Mobile Relay Research Articles

Age-of-Information-Based URLLC-Enabled UAV Wireless Communications System

This article considers an unmanned aerial vehicles (UAVs) communication network, where UAV operate as an aerial mobile relay between a source and a destination of the network. To capture the “timeliness” of the received information at the destination node, a new performance metric named Age of Information (AoI) is considered. In addition, a short packet communication scheme maintains low latency in the proposed UAV wireless communication system. The finite block-length theory investigates the performances of the short packet communications scheme in the UAV-assisted wireless communications system. In this article, the Average AoI (AAoI) is estimated by applying the stochastic hybrid systems (SHSs) model. The SHS model comprises discrete states represents events that reset the AoI process and continuously dynamics that represent linearly growing age processes. Finally, a closed-form expression for the AAoI of the proposed UAV wireless communication system is derived and it can be used to estimate the optimal altitude, block length, and other parameters.

Deep Learning-Based Energy Optimization for Edge Device in UAV-Aided Communications

Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively.

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes.

Energy-efficient communication for UAV-enabled mobile relay networks

Relaying that can be classified as fixed or mobile is an effective technique to improve the throughput and reliability and also to extend the coverage in wireless networks. Compared with the conventional fixed relaying, mobile relaying has attracted great interest recently, because of the the rapid development of unmanned aerial vehicles (UAVs) having high mobility, low cost and deployment flexibility properties. Thus, in this study, mobile relaying technique in which the relay nodes are mounted on the fixed wing UAVs is considered. Due to the increasing energy consumption costs, environmental reasons, low lifetime of the mobile devices and limited on-board energy of the UAVs, energy-efficient communication is an important topic for the future wireless network design. Therefore, energy-efficient communication is examined for the UAV-enabled Decode-and-Forward (DF) mobile relay networks in this paper. Energy-efficiency (EE) maximization problem is formulated as the ratio of the total data rate to the total energy consumption by considering the quality of service (QoS) requirements of the users. As the formulated problem is mixed integer non-convex, it is hard to solve in this form. Thus, we decompose it into two subproblems as user-scheduling and power allocation and propose to solve each subproblem separately to overcome the complexity of the problem. The efficiency of the proposed algorithm is shown by performing extensive simulations for various system parameters.

Deep Learning-Assisted Secure UAV-Relaying Networks With Channel Uncertainties

Unmanned aerial vehicles (UAVs) play a critical role for 5 G/B5G communications. In this paper, we investigate the physical layer security issue for ground transmissions with the help of a UAV. Specifically, we consider the scenario without direct transmissions and employ the UAV as a mobile relay to amplify and forward the confidential information. Meanwhile, the receiver transmits cooperative jamming attempting to disrupt eavesdropping. We investigate the UAV deployment and receiver jamming strategy jointly for the optimized secrecy rate, while considering the channel uncertainties. In particular, for the case of complete channel state information (CSI), the jamming power is obtained by bi-section search, and we propose a data-trained deep neural network (DNN) to approximate the optimal UAV deployment. Meanwhile, for the case with partial CSI, we design a deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> network (DQN) to learn the optimal UAV deployment, due to the increased complexity to determine the jamming strategy with channel statistics. Simulation results demonstrate that both the learned DQN and trained DNN model effectively approximate the optimal deployment of the UAV. Moreover, compared with the baselines, our proposal significantly improves the security performance of legitimate transmissions.

Investigating the effect of High Altitude Platform Positioning on Latency and Coverage of 4G Cellular Systems

Wireless communication technologies are rapidly being adopted and developed by countries all over the world as a strategy for sustaining a digital economy. This has proven very useful for economic recovery from the crises brought about by the COVID-19 pandemic of the year 2020. The latency and coverage area of a wireless network are two major areas that are always seeking improvement. The High Altitude Platform communication technology can provide improvement in speed and coverage area for 4G cellular systems. This work investigated the effect of positioning High Altitude Platforms on the latency and coverage of 4G cellular Systems. A quantitative approach was used in the methodology of this paper. A HAP model showing a single platform flying in a circular trajectory over Base Transceiver Stations BTSs and serving as a relay mobile station was presented. A detailed simulation algorithm for the HAP and results for the simulation were given. Results showed that using the HAP as a relay mobile station in a network can give a latency reduction of up to 58.9%. Also, the altitude of the HAP directly affects the angle of reception which was found to improve the coverage.

