Unmanned Ariel Vehicles (UAVs) are flying objects whose trajectory can be remotely controlled. UAVs have lot of potential applications in the areas of wireless communications, internet of things, security, traffic management, monitoring, and smart surveying. By enabling reliable communication between UAVs and ground nodes, emergency notifications can be efficiently and quickly disseminated to a wider area. UAVs can gather data from remote areas, industrial units, and emergency scenarios without human involvement. UAVs can support ubiquitous connectivity, green communications, and intelligent wireless resource management. To efficiently use UAVs for all these applications, important challenges need to be investigated. In this paper, we first present a detailed classification of UAVs based on factors such as their size, communication range, weight, and flight altitude. We also explain the hardware system configuration and uses of these UAVs. We present a brief overview of recent work done related to three major challenges in UAVs. These challenges include trajectory control, energy efficiency and resource allocation. We also present three open challenges and future opportunities for efficient UAV communications. These include use of learning algorithms for resource allocation and energy efficiency in UAVs, intelligent surfaces-based communications for enhanced reliability in UAVs, and security algorithms to combat malicious attacks against UAVs.
Unmanned Ariel Vehicles (UAVs) are objects that can fly around providing communications and logistic support for many applications. Key areas in which UAVs can benefit include efficient crop monitoring, delivery of goods, intelligent monitoring of places for security, carrying out surveys of various locations, developing a real time map, etc. [
UAVs can also boost wireless communication performance by acting as reliable access and relay nodes. In rural areas where there is no Internet connectivity, UAVs can serve as Internet access provider. Similarly, for emergency communications, UAVs can play a vital role by taking the emergency messages and spreading the notification to other wireless nodes and control centers [
There are many important future applications where UAVs will be an integral part. UAVs will be deployed in Intelligent Transport Systems (ITS) for improving the city traffic management. In the area of agriculture, efficient spray of pesticides on crops can be performed using UAVs. Moreover, UAVs can also help in improving the security systems for military applications. Finally, emergency scenarios can greatly benefit from UAVs by effective communication between emergency nodes and helper nodes [
To effectively deploy UAVs for emergency communication scenarios, many important challenges need to be resolved. For example, UAVs are battery operated devices and energy efficiency will be an important part. Algorithms and protocols are needed to improve the overall lifetime of UAVs without requiring them to be recharged. Also, there will be many UAVs placed in an area and energy efficient load balancing is a key area that will impact the overall UAV efficiency.
Another area which requires research attention is trajectory control of UAVs. As UAVs are movable devices, their trajectory needs to be controlled in a manner that can improve the communication performance. The coverage range of UAVs needs to be enhanced and interference among UAVs needs to be reduced. Also, the UAVs need to reach the emergency area using shortest route in a quick time. Energy is also directly related to trajectory control. An optimal trajectory control algorithm will improve the energy efficiency of the system.
Resource allocation is another vital area of concern. Communication resources such as spectrum, transmission power, data rate and modulation need to be carefully selected to take the maximum benefit from UAVs. In this context, inter-UAV and UAV to other wireless nodes (vehicles and IoT nodes) cooperation will be needed to achieve maximum sum rate in the wireless system. Efficient resource allocation in turn improves the UAV energy efficiency by improving data transmissions, reducing number of collisions, and reducing number of retransmissions.
In this paper we focus on the three main challenges of UAVs for efficiency communications. These include trajectory control, energy efficiency and resource allocation. We present an overview of UAVs and UAV based communications. We provide a brief survey of recent work done to overcome these three challenges in UAVs. Finally, we present some of the open research challenges and future opportunities in the area of UAV based communications.
In this section, we first discuss UAVs and their working. We follow it up with a discussion on UAV based communications.
An unmanned aerial vehicle (UAV) (or Remotely piloted aircraft, commonly known as a drone) is an aircraft without a human control on board. It has configurable elements including a UAV, a ground-based control system, and communications system. The flight of UAVs may operate with various degrees of independence: either under remote control by a human controller or using on-board computers.
