UAV-assisted Communication Research Articles

Trajectory Design for Throughput Maximization in UAV-Assisted Communication System

In this paper, we study an unmanned aerial vehicle (UAV) assisted communication system to provide service to the ground users. We address the problem of UAV deployment in three-dimensional (3D) space to provide on-demand coverage to the users such that their sum rate is maximized. First, we find the optimal UAV location in 3D space where the sum rate is maximized for all ground users. Thereafter an optimal trajectory is designed for the UAV to travel from the initial to the optimal location, such that the overall average sum rate during the flight is maximized while meeting the on-board energy availability and flight duration constraints. The problem formulated is non-convex. To obtain the optimal location, we approximate the rate expression to obtain the concave regions and apply alternating optimization. An iterative scheme is proposed to obtain the optimal UAV trajectory, which computes the optimal location in each time slot sequentially, followed by a greedy approach to reach the final location. Simulation results provide useful insights into the optimal location and the UAV trajectory problem and show on an average 16.5% improvement over the benchmark schemes.

Performance Analysis of a SWIPT Enabled UAV-Assisted Relaying

In the recent era, unmanned aerial vehicle (UAV) plays an important role in numerous application fields related to the wireless communication system. Due to its precise control, efficient deployment, and affordable cost, UAV-assisted communication attracts significant attention to all the sectors including the defense sector, agriculture sector, and security purpose, and so on. Though UAVassisted relaying has enormous advantages but there are potential challenges while UAV deploys as a relay. For example, deploying UAV in the wireless communication field, its battery life is the main concern due to its limited battery size and storage capacity. To get significant benefits from UAV while deployed in the cooperative communication network, the battery status of the UAV is an unavoidable issue. To minimize the aforementioned problem, energy harvesting (EH) techniques can be an efficient solution. The UAV can harvest energy from the transmitted power by the source and with the help of this harvested energy UAV can retransmit the signal to the destination. However, there are several parameters that also significantly influence the UAV-based cooperative system performance such as UAV’s position, time allocation factor and power allocation factor, and UAV’s height. Considering the importance of the aforementioned parameters, in this paper, we have considered simultaneous wireless information and power transfer (SWIPT) enabled UAV-assisted relaying network and evaluate the system outage performance with different parameters aspects. We have provided some insight about the parameters such as the UAV’s position, the power allocation factor and the time allocation factor and the UAV’s height by providing simulation results such as the outage probability versus transmit power in the different urban scenario, the outage probability versus time allocation factor and power allocation factor and the outage probability versus UAV’s height. These simulation results clearly show the significance of the abovementioned parameters in wireless-powered UAVassisted cooperative communication. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

UAV Selection and Link Switching Policy for Hybrid Tethered UAV-Assisted Communication

Unmanned aerial vehicles (UAVs) have been a frontier of communication technology research for their unique advantages. Their mobility and flexibility enable their use in next-generation wireless networks. This letter explores tethered UAV-assisted hybrid cooperative communication to improve the end-to-end performance of the links between base stations and user equipment. We propose a simple, novel, channel state information (CSI)-free UAV selection policy and a hybrid PHY layer (RF or mmWave or FSO) link switching policy. We develop an insightful outage probability analysis of the proposed policy. The policy and its analysis serve as a useful analytical benchmark for UAV-assisted wireless systems.

A Survey on Unmanned Aerial Vehicles-Assisted Internet of Things: A Service-Oriented Classification

Unmanned Aerial Vehicles (UAVs) are well-developed technologies that were first utilized for military applications such as border monitoring and reconnaissance in hostile territories. With the advancement of the Internet of Things (IoT) systems and smart mobile devices, several applications in various industrial, agricultural, smart homes, smart cities, smart transportation, etc. domains have emerged. These applications usually require broad coverage, high energy consumption, computation-intensive processing, and access to rich data gathered by sensor devices. UAVs’ inherent features such as high dynamicity, low deployment and operational costs, quick deployment, and line of sight communication have motivated researchers in the IoT domain to consider UAVs integration into IoT systems toward the notion of UAV-assisted IoT systems. In this paper, recent literature on UAV-assisted services in IoT environments is studied. A service-oriented classification is applied in order to categorize the presented schemes into four broad domains of UAV-assisted data-related services, UAV-assisted battery charging, UAV-assisted communications, and UAV-assisted Mobile Edge Computing (MEC). The literature belonging to each category is summarized with respect to their main points. Finally, some possible future directions are discussed to highlight the challenges associated with designing UAV-assisted IoT systems.