Mobile Two-Way Molecular Communication via Diffusion Using Amplify-and-Forward and Analog Network Coding.

Mobile molecular communication via diffusion (MCvD) has attracted lots of attentions due to its time-varying channels. In this paper, we investigate a mobile two-way MCvD model, which consists of two mobile source nanomachines and a mobile relay nanomachine. The amplify-and-forward (AF) relaying and analog network coding (ANC) are utilized to implement the exchange of information between two source nanomachines in this model. To explore the performance of the mobile two-way MCvD system, we first adopt the depleted molecule shift keying (D-MoSK) modulation, and then the mathematical expressions of symbol error probability (SEP) and mutual information are derived by using AF and ANC scheme on the basis of maximum a posteriori (MAP) probability detection. Finally, we present the numerical and simulation results. Compared with the AF-No-ANC scheme which is without use of ANC scheme, the scheme of combining AF and ANC can significantly improve the performance of SEP and mutual information. Moreover, the D-MoSK modulation outperforms the molecule shift keying (MoSK) modulation for this mobile two-way MCvD system. In addition, we propose the evaluation and discussion about the impacts of several important parameters on the performance of this system. These results can be used to design mobile two-way MCvD system with lower SEP and higher mutual information.

An Efficient Viterbi Algorithm for Communication System

The Viterbi decoding algorithm is widely used in areas like Decoding convolutional codes in satellite communication, digital TV, wireless local area networks, mobile relay. Also, the method is used in the development of Automatic Speech Recognition (ASR) and storage systems that work automatically. For Viterbi decoder-based architectures with low latency and complexity, which proposes error detection techniques that are effective. This paper explores the Viterbi algorithm which has two types of approaches for two types of subparts. Important aspects of any communication system are area/power consumption and throughput /efficiency. Minimization of these aspects is the need for an efficient system. This paper explores unwanted logical block reduction by modifying the present logical block. This paper explores signature-based approaches which result in acceptable efficiency. Also, another approach is used to achieve error detection in permanent and transient faults. This error detection is achieved by recomputing with encoded operands. Encoding means the use of shifting operation or the use of rotation operation. This approach makes the system slightly efficient. The proposed approaches can be based on reliability and the efficiency objective.

Sum-rate maximization for UAV-enabled two-way relay systems

In this paper, an Unmanned Aerial Vehicle (UAV)-enabled two-way relay system with Physical-layer Network Coding (PNC) protocol is considered. A rotary-wing UAV is applied as a mobile relay to assist two ground terminals for information interaction. Our goal is to maximize the sum-rate of the two-way relay system subject to mobility constraints, propulsion power consumption constraints, and transmit power constraints. The formulated problem is not easy to solve directly because it is a mixed integer non-convex optimization problem. Therefore, we decompose it into three sub-problems, and use the mutation arithmetic of the Genetic Algorithm (GA) and Successive Convex Approximation (SCA) to dispose. Besides, a high-efficiency iterative algorithm is proposed to obtain a locally optimal solution by jointly optimizing the time slot pairing, the transmit power allocation, and the UAV trajectory design. Numerical results demonstrate that the proposed design achieves significant gains over the benchmark designs.

