A cost-efficient elastic UAV relay network construction method with guaranteed QoS

Due to the great potential of the combination of machine learning technology and unmanned aerial vehicle (UAV) enabled wireless communications, various optimization algorithms on resource allocation have been proposed for the Internet of Things. UAVs not only can perform the missions under the extreme conditions but also enhance the overall performance of the system as an aerial relay assisting transmission in the public and civil domains, which have been received extensive attentions. However, with the limited capacity and power constraints, they are difficult to support the transmission for the big data information users. In addition, the lack of spectrum resource poses challenges to satisfy the quality of service (QoS) of mobile users in wireless networks. To contribute to these urgent problems, this article first studies the potential and effective applications of UAVs, by introducing the Chinese remainder theorem (CRT) and nonorthogonal multiple access (NOMA) technologies into UAV relay networks. Two scenarios with/without direct transmissions between the source and destination nodes are investigated, following the decomposition and reconstruction mechanisms to satisfy the big data information transmission. Considering the user fairness, we further discuss the effect of the UAV numbers to the overall system capacity. To maximize the system capacity, the designs of transmission protocol and receiver are also discussed, in various channel conditions. Finally, a low complexity and efficient two‐stage power allocation scheme is established for the perspective of users and UAV relays.

As the common means of field emergency communication, the unmanned aerial vehicle (UAV) relay plays an important role in both civil and military fields. With the diversification of requirements and the limitations of single operation, multi-UAV cooperative work has attracted extensive attention. However, due to the UAV signal’s universality of broadcast and the limitation of power, information transmission is more likely to be interrupted. Therefore, in the presence of interference, this paper adopts forward amplification protocol under UAV relay transmission model. And two beamforming methods are proposed based on minimizing transmission power and maximizing communication rate of UAV relays. Then, we establish the power-constrained maximum security rate optimization problem with UAV relays, and deduce the corresponding beamforming weight vector and the optimal rate of safety. Finally, the simulation results show the effectiveness and accuracy of the proposed beamforming method.

The use of multiple heterogeneous, low-cost, small Unmanned Aerial Vehicles (UAVs) as a tool in several application domains is becoming increasingly important. One critical aspect to enable the use of such vehicles is the coordination/planning system, whose task complexity increases with the number of vehicles and the communications constraints that arise due to their small size and large distances. In this work, we propose a control architecture for a platoon of relay UAVs that are independent of the coordination system. The platoon task consists in interconnecting the communication link between the possibly mobile command station and a UAV in a mission. The relays are actively driven to deploy, create a network and maintain a desired Quality-of-Service (QoS) level, defined in this paper. We present an architecture that is composed by a waypoint generator based on the network QoS and a Time Coordinated Path Following (TCPF) controller with a method to reduce the frequency of information exchange between the relay UAVs, through the use of a self-triggered control strategy. Exploiting this architecture, it is possible to plan a mission operation for a UAV without the need of considering vehicle-to-command-station communication constraints that will be satisfied by the introduction of the relay-UAVs platoon. Simulation results are provided to illustrate the efficacy of the developed strategy. The self-triggered approach results in significant reduction of information exchange between the relay UAVs, while maintaining the user desired network QoS.

The use of small and miniature unmanned aerial vehicles (UAVs) for remote sensing and surveillance applications has become increasingly popular in the last two decades. The intermittent connectivity in a sparse UAV network makes it challenging to efficiently gather sensed image data. This paper investigates the communication of sensed images from a set of mobile survey UAVs to a static base station through the assistance of a relay UAV. Given the planned routes of survey UAVs, a set of relay waypoints are found for the relay UAV to meet the survey UAVs and receive the sensed images. An Online Message Relaying technique (OMR) is proposed to schedule the relay UAV to collect images. Without any global collaboration between the relay UAV and the survey UAVs, OMR utilizes a markov decision process (MDP) that determines the best schedules for the relay UAV such that the image acquisition rate could be maximized. Evaluation results show that the proposed relaying technique outperforms traditional relaying techniques, such as the traveling salesman problem (TSP) and the random walk, in terms of end-to-end delay and frame delivery ratio.

Path-optimization method for UAV-aided relay broadcast communication system

Due to the high mobility of on-board units (OBUs) and the large-scale network topology frequency hopping-based anti-jamming techniques are not applicable in vehicular ad hoc networks (VANETs). In this paper, to improve the communication performance of VANETs against smart jamming, unmanned aerial vehicles (UAVs) are used. These unmanned aerial vehicles observe the ongoing OBU and UAV communication status and induce the UAV to use a specific relay strategy. The UAV relays the OBU message to another roadside unit (RSU) with a better radio transmission condition if the serving RSU is heavily jammed or interfered. The interactions between a UAV and a smart jammer are formulated using an anti-jamming UAV relay game. The UAV decides whether or not to relay the OBU message to another RSU based on the relay game, the jammer observes the UAV and the VANET strategy it chooses the jamming power accordingly. The proposed relay strategy can reduce the bit-error rate of the OBU message efficiently and increasing the benefit of the VANET Using Q-learning analysis.