A survey on unmanned aerial vehicle relaying networks

With the explosive growth of data communications, existing infrastructure networks are under ever-increasing pressure. Due to the advantages of fully controllable mobility, rapid deployment, and low cost, the unmanned aerial vehicles (UAVs) have attracted much attentions from both industry and academia in recent years, and it has become an inevitable trend to employ UAVs to enhance the network performance in different environments. As an important paradigm of UAV-assisted communications, UAV relaying communications has been regarded as a promising solution in enhancing connectivity and improving transmission rate. This paper for the first time comprehensively summarizes UAV relaying communications and its application scenarios, including single UAV relaying networks, multi-user UAV relaying networks, multi-hop UAV relaying networks, as well as Internet of UAVs, and deeply analyzes the key technologies and challenges to be solved under this topic. Furthermore, the state-of-the-art researches and opportunities of UAV relaying communications are discussed in detail.

3D Deployment of Multiple UAV-Mounted Base Stations for UAV Communications

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their high mobility and low cost. This article investigates a communication system assisted by multiple UAV-mounted base stations (BSs), aiming to minimize the number of required UAVs and to improve the coverage rate by optimizing the three-dimensional (3D) positions of UAVs, user clustering, and frequency band allocation. Compared with the existing works, the constraints of the required quality of service (QoS) and the service ability of each UAV are considered, which makes the problem more challenging. A three-step method is developed to solve the formulated mixed-integer programming problem. First, to ensure that each UAV can serve more number of users, the maximum service radius of UAVs is derived according to the required minimum power of the received signals for the users. Second, an algorithm based on artificial bee colony (ABC) algorithm is proposed to minimize the number of required UAVs. Third, the 3D position and the frequency band of each UAV are designed to increase the power of the target signals and to reduce the interference. Finally, simulation results are presented to demonstrate the superiority of the proposed solution for UAV-assisted communication systems.

UAV-Assisted Communication in Remote Disaster Areas Using Imitation Learning

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users’ information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.

Cache-Enabled Data Rate Maximization for Solar-Powered UAV Communication Systems

Currently, deploying fixed terrestrial infrastructures is not cost-effective in temporary circumstances, such as natural disasters, hotspots, and so on. Thus, we consider a system of caching-based UAV-assisted communications between multiple ground users (GUs) and a local station (LS). Specifically, a UAV is exploited to cache data from the LS and then serve GUs’ requests to handle the issue of unavailable or damaged links from the LS to the GUs. The UAV can harvest solar energy for its operation. We investigate joint cache scheduling and power allocation schemes by using the non-orthogonal multiple access (NOMA) technique to maximize the long-term downlink rate. Two scenarios for the network are taken into account. In the first, the harvested energy distribution of the GUs is assumed to be known, and we propose a partially observable Markov decision process framework such that the UAV can allocate optimal transmission power for each GU based on proper content caching over each flight period. In the second scenario where the UAV does not know the environment’s dynamics in advance, an actor-critic-based scheme is proposed to achieve a solution by learning with a dynamic environment. Afterwards, the simulation results verify the effectiveness of the proposed methods, compared to baseline approaches.

Spectrum on Demand: A Competitive Open Market Model for Spectrum Sharing for UAV-Assisted Communications

Unmanned aerial vehicle (UAV)-assisted communication has attracted significant interest from the industry, especially with regard to the vision of providing ubiquitous connectivity for beyond 5G (B5G) networks. in this article, we motivate the need for utilizing licensed spectrum for UAV-assisted communication and discuss its advantages such as reliability and security. We explore a new dimension to spectrum sharing by proposing a decentralized competitive open market approach-based model, where the different mobile network operators (MNOs) have the opportunity to lease the spectrum to UAV base stations (UAV-BSs), leading to new revenue generation opportunities. The proposed spectrum sharing mechanism is based on the logarithmic utility function and willingness to pay of each UAV-BS. We provide a trade-off analysis between spectrum sharing and price offered by the MNOs, highlighting the impact of the willingness to pay on the spectrum sharing. The results also highlight the behavior of price and spectrum shared w.r.t. time, thereby providing an insight into different performance regions until the algorithm converges to its optimal value. in addition, we also present future directions that could lead to interesting analyses, especially with regard to incentive- based spectrum sharing and security.