Harvest-on-Sky: An AoI-Driven UAV-Assisted Wireless Communication System

The 5G cellular network provides a sufficient data rate to support the ever growing number of devices that will be connected to the Internet. To enable Internet of Things (IoT) applications in remote locations, it is very crucial to increase the coverage of the cellular networks. This article proposes a wireless communication system that exploits a full-duplex enabled unmanned aerial vehicle (FD-UAV) integrated into a communication network as a mobile relay to expand network coverage. With the deployment of IoT devices, a large volume of data is introduced into the network, causing a delay in data delivery due to scarcity of resources. Real-time information updates play an important role in modern IoT applications including connected vehicles in intelligent transportation systems, digital healthcare, real-time tracking, and so on. Decision making based on the latest fresh information is critically important for these applications. We quantify the freshness of data in IoT applications using a new performance metric, the age of information (AoI). This work proposes a new mathematical model to estimate the average AoI (AAoI) in the FD-UAV assisted cooperative wireless communication system. Here, sensors located in a transport infrastructure harvest energy from radio frequency (RF) signals transmitted by the FD-UAV via a wireless power transmission RF energy harvesting (EH) technique. Then sensors consume this energy to transmit their sensed real-time observations to the access point with the aid of FD-UAV. A closed-form expression for the AAoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for EH that minimizes the AAoI is identified together with the effect of self-interference experienced by the UAV.

Outage Probability Analysis for Two-Way Amplify-and-Forward Mobile Relay Assisted Device-to-Device Communication in Rayleigh Fading Channel

Device-to-Device (D2D) communication underlaying cellular network is potential to enhance the spectral efficiency, reduce the energy consumption and improve individual quality-of-service (QoS) experience. But intra-cell orthogonality is impaired due to spectral reusing and severe co-channel interference could be imposed between cellular and D2D links. Besides, the direct links between local communicating pairs may be unfavorable and suffer an unsatisfied QoS experience since the randomness of channel condition. So, we are focused on analyzing the outage probability of D2D links assisted by two-way relays on Rayleigh fading channel. Specifically, we consider an amplify-and-forward relay protocol at relay due to their limited processing ability, and devise power control at both D2D transmitter and relay to maintain the QoS requirement of prioritized cellular links. The simulation results verify the correctness of the outage probability analyses for D2D links, and indicate the performance gain when relays are introduced to facilitate D2D links.

Delay performance of priority-queue equipped UAV-based mobile relay networks: Exploring the impact of trajectories

UAV-based mobile relay communication systems have an important position in future wireless networks, such as Beyond 5G and 6G, which provide ubiquitous network services. Along this line, UAV-based single-hop relay systems have numerous applications in emergency situations, such as data ferry and assisting post-disaster areas. Although there have been numerous works on optimizing trajectories and other communication resources on such systems, there are few studies on delay performance at the granular packet level. In addition to having homogeneous traffic, such a system can be equipped with diverse traffic with different QoS requirements, owing to the emergence of various applications in today’s communication networks. Based on the traffic requirements, packets with high-priority traffic should be served ahead of those with low-priority ones. To this end, we aim to analytically study the average end-to-end packet delay and buffer overflow performance of such a system at the granular packet level. However, packet-level queuing analysis of heterogeneous traffic is not as straightforward as that of homogeneous traffic. While considering different traffic requirements, we establish a priority queuing model for the source and the UAV nodes based on the stochastic process. Then, on the basis of the established queuing model, the average end-to-end packet delay and buffer overflow probability of the system under different trajectories are studied. Through extensive numerical simulations, we verified the accuracy and effectiveness of the proposed analytical model. The results show that the average packet delay and packet drop probability of high-priority packets under all trajectories are significantly better compared to low-priority packets. UAV trajectory has a great impact on network performance, and an appropriate mobile trajectory that takes full use of channel capacity is superior to a static one.

Completed Tasks Number Maximization in UAV-Assisted Mobile Relay Communication System

Unmanned aerial vehicle (UAV) with high flexibility, ease of deployment, and low cost has been widely applied to our daily life. In this paper, an UAV-assisted mobile relay communication system is considered, in which one UAV and one access point (AP) jointly provide computing resources for dense user equipment (UE) on the ground. We aim to maximize the number of tasks completed on the UAV and AP by optimizing the local computing frequency, offloading ratio, offloading power, computing frequency of the AP, computing frequency of the UAV, relaying power of the UAV, and relaying ratio of the UAV. Because the formulated problem is a mixed-integer nonlinear programming (MINP) problem that is very difficult to solve directly, we convert it into the problem of minimizing the computing frequency allocated by the UAV and AP to each UE. Then block coordinate descent (BCD) method is adopted to solve the converted problem. An optimal partial offloading and relaying scheme (POARS) is proposed to obtain the maximal number of completed tasks. Finally, simulation results show that POARS has a better performance than other schemes.

