.These issues are presented as fundamental open questions for URLLC-enabled UAV communication.
  • IoT integration: Future communication networks must integrate diverse IoT technologies, with UAVs suggested as possible solutions for easing integration and addressing terrestrial-network weaknesses.The survey frames IoT integration as a continuing direction for UAV communication research.

3 Recent Technological Advancements

Recent UAV communication research combines AI, navigation, security, antenna design, and optimization to improve efficiency, reliability, latency, and energy use. The surveyed work also develops communication-aware routing, path planning, cryptographic protection, and resource-management approaches.

  • Artificial Intelligence: AI and machine learning support failure prediction, response-time prediction, computation offloading, link-quality-based positioning, and disaster-stage classification.ACODS combines machine learning with MPTCP to choose between onboard processing and transmission; link-quality updates improve stability and accuracy over KNN and TR.
  • Navigation Strategies: Path-planning methods extend service range, avoid no-signal areas, maintain communication links, and support multi-drone coordination and collision avoidance.A modified A* algorithm uses 3G communication, while other work uses broadcast-range constraints and wireless exchanges for cooperative localization and collision avoidance.
  • Secure UAV Communication: Security research addresses drone detection, Jelly Fish attacks, encrypted communication, key generation, authentication, intrusion detection, and regulatory uncertainty.Approaches include 60 GHz detection, multicast routing, optical codewords, homomorphic authenticated encryption, EEG-derived AES keys, quantum key distribution, and link encryption.
  • Optimization Theory for UAV Communication System: Optimization reduces power consumption and latency through computation offloading, TDMA-based BLOS control, frequency switching, and latency-aware drone base-station placement.The surveyed methods target onboard energy limits, delay fluctuation, interference robustness, coverage, and mobile-user latency.
  • Summary of Lessons Learned: Trajectory optimization is critical because UAV communication performance depends on QoS requirements, energy use, vehicle size, and environmental barriers.The summary identifies trajectory design as a central concern for UAV communication networks.
  • Summary of Lessons Learned: Security becomes increasingly important as UAV numbers grow, and software and hardware technologies can greatly mitigate threats to collected and transmitted data.The paper’s summary links expanding UAV deployment with the need to protect data against potential hijacking attempts.
  • Summary of Lessons Learned: Machine learning and other artificial intelligence techniques address navigation planning, response-time prediction, and packet-transmission failure prediction.These techniques are presented as approaches for overcoming key UAV communication challenges.

4 Applications of UAV Communication

UAV communication is applied to emergency infrastructure, surveillance, rescue, positioning, disaster management, and cellular connectivity. These systems can restore or extend communications, but highly mobile users and UAV links remain important constraints.

  • UAV-Aided Disaster Management Network: UAVs can provide emergency communication infrastructure after natural disasters by replacing damaged infrastructure or reducing deployment time.Specialized drone fleets can scan regions, convey information, and organize mission-specific functions through internal modules.
  • Surveillance and Rescue: UAV surveillance systems use communication hardware, aerial mobile stations, survivor devices, broadcast, sensor fusion, WiFi, and barometer data for public safety and survivor localization.These systems can reduce coverage gaps and network congestion while supporting location estimation for buried mobile phones.
  • Surveillance and Rescue: Positioning accuracy is improved with Kalman and optimization algorithms, as well as RTK, PPK, and GCP techniques, because conventional drone GPS can be inaccurate.The methods target reduction of distance errors in UAV positioning.
  • UAV-Aided Disaster Management Network: Cellular-connected UAV networks maintain reliable and secure connectivity, closed-circuit communication, command mechanisms, and amplified warnings when terrestrial networks are damaged.The surveyed architecture targets areas affected by conflict, natural hazards, or technological hazards.
  • UAV-Aided Disaster Management Network: Device-to-device communication can improve cellular-network reliability and uplink capacity for responders outside affected areas.The approach also provides emergency workers with an aerial view of damaged areas.
  • UAV-Aided Disaster Management Network: Traditional mobile and web video-streaming techniques are unsuitable for highly mobile UAVs, motivating algorithms that improve real-time streaming quality and reduce wireless-link uncertainty.UAV link quality depends on vehicle speed and distance between the ground station and UAVs.
  • UAV-Aided Disaster Management Network: A WiFi-integrated UAV network can provide VoIP communication for affected people, but one disaster-management network cannot handle user mobility.K-means clustering and genetic algorithms were used to improve network performance, while user mobility remained a critical weakness.

5 Challenges, Open Issues, and Future Directions

Future UAV communication research must address mobility, energy use, AI deployment, connectivity, and integration with 5G and IoT. The survey identifies open issues involving reliable high-throughput links, power management, latency, and stable multi-UAV operation.

  • Future UAV networks: IEEE 802.11x WLAN provides high throughput but is not optimized for highly mobile UAV networks.Reliable wireless technology sustaining high throughput over extended coverage remains lacking.
  • Artificial intelligence techniques: AI-based UAV communication still requires suitable technique selection and improved computation efficiency because processing and transmission latency can reduce network performance.Challenges include position verification, route management, mission-success estimation, and the complexity of multidimensional UAV networks.
  • Future UAV networks: Future systems need improved power, connectivity, stable functioning, flying time, beyond-sight control, and data-compression failure prediction.Energy conservation remains difficult in multi-UAV scenarios requiring frequent data transmission and ground-operator connections.
  • Future UAV networks: Cellular connectivity, high-altitude channel enhancement, uplink and downlink traffic management, 5G, NOMA, and IoT-based approaches remain open or promising directions.The cited emerging methods are associated with energy saving, fast integration, and ease of adoption.

6 Conclusion

The conclusion reviews hardware and software communication advances for UAVs, including antennas, signal management, network architectures, algorithms, and security methods. It emphasizes that safe, reliable, low-latency, and long-duration operation remains constrained by power and latency challenges.

  • 6 Conclusion: The survey covers UAV communication hardware and software, including antenna arrays, signal management, and centralized and decentralized techniques.It also discusses FANET, NDN, AFW, and DTN for synchronization and latency minimization.
  • 6 Conclusion: QDTD-based routing and eCLSC-TKEM are presented as initial steps toward secure and reliable communication between drones and other entities.These methods address routing and security within the surveyed communication landscape.
  • 6 Conclusion: Current security and optimization solutions remain inadequate for substantially increasing UAV flying time because of power-consumption and latency constraints.The survey reviews ACODS, input/output-device power optimization, and battery-life analysis.
  • 6 Conclusion: Balancing communication, mechanical structure, and optimization algorithms is identified as necessary for safe, reliable, powerful drones with long flying times and minimal communication latency.The conclusion links these components to overall UAV network infrastructure.
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