UAV-Assisted Wireless Energy and Data Transfer With Deep Reinforcement Learning

As a typical scenario in future generation communication network applications, UAV-assisted communication can perform autonomous data delivery for massive machine type communication (mMTC), where the data generated from Internet of Things (IoT) devices can be carried and delivered to the corresponding locations with no direct communication channels to the IoT devices. Wireless energy transfer technique can recharge the UAV when the system is in operation, assisting the UAV to continuously collect and deliver data. In this work, we formulate a Markov decision process (MDP) model to describe the energy and data transfer optimization problem for the UAV. To maximize the long-term utility of the UAV, the MDP model is solved by value iteration algorithm to obtain the optimal strategies of the UAV to collect data, deliver data, and receive transferred energy to replenish on-device battery energy storage. Furthermore, to tackle the issues of system state uncertainties, partially observable states, and large state space in UAV-assisted communication systems, we extend the MDP model and solve it by using a ${Q}$ -learning and a deep reinforcement learning (DRL) schemes. Simulations and numerical results validate that, compared with baseline schemes, the proposed MDP model with DRL based scheme can achieve better wireless energy and data transfer strategies in terms of the higher long-term utility of the UAV.

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

Employing Unmanned Aerial Vehicles for Improving Handoff Using Cooperative Game Theory

Heterogeneous wireless networks that are used for seamless mobility are expected to face prominent problems in future fifth generation (5G) cellular networks. Due to their proper flexibility and adaptable preparation, remote-controlled unmanned aerial vehicles (UAVs) could assist heterogeneous wireless communication. However, the key challenges of current UAV-assisted communications consist in having appropriate accessibility over wireless networks via mobile devices with an acceptable Quality of Service grounded on the users’ preferences. To this end, we propose a novel method based on cooperative game theory to select the best UAV during handover process and optimize handover among UAVs by decreasing the i) end-to-end delay, ii) handover latency, and iii) signaling overheads. Moreover, the standard design of software-defined network with media-independent handover is used as forwarding switches in order to obtain seamless mobility. Numerical results derived from the real data are provided to illustrate the effectiveness of the proposed approach in terms of number of handovers, cost, and delay.

Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network

Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation

Due to affordable price, high mobility and flexible maneuverability, Unmanned Aerial Vehicle (UAV)-assisted communication can play an important role in the deployment of the Internet of Things (IoT) in emergency. Since UAV network performance is highly dependent on UAV deployment location, trajectory design becomes the research hotspot in UAV-assisted communication. To this end, a UAV-assisted multicarrier wireless powered communication model is proposed for IoT scenarios in this paper. Specifically, as an aerial base station, the UAV transmits Orthogonal Frequency Division Multiplexing (OFDM) signals to IoT nodes, while IoT nodes decode information and harvest energy from the signals. Afterwards, IoT nodes transmit information to the UAV by using the harvested energy. A joint UAV trajectory optimization and resource allocation scheme based on OFDM is proposed. The aim is to maximize the minimum achievable rate in the uplink among all IoT nodes by jointly optimizing UAV trajectory, subcarrier, power and subslot allocation, subject to the achievable sum rate of all IoT nodes in the downlink. Due to the non-convexity and complexity of the formulated optimization problem, an alternative iteration algorithm is proposed to deal with the problem. Simulation results that the proposed algorithm can optimize the UAV trajectory and adapt to the node movement. Compared with conventional resource allocation schemes, the proposed scheme not only significantly enhances the minimum achievable rate, but also works well for two flight modes.

