Mobile Relay Research Articles

Intelligent joint communication and computation scheme of UAV-assisted Offloading in high speed rail scenarios

With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

High-Speed Rail Multiple-Input–Multiple-Output Channel Model Based on Reconfigurable Intelligent Surface System

This paper presents a geometry-based stochastic model (GBSM) for a reconfigurable intelligent surface (RIS)-assisted multiple-input–multiple-output (MIMO) system tailored to high-speed rail environments, addressing energy leakage at space lattice points caused by the time-dependent reception direction and differing antenna array resolutions. Initially, this study explores the spatial domain feature map and spreading antenna lobe characteristics from various RIS array configurations to reshape the polarization distribution and facilitate a virtual antenna array. Subsequently, the time-dependent reception direction and position are derived by integrating the dynamics of train operations. Finally, the received signal is aligned with the polarization direction to assess the signal reception efficiency and finalize the model output. Research findings indicate that the number of RIS elements, spacing between RIS elements, and mobile relay (MR) movement characteristics significantly influence the performance of the system. Compared to existing models, the proposed model proficiently captures the effects of time-dependent receiver angle properties and RIS array configuration.

A UAV communication strategy based on user distribution time series prediction

Abstract To maximize the role of unmanned aerial vehicles (UAVs) as mobile relays to assist communication, while meeting the requirements of service quality and energy constraints, it is necessary to fully consider the motion trajectories and spatiotemporal distribution of ground users in UAV wireless communication systems. Therefore, an algorithm that combines Long Short-Term Memory (LSTM) and Gaussian Mixture Model (GMM) is proposed to predict user distribution. The LSTM, with its capability of handling temporal data, enables the UAV to refer to the preceding information during flight decision-making and make action outputs based on predictions of the dynamic environment. Simulation results demonstrate that the proposed approach can accurately predict the position information of mobile users, achieve faster convergence, and obtain better performance in dynamic scenarios. The system performance has improved by 5.8 percent.

Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design

Secure Short-Packet Communications via UAV-Enabled Mobile Relaying: Joint Resource Optimization and 3D Trajectory Design

An analytical approach for modelling unmanned aerial vehicles and base station interaction for disaster recovery scenarios

Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in various sectors for various applications and services. In wireless networking, UAVs can be used as a vital part of the supplementary infrastructure to improve coverage, principally during public safety emergencies. Because of their affordability and potential for widespread deployment, there has been a growing interest in exploring the ways in which UAVs can enhance the services offered to isolated ground devices. Large areas may lose cellular coverage following a public safety emergency that impacts critical communication infrastructure. This prompts the need for the employment of D2D communication frameworks as a complement. In such critical conditions, timely response and network connectivity are essential factors for reliable communication. This study focuses on the mathematical models of UAV-based wireless communication in the context of disaster recovery. Particularly, we aim to model a queuing framework comprising UAVs as mobile relay nodes between the stranded user devices and neighbouring operational base stations. We present an iterative solution with a novel method for generating initial conditions for the two-stage queuing model. The approximate approach presented is validated for its accuracy using discrete-event simulation. The effects of various factors on performance measures are also analysed in detail. The validation results show that the discrepancy between the analytical approach and the simulation is less than 5%, which is the confidence interval of the simulation.

On energy conservation for fixed-wing UAV assisted practical full-duplex relaying with service requirement and bank angle limit

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

Urban Delay-Tolerant Multicast Using Uncontrolled Mobile Relay

The development of network functionalities in the urban environment is accompanied by the emergence of new publicly available data sources. They are the basis of the introduced research architecture and environment which are used to investigate the new multicast algorithms proposed in this paper. These message-oriented algorithms are primarily intended to meet the needs of opportunistic routing in heterogeneous urban sensor networks. Although, due to their generalized and protocol-agnostic design, they can be of use in other network applications and research areas. Uncontrolled mobile relay devices are the key elements of the presented delay-tolerant multicast framework. Multicast structures are modeled in four Polish cities based on open data on the location of public transportation vehicles and elements of urban infrastructure. Over 16,000 graphs were built and analyzed. It has been shown that the use of uncontrolled mobile relay enables the construction of time-spanning time-changing multicast structures. Their features are determined by the topology of a given city area, the distribution of destination nodes, as well as the number and the routes of mobile relay nodes. The efficacy and efficiency of the algorithms depend on the radio range of the nodes, maximum time span of forwarded messages, and network structure knowledge availability.

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

Mobile Relaying Between USV and AUV Under FER Constraints for Underwater Data Transmission

Mobile Relaying Between USV and AUV Under FER Constraints for Underwater Data Transmission

Energy efficiency maximization for UAV-enabled amplify-and-forward relaying via joint power and trajectory optimization

This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach’s algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes.

QoS-Aware User Association and Transmission Scheduling for Millimeter-Wave Train-Ground Communications

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

Average Age-of-Information Minimization in Aerial IRS-Assisted Data Delivery

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this paper, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average age of information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the non-linear relations among optimization variables make the formulated problem non-convex and intractable to solve, we propose a block coordinate descent (BCD) based iterative algorithm which decomposes the formulated problem into several sub-problems. The variables are optimized in each sub-problem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.