Various organizations have proposed formation of reference norms for the use of UAVs worldwide. The European Organization of UAVs (EUROUVS) has made up a group of UAV systems based on certain parameters [
Micro and Mini UAVs have a maximum takeoff weight of 0.1 and < 30 Kg respectively. The maximum flight altitude remains below 300 m. The endurance of these UAVs is less than 2 h. These UAVs operate at a communication range of up to 10 Km. We also show the hardware system configuration and uses of Micro and Mini UAVs in
Tactical UAVs can support a take off weight of around 150–1500 kg. The maximum flight altitude by these UAVs range up to 8000 m. The endurance for tactical UAVs varies from 2–48 h. Close Range (CR) tactical UAVs have an endurance of around 4 h whereas Medium Altitude Long Range (MALE) UAVs have an endurance of up to 48 h. Similarly, the communication range for CR UAVs is 30 m and for MALE UAVs is around 500 m. The hardware system configuration and uses of tactical UAVs is shown in
Strategic UAVs can support highest takeoff weight of up to 12,500 Kg. The maximum flight altitude is 20,000 m. The endurance is up to 48 h. The strategic UAVs such as High Altitude Long Endurance (HALE) also has longest communication range of greater than 2000 Km. The hardware uses in strategic UAVs include Global Hawk, Raptor, Condor and many others as shown in
Special Task UAVs can support a take off weight of 250 Kg. The maximum flight altitude supported is around 30,000 Kg by the Exo-stratospheric (EXO) UAVs. The endurance of these UAVs ranges from 3–48 h. The communication range for these UAVs can go up to 2000 Km. These UAVs use hardware of type MALI, Harpy, Lark, and Marula as shown in
There are several survey papers related to UAV communications [
In this section, we review recent trends in UAV communications. Particularly, we present the recent work done in three areas of trajectory control, energy efficiency and resource allocation.
As UAVs are mobile devices, optimal trajectory control is a key challenge which impacts connectivity with the ground base station as well as resource allocation. We show the recent work done in this area in
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Cellular UAVs and ground base stationsUAV trajectory control during mission such that connectivity remains intact and time to destination is minimizedUsed convex optimization and graph theory techniques | Reduced mission completion timeImproved SNR during mission |
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Ant colony-based optimization algorithm for initial trajectory generationCollision avoidance scheme for trajectory correctionInscribed circle scheme for trajectory smoothness | Reduced path lengthSmoother trajectory |
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Objective is to maximize sum rate in the presence of unknown transmitter and channel parametersProblem formulation is Markov Decision Process (MDP)Solution using model free reinforcement learning | Maximum sum rateConvergence of algorithm within a small training time |
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UAVs used for sensing data and disseminationTrajectory design to minimize the age of informationDeep Reinforcement learning based compound-action actor-critic scheme to learn optimal trajectory actions | Reduced age of information |
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Coordination among UAVs using sense and send protocolDecentralized Q learning algorithm for trajectory selection | Lower convergence timeEfficient sensor data transmissions |
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Energy efficiency is an important challenge for UAVs as they are battery operated devices, and it is difficult to recharge them periodically. We present recent work done in this area in
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Total energy consumption is minimized keeping connectivity intactUAV trajectory is designed using heuristic algorithm and dynamic programmingConnectivity requirement is formulated as traveling salesman problem | Reduced outage probabilityReduced energy consumption |
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UAV altitude from ground assumed to be fixedOptimal trajectory, transmit power and speed control and UAV-user scheduling algorithmUsed successive convex optimization and Dinkelbach technique | Higher throughputReduced energy consumption |
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UAVs act as relay to facilitate communications between ground nodesUAV trajectory, transmit power and speed control for efficient relayingSolution using sub-optimal iterative algorithm | Higher energy efficiency |
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UAVs used to collect sensed data from many sensorsSub-optimal routing of UAVs such that sensor’s residual energy is maximizedFeasible set of UAV locations using Voronoi diagram | Improved convergence time as compared to exhaustive searchImproved energy efficiency but lower than exhaustive search |