Reliable auxiliary communication of UAV via relay cache optimization

With the continuous reduction of drone costs and the miniaturization of equipment, many new applications have emerged in the civil and commercial fields. The remote sensing technology of Unmanned Aerial Vehicle (UAV) is used to manage and partition farmland accurately. Compared with the traditional field grid sampling method, UAV remote sensing technology can break through operating conditions, continuously collect data at low altitudes, monitor the area more flexibly, and significantly reduce labor and safety costs. Since UAVs can only provide data transmission services to users through wireless backhaul links established with ground base stations, the need to access the network is easy to lead to the disclosure of user privacy. The capacity of wireless backhaul links is limited, limiting the transmission rate of drones and reducing the user’s quality of service. Therefore, we apply edge caching technology to the assisted relay communication network to study the impact of caching technology on the performance of mobile relay systems. In particular, we propose the horizontal position design method in the buffer auxiliary relay single-user system, and the 3D position design method in the buffer auxiliary relay multi-user system. Position system, which can be reached by the maximum system average and the optimal speed, is designed. Besides, with the help of objective function conversion and classic derivation analysis method, the semi-closed expression of the optimal position of the UAV is obtained, and the intersection of the mobile relay end velocity and the user end velocity is used as the initial value. Meanwhile, the solution formula is substituted after continuous iteration so that the best advantage of local velocity can be obtained. In addition, mobile relay system is deployed to establish a two-hop wireless link to achieve reliable communication between Base Station (BS) and users security trust. The simulation experiment proves that compared with other methods, the method proposed in this paper has considerable performance improvement in power convergence, speed, trajectory path loss, and energy cost, which can provide higher-quality communication services for users in the system and better support for the broad application of drones.

An energy efficient coverage guaranteed greedy algorithm for wireless sensor networks lifetime enhancement

One of the most significant difficulties in Wireless Sensor Networks (WSNs) is energy efficiency, as they rely on minuscule batteries that cannot be replaced or recharged. In battery-operated networks, energy must be used efficiently. Network lifetime is an important metric for battery-powered networks. There are several approaches to improve network lifetime, such as data aggregation, clustering, topology, scheduling, rate allocation, routing, and mobile relay. Therefore, in this paper, the authors present a method that aims to improve the lifetime of WSN nodes using a greedy algorithm. The proposed Greedy Algorithm method is used to extend the network lifetime by dividing the sensors into a number of disjoint groups while satisfying the coverage requirements. The proposed Greedy algorithm has improved the network lifetime compared to heuristic algorithms. The method was able to generate a larger number of disjoint groups.

Reinforcement Learning for Motion Policies in Mobile Relaying Networks

We consider joint beamforming and relay motion control in mobile relay beamforming networks, operating in a spatio-temporally varying channel environment. A time slotted approach is adopted, where in each slot, the relays implement optimal beamforming and estimate their optimal positions for the next slot. We place the problem of relay motion control in a sequential decision-making framework. We employ Reinforcement Learning (RL) to guide the relay motion, with the goal of maximizing the cumulative Signal-to-Interference+Noise Ratio (SINR) at the destination. First, we present a model based RL approach, which predictively estimates the SINR and accordingly determines the relay motion, based on partial knowledge of the channel model along with channel measurements at the current relay positions. Second, we propose a model-free deep Q-learning approach, which does not rely on channel models. For the deep Q-learning approach, we propose two modified Multilayer Perceptron Neural Networks (MLPs) for approximating the value function Q. The first modification applies a Fourier feature mapping of the state before passing it through the MLP. The second modification constitutes a different neural network architecture that uses sinusoids as activations between layers. Both modifications enable the MLP to better learn the high frequency value function and have a profound effect on convergence speed and SINR performance. Finally, we conduct a comparative analysis of all the presented approaches and provide insights on advantages and drawbacks.