This paper investigated the multiple unmanned aerial vehicle (UAV) relays' assisted network in the Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large-scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which was also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused by dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed-form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered from aggregated interference were derived in detail. In addition, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor, and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. The simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse environments for applications like disaster detection and mine exploration. To process the massive volume of sensing data generated by various sensory payloads in these applications, existing works either compress deep learning (DL) models to conduct onboard computing, or offload raw data back to the resourceful ground station with the help of relay UAVs due to base station damage. However, the former sacrifices the inference accuracy of DL models (up to 10% accuracy loss), while the latter achieves high accuracy at the cost of significant latency, due to limited wireless communication resources in the multi-hop transmission. To address the problem, exploiting the resources of the UAV swarm including both task UAVs and relay UAVs, we build up an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>ll-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ky <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>utonomous com<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u>uting (ASAP) system to autonomously conduct collaborative computing in the swarm, to achieve both high accuracy and low latency of sensing data processing. In detail, we first propose a novel UAV swarm-native collaborative computing architecture, considering the general hierarchy and clustering structure of UAV swarms, as well as the characteristic of DL model execution. We then design an elastic efficient task scheduler to allocate computing tasks for UAVs, and update the scheduling scheme online when some UAVs are unavailable, with the aid of a lightweight and accurate DL inference performance predictor. Finally, we design an adaptive inter-UAV data compressor, to adapt to the limited and dynamic communication resources between UAVs. Experiment results on 24 airborne computers and five real-world UAVs show that, the proposed system can perform collaborative computing in a timely manner and effectively deal with situations when some UAVs become unavailable.

In this paper, we consider a hybrid satellite-terrestrial network (HSTN) where a multiantenna satellite communicates with a ground user equipment (UE) with the help of multiple cache-enabled amplify-and-forward (AF) three-dimensional ($3$D) mobile unmanned aerial vehicle (UAV) relays. Herein, we employ the two fundamental most popular content (MPC) and uniform content (UC) caching schemes for two types of mobile UAV relays, namely fully $3$D and fixed height. Taking into account the multiantenna satellite links and the random $3$D distances between UAV relays and UE, we analyze the outage probability (OP) of considered system with MPC and UC caching schemes. We further carry out the corresponding asymptotic OP analysis to present the insights on achievable performance gains of two schemes for both types of $3$D mobile UAV relaying. Specifically, we show the following: (a) MPC caching dominates the UC and no caching schemes; (b) fully $3$D mobile UAV relaying outperforms its fixed height counterpart. We finally corroborate the theoretic analysis by simulations.

Unmanned aerial vehicle (UAV) is an aircraft without a human pilot onboard and a sort of unmanned vehicle. UAV Network research activities in the real world can pose several challenges, especially in urban environments or in densely populated environments. This is due to the nature of the UAV which requires a large area of operation. Therefore, digital simulation is vital in conducting research related to UAV and its networks. One of the UAV simulators recently developed is FlyNetSim. FlyNetSim is an open-source UAV Network Simulator based on NS-3 and Ardupilot. In the application, UAV can have some network-related problems. One of them is when a UAV is in an environment that has a lot of interfering networks because it is contending a network channel. This can cause network delay. These delays will raise with the increase in the number of nodes that interfere with the UAV. To overcome the problem of interfering nodes, we propose to add a priority set module for UAV and Ground Control Station (GCS). The proposed module will set priority for the UAV and GCS socket. This module is intended to give priority to packets sent by UAVs or GCS. Later the packet that has priority will be sent first to reduce the delay caused by the appearance of interfering nodes. Setting priority in the socket may reduce the average delay caused by contending nodes. Test result shows that there is a 16.2% reduction of average delay. Test result also shows that there is a 4% raise in packet delivery ratio.

Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone.

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

Autonomous formation flight has been a fast growing research area during the last decade. Unmanned aerial vehicles (UAVs) can achieve a wide range of benefits from formation flight, such as fuel saving and aerial refueling. In this paper, the development of a small UAV system, called Swallow UAV system, as testbed of formation flight, is presented. Swallow UAV system includes Swallow UAV itself and a Hardware-In-the-Loop Simulation Environment (HILSE). Swallow UAV is a fixed wing, pusher-type, electric motor driven aircraft with 1.4 meter wing span. Its avionics system includes an onboard computer and a sensor system with GPS, IMU, air data sensor, and motor monitoring unit to provide position, velocity, inertial data, and system health. Communications between UAVs and the ground control station (GCS) are conducted by wireless modem. A fuzzy navigation and control system (FNCS) is developed for the waypoint navigation and attitude control of the aircraft. GPS velocity based pseudo attitude and fuzzy logic control are applied to FNCS for attitude estimation and fast implementation of controllers. HILSE is built to verify FNCS with onboard computer on the ground. X-Plane flight simulator is applied to simulate aircraft dynamics and output sensor data. GCS software, built with data distributor, transmits these data to the onboard computer and receive control signal from it so the onboard computer can control the aircraft in X-Plane. HILSE can perform pre-flight test for software debug, which improves safety and efficiency of flight test. The design of Swallow UAV is verified by flight test and the result shows that it has sufficient flight performance with waypoint navigation and attitude control capability to be used as a formation flight testbed.

By relaying messages between users, unmanned aerial vehicles (UAVs), which are frequently utilized in the communication industry, can be used as airborne communication relays to resolve connectivity problems. In order to effectively connect a large number of heterogeneous users dispersed on the ground, we deploy a large number of UAVs for reliable relay coverage and message transmission between users. We initially put forth a relay coverage algorithm that continuously optimizes the position of UAVs and the cell division of terrestrial users in order to provide users with full coverage. Second, we study several relay message forwarding methods between UAVs while taking into account the features of heterogeneous users. A relay selection method, which takes the relay link throughput as the optimization goal, is proposed to ensure full connectivity between UAVs. Finally, we contrast and analyze how various relay forwarding methods affect the effectiveness of the relay network. The experimental results demonstrate that the proposed relay coverage algorithm can balance the UAV relay load better and reduce transmission delay, while the suggested relay selection strategy can minimize the number of isolated UAVs and maximize the throughput of UAV relay links.

The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time.