Enabled Wireless Networks Research Articles

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.

MUSCAT: Distributed multi-agent Q-learning-based minimum span channel allocation technique for UAV-enabled wireless networks

We consider a minimum span channel allocation problem (MS-CAP) to overcome spectrum scarcity and facilitate the efficiency of unmanned aerial vehicle (UAV)-enabled wireless networks. Basically, the MS-CAP minimizes the difference between the maximum and minimum used frequency, i.e., the required total bandwidth, while guaranteeing the quality-of-service (QoS) requirements for each wireless link in the network. The conventional optimal minimum span channel allocation (MS-CA) scheme is based on a centralized approach, assuming that global network information is available at the central controller. In practice, however, this may not be feasible for dynamic environments like UAV-enabled wireless networks since the real-time exchange of network information and channel allocation results with dynamically moving UAVs is formidable. Hence, we propose a novel practical MS-CA algorithm based on distributed multi-agent reinforcement learning (MARL), where each agent independently learns its best strategy with its local observations. To the best of our knowledge, the proposed technique is the first work of designing a distributed MARL for the MS-CAP for multi-UAV-enabled wireless networks in the literature. Numerical results reveal that the proposed distributed MS-CA technique can efficiently save the required total bandwidth while ensuring the QoS requirements of each link, represented by the signal-to-interference plus noise ratio (SINR) threshold, even in dynamic wireless networks. It validates the applicability of the proposed distributed MS-CA framework to dynamic networks.

Low-Latency Mobile Virtual Reality Content Delivery for Unmanned Aerial Vehicle-Enabled Wireless Networks With Energy Constraints

In this paper, we leverage the benefits of unmanned aerial vehicle (UAV)-enabled wireless networks in providing high link qualities and on-demand services to achieve low-latency mobile virtual reality content delivery. Taking the limited energy storage at the UAVs into consideration, we formulate an average latency minimization problem, subject to the constraints on the UAV's kinematic, computation, and transmission capabilities. Utilizing the Lyapunov optimization approach, we first transform this NP-hard problem into a weighted-latency-plus-energy minimization problem for each time slot. Then, we further decompose the transformed problem into three subproblems, namely, the UAV's trajectory design subproblem, the UAV's processing frequency allocation subproblem, and the UAV's transmission power control subproblem. An iterative algorithm is proposed to solve these three subproblems. To reduce the complexity, we propose a low-complexity algorithm that designs the UAV's trajectory with the target of flight energy minimization. Through numerical results, we show that, compared to other benchmarks, our proposed algorithms can achieve a good trade-off between the average latency and the energy consumption by judiciously tuning the Lyapunov control parameter.

Multiagent Collaborative Learning for UAV Enabled Wireless Networks

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches

In this paper, we address dynamic network selection problems of mobile users in an intelligent reflecting surface (IRS)-enabled wireless network. In particular, the users dynamically select different service providers (SPs) and network services over time. The network services are composed of adjustable resources of IRS and transmit power. To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve. For this, the replicator dynamics is used to model the service selection adaptation of the users. To allow the users to take their past service experiences into account their decisions, we further adopt an enhanced version of the evolutionary game, namely fractional evolutionary game, to study the SP and network service selection. The fractional evolutionary game incorporates the memory effect that captures the users’ memory on their decisions. We theoretically prove that both the game approaches have a unique equilibrium. Finally, we provide numerical results to demonstrate the effectiveness of our proposed game approaches. In particular, we have reveal some important finding, for instance, with the memory effect, the users can achieve the utility higher than that without the memory effect.

A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>

Mobile User Trajectory Tracking for IRS Enabled Wireless Networks

In this paper, we consider an intelligent reflecting surface (IRS) enabled mobile network, where a fixed access point (AP) communicates with a mobile user (MU) via the aid of an IRS. We assume that the MU moves from one elementary square to another following a Markov random walk within a grid, and propose a maximum a posteriori (MAP) criterion to track the movement of the MU by leveraging the line-of-sight component of the IRS-MU link. Since it is infeasible to derive an explicit expression for the average probability of estimation error (APEE) for the proposed MAP criterion, we derive a closed-form upper bound for the APEE, which is used as the cost function to optimize the phase shifts of the IRS units. Considering the unit modulus constraints incurred by the IRS units, a manifold optimization (MO) method is firstly employed to gain a favorable solution to the formulated optimization problem, followed by a low-complexity codebook based solution to circumvent the high computational cost of the MO method. Our numerical results demonstrate the superior performance of the proposed IRS phase shift designs over the benchmark method.

Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks

Efficient scheduling policy is crucial in wireless networks due to delay-sensitivity of many emerging applications. In this work, we consider a joint user pairing and scheduling (UPaS) scheme for multi-carrier non-orthogonal multiple access (MC-NOMA)-enabled wireless networks to reduce the maximum completion time of serving uplink users. The NOMA scheduling problem is shown to be NP-hard and a shortest processing time (SPT)-based strategy to solve the same problem within affordable time and complexity is introduced. The simulation results confirm the efficacy of the proposed scheduling scheme in terms of the maximum completion time in comparison with orthogonal multiple access (OMA) and random NOMA pairing.

Tracking and Elimination of Blockhole and wormhole in MANETS

Now a day’s wireless networks are achieving lots of fame depending upon the needs of the people in wireless connections without based on geographical location. To make communication possible with the people and to exchange the data from source to destination by not using any of the wires for that enabled wireless networks. Such types of networks operation mode provide internet and connection services to cellular to alone or they may be connected with more than 1 point. The problems shown by these networks are limiting the band widths, power of the battery and enhancement of quality & coverage transmission problems and these are similar to the problems of wireless communication. The major problems in MANET are black hole attacks and worm hole attacks.in this paper we propose the best detection, prevention and isolation techniques for various attacks which occur in Manets.Finally, the experimental analysis shows that we have reduced the major attacks by malicious node.Finally, by analyzing the advantages and disadvantages of all the existing techniques, we proposed an algorithm to detect wormhole attack in ad hoc networks.

Robust Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks in the Presence of Jammers

This paper investigates a multi-unmanned aerial vehicle (UAV) enabled wireless communication, where a number of ground nodes (GNs) are scheduled to communicate with UAVs in the presence of jammers with imperfect location information. Considering different quality of service (QoS) requirements for a wide range of applications, we aim to improve the minimum throughput, the average throughput, and the delay-constrained minimum throughput of all GNs, respectively, via the joint design of UAVs' trajectories, GNs' scheduling and power allocation. However, the formulated optimization problems are difficult to solve due to the non-convex and combinatorial nature. To overcome this difficulty, we propose two block coordinate descent (BCD) based algorithms to solve them sub-optimally with the aid of slack variables, successive convex approximation (SCA) technique and S-procedure. Numerical results show that our proposed algorithms outperforms the benchmark algorithms and offers a considerable gain in the view of different QoS requirements, giving a certain practical significance.

USING AUTOMATED BATTERY REPLACEMENT STATIONS FOR THE PERSISTENT OPERATION OF UAV-ENABLED WIRELESS NETWORKS DURING NPP POST-ACCIDENT MONITORING

Motivation. After the Fukushima, nuclear power plant (NPP) accident, an unmanned aerial vehicle (UAV)-enabled wireless network (UEWN) is considered to be used for transmitting the data from monitoring stations (MSs) to the crisis center (CrS) during NPP post-accident monitoring missions. Nevertheless, the popular lightweight UAVs have an endurance of about 20–40 minutes only. The last fact presents a significant barrier to use a UEWN in complex, long-term NPP post-accident monitoring missions. The subject matter of the paper is the process of ensuring the persistent operation of UEWN. This paper aims to propose an approach to ensuring the persistent operation of UEWN during NPP post-accident monitoring missions via automatic battery replacement stations (ABRSs). The objectives of the paper are: to propose a scheme of deployment of a UEWN with ABRSs for the given scenario; to give an example of the proposed scheme application for persistent transmitting the data from a MS to the CrS during Zaporizhzhia NPP (ZNPP) post-accident monitoring missions; to discuss an example of the proposed scheme application. The following results were obtained. A simplified scheme of deployment of a UEWN with ABRSs for transmitting the data from the MS to the CrS during NPP post-accident monitoring missions was developed and described. Two segments within the UEWN were considered: 1) Wi-Fi segment, comprising the WiFi equipment of the MS, the onboard WiFi equipment of the UAVs of a multi-rotor type (MUAVs), and onboard WiFi equipment of the UAV of an airplane-type (AUAV); 2) LoRaWAN segment, comprising the LoRaWAN equipment of the AUAV and the LoRaWAN equipment of the CrS. An example of deployment of a UEWN with ABRSs for transmitting the data from an MS of ZNPP to the CrS was given and described. A shift schedule for 2 MUAV fleets ensuring the persistent operation of the UEWN during post-accident ZNPP monitoring missions was built and analyzed. It was evaluated how the flight distance for the MUAV between its location point in the WiFi segment and the ABRS effects: the duty time for the MUAV fleet; the waiting time for the MUAV to flight to the point of its location in the WiFi segment; the number of the MUAV fleets for ensuring the persistent operation of the UEWN. The new research will aim at developing a scheme of deployment of the UEWN with ABRSs for several WiFi segments