Integration of Micro-Structured Photovoltaic Cells into the Ultra-Light Wing Structure for Extended Range Unmanned Aerial Vehicles

There have been large developments in the unmanned aerial vehicles (UAV) industry over the last decade. Although UAV development was mainly for military related use in the beginning and despite there being fear surrounding the release of this technology to the open market for quite a long time, nowadays, there are a variety of applications where UAVs are used extensively, such as in agriculture, infrastructure inspection and monitoring, mobile retranslation relays for communications, etc. One of the weaknesses of electrically propelled UAVs is flight autonomy; there is often a difficult trade-of between the weight of the payload, batteries, and surface to be surveyed that is necessary to determine. There have been many attempts to use photovoltaic cells to increase the flight time for UAVs; however, a reliable solution has not yet been developed. The present paper presents improvements that have been conducted to extend the autonomy of electrically derived UAVs: instead of gluing photovoltaic cells on the wings, the new approach embeds the solar cells into the wing structure as well as develops a new wing that is significantly lighter to compensate for the weight added by the photovoltaic cells. It was demonstrated that by using this approach, a 33% increase in the flight time can be achieved with only one modified wing in a prototype vehicle.

User Grouping and Energy Harvesting in UAV-NOMA System With AF/DF Relaying

The development of unmanned aerial vehicles (UAVs) is one of the driving forces for revolutionizing wireless communications in the modern era. Although there are many unique features of UAV networks, their widespread use is still hampered by the short battery life. As a result, most of the research efforts in this domain mainly have focused on the efficient use of energy through trajectory planning and cooperative communication. While there have been a few proposals to use energy harvesting techniques for UAV networks, a complete understanding of the performance limits of such networks is still missing in the literature and needs further investigations. Within this context, our paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique. Specifically, a cooperative communication system, where a UAV serves as a mobile relay, is fully considered. The UAV relay is considered to be wirelessly-powered and harvests energy from the radio signals received from a nearby base station. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are provided for each UAV relaying protocol. Extensive simulations are performed to verify the accuracy of the derived closed-form expressions. The results provided in this work characterize the outage and ergodic capacity performance of NOMA-enabled UAVRNs.

Drone Swarm as Mobile Relaying System: A Hybrid Optimization Approach

Drones are increasingly employed in several application domains thanks to their inherent versatility. This work envisions a scenario in which a swarm of Unmanned Aerial Vehicles (UAVs) enables the communication between a set of Sensor Nodes (SNs) and a control center. Considering a general fading channel model, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to maximize the overall amount of relayed data by jointly optimizing trajectory and scheduling plan of each drone. Combining convex optimization and Ant Colony Optimization (ACO) algorithm, a quasi-optimal solution is obtained. Finally, numerical results demonstrate the effectiveness of the proposed solution in different parameter configurations and with respect to a benchmark algorithm.

Latency Aware 3D Placement and User Association in Drone-Assisted Heterogeneous Networks With FSO-Based Backhaul

Drone Base Station (DBS) is expected to play an important role in next generation cellular networks due to its maneuverability in establishing air-to-ground Line of Sight (LoS) links. Working as a mobile relay node between the Macro Base Station (MBS) and the ground users, the DBS can significantly reduce the latency of ground users by offloading traffic loads from the MBS to the DBS since a better channel condition can be provided. In this paper, we focus on how to determine the 3D location of the DBS, the user association and the bandwidth allocation policy between the MBS and the DBS in order to minimize the total average latency ratio of all users with the constraint of each user's QoS requirement and total available bandwidth. The formulated problem is a mixed integer nonconvex optimization problem, a very challenging and difficult problem. We propose a cyclic iterative algorithm to efficiently solve it by decomposing the primal problem into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. In each iteration, the two sub-problems are alternatively optimized by using the output of one as the input for the other. Numerical simulation results demonstrate the significant latency ratio reduction of our proposed algorithm as compared to other baseline schemes.