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UAVs provide connectivity to the ground base stationsTrajectory control and transmission scheduling of UAVs such that energy is recharged on time and fairness is achievedUse deep deterministic policy gradient approach | Improved energy efficiencyImproved throughputImproved fairness |
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Resource allocation is an important challenge that enables efficiency use of UAV for many applications. In this sub-section, we present recent work in the area of efficient resource allocation for UAVs. The recent works and their key ideas are shown in
Reference | Key idea | Results |
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UAV based mobile edge computing systemUAV acts as edge nodeEfficient offloading of tasks from ground users to edge node using UAV trajectory controlPenalty dual decomposition algorithm to solve the optimization problem | Reduced task computation delay |
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Joint resource allocation and access selection technique for UAVs to assist base stationsStackelberg game theory-based modelAccess selection uses dynamic evolutionary game and resource allocation uses non-cooperative gamePayoff based on ergodic rate performance | Fast equilibrium achievementImproved bandwidth allocation |
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Resource allocation among multiple UAVsSelect best ground user node for communication, transmit power and sub-channelGoal is to maximize the long-term reward function using multi-agent reinforcement learning algorithm and no information exchange among UAVs | Low complexityAcceptable performance in terms of reward function with less information exchange |
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Goal is to maximize the sum-rate using efficient resource allocation and trajectory control for solar powered UAVsOnline monotonic optimization algorithm that provides optimal trajectory, transmit power and sub-carrier allocationSub-optimal iterative optimization algorithm to reduce complexity | Improved system throughput |
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Considers UAV-assisted small cell scenario with heterogenous users each having different QoS requirementObjective is to maximize the defined utility which allows UAV to serve maximum number of users with minimum energy consumptionIteratively evaluates the number of users that can be served within the allowable range | Higher number of users served within the available energy budget |
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In this section, we highlight the future challenges and opportunities related to UAV communications. We discuss three important possibilities for future work, namely, learning algorithms, intelligent surfaces and security.
Machine learning algorithms have been recently used for solving challenges related to UAVs such as resource allocation and energy efficiency. As the UAVs have freedom to operate and perform tasks from remote locations or locate computers inside the UAVs, data collection is an important challenge for UAVs. With efficient and accurate data collection, machine learning algorithms can facilitate UAVs to accurate analysis, control, and predictions in wireless networks. As an example, random forest technique which is an ensemble learning technique, can be used for improving data classification. Federated learning can be used for developing accurate global machine learning models based on distributed local machine learning models (based on local UAV data). Similarly, deep learning models need to be investigated for quicker convergence time of machine learning algorithms in the context of UAVs.
Intelligent surfaces are another technology that can improve the signal propagation between transmitter and receiver. This can be achieved by placing meta-surfaces at different locations whose phase shift can be programmed remotely and can reflect the transmitted signals to improve reliability of transmission. UAV to ground station transmission can also take benefit from these intelligent surfaces. In this context, efficient use of intelligent surfaces, optimal phase shift design of meta-surfaces and resource allocation in this new system model needs to be investigated.
Security and privacy are key concerns when using UAVs to improve network connectivity and use them for IoT applications. UAVs are prone to attacks by malicious users that can disrupt the confidentiality, privacy, and data integrity. In this context, recent techniques such as blockchains and physical layer security requires thorough research and evaluation. In particular, future research must focus on level of security provided by these techniques for UAV communications, and quality of service and reliability tradeoff.
In this paper, we present a brief review of recent work done in the area of UAV communications. We discuss the working of UAVs, and important challenges for efficient UAV based communications. We review the recent trends in the three main areas, namely trajectory control, energy efficiency and resource allocation. We also discuss the future challenges and opportunities that can further improve UAV based communications.
We thank the deanship of scientific research at Umm Al-Qura University for providing funding for this research.