Secrecy Transmission in Large-Scale UAV-Enabled Wireless Networks

This paper considers the secrecy transmission in a large-scale unmanned aerial vehicle (UAV)-enabled wireless network, in which a set of UAVs in the sky transmit confidential information to their respective legitimate receivers on the ground, in the presence of another set of randomly distributed suspicious ground eavesdroppers. We assume that the horizontal locations of legitimate receivers and eavesdroppers are distributed as two independent homogeneous Possion point processes (PPPs), and each of the UAVs is positioned exactly above its corresponding legitimate receiver for efficient secrecy communication. Furthermore, we consider an elevation-angle-dependent line-of-sight (LoS)/non-LoS (NLoS) path-loss model for air-to-ground (A2G) wireless channels and employ the wiretap code for secrecy transmission. Under such setups, we first characterize the secrecy communication performance (in terms of the connection probability, secrecy outage probability, and secrecy transmission capacity) in mathematically tractable forms, and accordingly optimize the system configurations (i.e., the wiretap code rates and UAV positioning altitude) to maximize the secrecy transmission capacity, subject to a maximum secrecy outage probability constraint. Next, we propose to use the secrecy guard zone technique for further secrecy protection, and analyze the correspondingly achieved secrecy communication performance. Finally, we present numerical results to validate the theoretical analysis. It is shown that the employment of secrecy guard zone significantly improves the secrecy transmission capacity of this network, and the desirable guard zone radius generally decreases monotonically as the UAVs’ and/or the eavesdroppers’ densities increase.

Cyclical NOMA Based UAV-Enabled Wireless Network

In order to achieve high spectral efficiency and low access delay, this paper introduces cyclical non-orthogonal multiple access (NOMA) into unmanned aerial vehicle (UAV)-enabled wireless network. It allows the UAV to communicate with multiple ground users in the same time–frequency resources, cyclically. The minimum throughput over all ground users is maximized by jointly optimizing multiuser communication scheduling with cyclical NOMA and UAV trajectory. It turns out that the maximization of minimum throughput is a mixed integer non-linear non-convex optimization problem. In this paper, this problem is decoupled into two blocks, i.e., the optimization of multiuser communication scheduling with cyclical NOMA and the optimization of UAV trajectory. Then, a joint optimization algorithm is proposed based on the block coordinate descent method. The simulation results demonstrate that the proposed joint optimization method can double the minimum throughput, compared with cyclical TDMA. In addition, it reduces users’ average access delay and UAV’s flying range under the same minimum throughput.

Learning-Based User Association for Dual-UAV Enabled Wireless Networks With D2D Connections

When the communication infrastructures are damaged in disasters, unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to achieve rapid service recovery. However, the wireless coverage of a single UAV is limited, and device-to-device (D2D) transmission can be exploited to accommodate more users with wireless service. Thus, in this paper, we consider the user association for a dual-UAV-enabled wireless network with the help of D2D connections in disasters. To achieve better performance, we maximize the weighted sum rate of the UAV-served users and the total number of D2D-connected users by optimizing the user association. The formulated problem is a combinatorial optimization problem involving binary variables, which is extremely difficult to solve. Accordingly, we propose two algorithms to solve it approximatively. The first algorithm is the learning-based clustering algorithm by viewing the optimization as a clustering problem. The users who can be served by the UAVs are regarded as cluster centers, which need to be selected optimally. In the second one, the binary variables are relaxed into continuous variables, and then, the problem can be solved by the existing optimization tools. The simulation results demonstrate that these two algorithms can achieve excellent suboptimal performance, and the computational complexity of the learning-based clustering algorithm is much lower.

Partition-Based Caching in Large-Scale SIC-Enabled Wireless Networks

Existing designs for content dissemination do not fully explore and exploit potential caching and computation capabilities in advanced wireless networks. In this paper, we propose two partition-based caching designs, i.e., a coded caching design based on Random Linear Network Coding and an uncoded caching design. We consider the analysis and optimization of the two caching designs in a large-scale successive interference cancelation (SIC)-enabled wireless network. First, under each caching design, by utilizing tools from stochastic geometry and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general file size regime. To further obtain design insights, we also derive closed-form expressions for the successful transmission probability in the small and large file size regimes, respectively. Then, under each caching design, we consider the successful transmission probability maximization in the general file size regime, which is an NP-hard problem. By exploring structural properties, we successfully transform the original optimization problem into a Multiple-Choice Knapsack Problem (MCKP), and obtain a near optimal solution with 1/2 approximation guarantee and polynomial complexity. We also obtain closed-form asymptotically optimal solutions. The analysis and optimization results show the advantage of the coded caching design over the uncoded caching design, and reveal the impact of caching and SIC capabilities. Finally, by numerical results, we show that the two proposed caching designs achieve significant performance gains over some baseline caching designs.

Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAVs' trajectory and power control. The formulated problem is a mixed integer non-convex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the non-convex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge to at least a locally optimal solution. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

AC-POCA: Anticoordination Game Based Partially Overlapping Channels Assignment in Combined UAV and D2D-Based Networks

Device-to-device (D2D)-enabled wireless networks are becoming increasingly popular. However, in remote, rural, and disaster affected areas, it is difficult to construct such wireless networks due to the unavailability or inadequacy of cellular infrastructures. Unmanned aerial vehicles (UAVs) can be a good candidate to promptly construct the D2D-enabled wireless network. However, the assignment of the radio channels of the nodes (i.e., UAVs and user terminals) is challenging due to the availability of only a limited number of orthogonal channels and the interference issue resulted from using arbitrary channels. Furthermore, the dynamic topology and high mobility of nodes in such a combined UAV and D2D-based network make conventional channel assignment (CA) algorithm no longer suitable. In this paper, we formally address this problem, and demonstrate how partially overlapping channels (POCs) and game theory can be exploited to alleviate the problem. In this vein, we propose a distributed anticoordination game based POC assignment algorithm referred to as AC-POCA. In our proposed AC-POCA, the nodes use only local information to play the game, and reach a steady state, uniqueness of which is verified through analysis. Also, the upper bound of AC-POCA (i.e., price of anarchy) is analytically evaluated, which is corroborated by simulation results. In addition, simulation results demonstrate the effectiveness of AC-POCA in terms of good throughput and low signaling overhead in a dynamic environment.

Joint Spectrum and Energy Efficiency in Device to Device Communication Enabled Wireless Networks

This paper presents a joint spectrum and energy efficiency maximization mechanism for device to device communication enabled wireless networks. Most approaches in the literature fail to take into account link heterogeneity in terms of their demands and the traffic they inject into the network. This paper presents a linear optimization formulation to allocate bandwidth to links, not only based on network topology but also based on traffic injection patterns of devices and link demands. We derive bounds on the achievable spectrum efficiency. The achieved spectrum efficiency and energy efficiency are further enhanced by deploying power control using $ {\mathcal {M}-}$ matrix theory. The proposed approach is shown to provide an improvement between 25% to up to four orders of magnitude in spectrum efficiency and between 25% to up to five orders of magnitude in energy efficiency compared to existing mechanisms, depending on the network topology and traffic injection patterns of the nodes.

Content Caching and Distribution in Smart Grid Enabled Wireless Networks

To facilitate wireless transmission of multimedia content to mobile users, we propose a content caching and distribution framework for smart grid enabled OFDM networks, where each popular multimedia file is coded and distributively stored in multiple energy harvesting enabled serving nodes (SNs), and the green energy distributively harvested by SNs can be shared with each other through the smart grid. The distributive caching, green energy sharing and the on-grid energy backup have improved the reliability and performance of the wireless multimedia downloading process. To minimize the total on-grid power consumption of the whole network, while guaranteeing that each user can retrieve the whole content, the user association scheme is jointly designed with consideration of resource allocation, including subchannel assignment, power allocation and power flow among nodes, where the user association scheme decides which SN serves which user. First, the optimal power allocated to each subchannel and the power flows among nodes are derived analytically. Then, the user association problem is decoupled from the subchannel assignment problem, and the proposed user association scheme has leveraged the multicasting nature of the content download process. To decide which subchannel is assigned to which node, the number of subchannels assigned to each node is first determined, according to the green energy generating rate and the associated traffic offload, and then the corresponding subchannels are selected from the total assigned system spectrum. Simulation results demonstrate that bringing content, green energy and serving node closer to the end user can notably reduce the on-grid energy consumption.

Software Defined Networking Enabled Wireless Network Virtualization: Challenges and Solutions

Next generation (5G) wireless networks are expected to support massive data and accommodate a wide range of services/use cases with distinct requirements in a cost-effective, flexible, and agile manner. As a promising solution, WNV, or network slicing, enables multiple virtual networks to share a common infrastructure on demand, and to be customized for different services/use cases. This article focuses on network-wide resource allocation for realizing WNV. Specifically, the motivations, the enabling platforms, and the benefits of WNV, are first reviewed. Then, resource allocation for WNV and the technical challenges are discussed. Afterward, an SDN enabled resource allocation framework is proposed to facilitate WNV, including the key procedures and the corresponding modeling approaches. A case study is provided as an example of resource allocation in WNV. Finally, some open research topics essential to WNV are discussed.