A Decision for Throughput Optimization in UAV-Enabled Emergency Outdoor–Indoor Fairness Communication

This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal fading caused by outdoor transmission loss through wall loss as well as indoor transmission loss when the UAV forwards the information to the indoor users reduces the channel gain and degrades the system downlink throughput. To improve the downlink throughput of the system and ensure communication fairness for indoor users, we designed a joint UAV location deployment and resource allocation (JLRO) algorithm that optimized UAV three-dimensional (3D) deployment location, power and bandwidth resource allocation. The simulation results demonstrate the convergence and validity of the proposed JLRO algorithm, as well as its superiority compared to benchmark algorithms.

Optimization of mobile‐relay‐aided base station sleep mode in wireless dense small cell network

AbstractIn this paper, a novel sleep‐mode base station (BS) selection scheme, namely forced handover, is proposed using the mobile stations as mobile relay stations in order to maximize number of sleeping BSs. Forced‐handover is a concept of forcing edge mobile stations to neighbour relay stations for both energy saving and SINR improvement of the network. The proposed energy saving scheme can be applied to not only micro sleep and macro sleep mode but also turning off the BSs. The performances of BS sleep mode schemes is investigated in various environments such as small cell network and different user distributions.

Resource allocation functionality with cluster aggregation (RAFCA) for secure HST video transmission

This paper presents an approach for resource allocation functionality with cluster aggregation (RAFCA) for securely transmitting surveillance videos in high-speed trains (HSTs). Each train wagon is assumed to be equipped with a surveillance camera, along with a mobile relay (MR) that communicates with the cellular base station (BS) on one hand and with the indoor devices inside the train on the other. The RAFCA approach is based on a permutation process of the video frames across multiple MRs, such that parts of the video captured by the camera of a given wagon are transmitted by the MRs of all other wagons. The probability of detection by an eavesdropper is calculated in this paper and shown to be negligible, which leads to the preservation of the privacy of the passengers. Moreover, the proposed approach is shown to have no or little impact on the quality of experience (QoE) of the transmitted videos, thus preventing quality degradation.

Resource optimization for energy‐efficient NOMA‐based multi‐UAV‐enabled relaying networks

AbstractOwing to the advantages of low cost, flexibility, and the transmission characteristics of air–ground (AG) channels, using the unmanned aerial vehicle (UAV) as mobile relay to assist wireless communication has received significant interest recently. A green non‐orthogonal multiple access (NOMA)‐based multi‐UAV‐enabled relaying system with different rate requirements is proposed here. To improve energy efficiency (EE) of the UAV relays, an optimization problem for user grouping, UAV trajectory design and resource allocation under the constraints of information causality constraint, UAVs' maximum service capacity, maximum transmit power constraint, and different communication rate requirements of the mobile users (MUs) is formulated. According to the grouping results, the UAVs' trajectory and power allocation by Dinkelbach method, successive convex approximation, and condensation algorithm are alternately optimized. To solve this highly coupled non‐linear mixed integer programming problem, a graph‐based grouping algorithm is proposed to reduce the relative distance between the UAV relay and the MU. Simulation results show that the proposed optimization algorithm can effectively improve the EE performance of this NOMA‐based multi‐UAV‐enabled relaying system.

Position optimization for mobile relay communications with constrained energy over a whole horizontal plane

This paper concerns the position optimization problem of a mobile relay over a whole horizontal plane. This problem is important because the position of a mobile relay directly affects the end-to-end performance, e.g., reliability, connectivity, and data rate. In this paper, we propose a new position optimization scheme of a mobile relay over a whole horizontal plane based on the one-bit feedback information from the destination node, which improves the performance over the prior scheme whose position of the mobile relay is optimized over a fixed orbit. In the proposed scheme, the mobile relay is equipped with merely one single onboard antenna. Moreover, no prior information about the positions of both the source node and the destination node is required. Thus, the proposed scheme can work at low network resources scenario, which is particularly suitable for mobile relay communication with constrained energy, e.g., the communications in a disaster area where the infrastructure is heavily damaged, volcano monitoring, and wireless powered communication networking. According to the characteristics of the proposed scheme, we further design two heuristic implementations to calculate the optimal position of a mobile relay over a whole horizontal plane. The first implementation has better steady performance whereas the second implementation has better convergence speed. We implement the proposed scheme and conduct an extensive performance comparison between the proposed scheme and prior schemes to verify the advantages of the proposed scheme.

Age–energy‐aware trajectory planning for UAV‐assisted data collection in Internet of Things

AbstractUnmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely remote monitoring by collecting information from monitoring devices and transferring the collected information to base station. The freshness of delivered information is critical to system performance, which can be quantified by the concept of age of information (AoI). Nevertheless, the fresher information comes at the cost of higher energy consumption at UAVs. Considering the limited onboard energy, it is essential to strike a balance between the age of delivered information and the required energy budget. here, both straight trajectory and circular trajectory of UAV are considered, and study a problem with the goal of supporting timely data collection while minimizing the energy consumption at UAV. The problem is formulated as a multi‐criteria optimization problem by jointly considering the aging of collected information, the trajectory planning of UAV and energy consumption at UAV. To solve the formulated problem, a solution procedure to find a sequence of Pareto‐optimal points is proposed. Simulation results demonstrate the Pareto‐optimal curve, which yields the energy‐efficient UAV trajectory for timely data collection.

Multi-UAV Path Learning for Age and Power Optimization in IoT With UAV Battery Recharge

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).