Uplink NOMA Random Access for UAV-Assisted Communications

This correspondence paper investigates uplink non-orthogonal multiple access (NOMA) for unmanned aerial vehicle base stations (UAV-BSs). Users under the service area of a UAV-BS perform NOMA random access (RA) by controlling their transmit power so that the UAV-BS can receive one of two predefined power levels. This facilitates successive interference cancellation (SIC) at the UAV-BS. This correspondence paper shows the maximum stable throughput of uplink NOMA RA in terms of the altitude and beamwidth of the UAV-BS. Further, we propose a stabilizing algorithm, with which a UAV-BS broadcasts the throughput-maximal retransmission probability every slot, while users access the channel based on the retransmission probability. Extensive simulations show that the proposed algorithm can achieve the maximum throughput 0.5869 (packets/slot) and stabilize the system with time-varying traffic.

UAV Positioning for Throughput Maximization Using Deep Learning Approaches.

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively.

Stochastic Geometric Analysis of Multiple Unmanned Aerial Vehicle-Assisted Communications Over Internet of Things

Due to the advantages of large area coverage, low capital cost and fast deployment, unmanned aerial vehicles (UAVs) are believed to play a key role in the emerging Internet of Things (IoT). In this paper, we first develop an effective analytical approach to characterize the properties of UAV-assisted communications over a large number of IoT devices by introducing average channel access delay for packets that can be successfully transmitted. Specifically, an IoT device is said to establish a full transmission to a UAV, only if its time duration covered by the UAV is greater than the specified average channel access delay. Then, we present a stochastic geometry based mathematical framework to analyze the coverage probability and average achievable rate for a multi-UAV assisted downlink network. Different from previous works: 1) we consider a flexible multi-UAV deployment strategy connecting IoT devices to the Internet via sky-haul links to the satellite, where the altitudes of the UAVs can be adjusted to fulfill the requirements of various IoT applications and 2) we derive analytical expressions, in particular integral form, for the coverage probability and average achievable rate. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum IoT system performances can be achieved.

On Ergodic Capacity and Optimal Number of Tiers in UAV-Assisted Communication Systems

In this paper, we consider unmanned aerial vehicle (UAV) assisted communication systems, where a number of UAVs are utilized as multi-tier relays between a number of users and a base-transceiver station (BTS). We model the wireless propagation channel between the users and the BTS as a Rayleigh product channel, which is a product of a series of independent and identically distributed (i.i.d.) Rayleigh multi-input multi-output channels. We put a special interest in optimizing the number of tiers in such UAV-assisted systems for a given total number of UAVs through maximizing the ergodic capacity. To achieve this goal, in the first part, we derive a lower-bound in a closed form for the ergodic capacity, which is shown to be asymptotically tight. In the second part, we analyze the optimal number of UAV-tiers with the derived bound, and propose a low-complexity solution that significantly reduces the search-size and yields near-optimal performance. Further, asymptotic properties both for the ergodic capacity and the solution for optimal number of tiers are extensively analyzed.

A Survey of Game Theory in Unmanned Aerial Vehicles Communications

Unmanned aerial vehicles (UAVs) can be deployed as wireless relays or aerial base stations to improve network connectivity and coverage in cellular networks. UAVs can also be used to significantly enhance the performance of mobile ad-hoc networks and wireless sensor networks. In the future, UAVs are expected to become an integral part of the fifth generation wireless networks as well as key enablers of the coming massive Internet of Things. However, there are still many challenging issues in designing architectures and deployment of UAV-based networks. To address the issues, game theory has recently been adopted as an effective tool for modeling and analyzing problems in UAV-aided networks. In this paper, we survey the applications of game theory in solving various UAV-assisted networks challenges. We first provide a brief introduction to wireless communications with UAVs and then introduce basic game theory concepts and their relation to wireless networks. We further present the classification and brief introduction to the games applied to solve problems in UAV-aided networks. We then provide a comprehensive literature review on game-theoretic techniques utilized in dealing with challenges in the UAV-based wireless networks. Finally, we introduce advanced distributed schemes for interference management in large UAV-assisted communication networks. This paper aims to provide readers with an understanding of UAV-aided networks in terms of their architecture, benefits, challenges, and various game theoretical solutions applied to these communications networks.