Optimal Deployment of Tethered Drones for Maximum Cellular Coverage in User Clusters

Unmanned aerial vehicle (UAV) assisted cellular communication is gaining significant interest recently. Although it offers several advantages over terrestrial communication, UAV communication suffers from two main shortcomings. The typical untethered UAV (uUAV) has a limited battery power supply and therefore limited flying time, and it needs an extra wireless backhaul link to connect users to the core network. In this paper, we propose the utilization of the tethered UAV (tUAV) to assist the cellular network, where the tether provides power supply and connects the tUAV to the core network through high capacity link. The tUAV however has a limited mobility due to the limited tether length. A stochastic geometry-based analysis is provided for the coverage probability of an UAV-assisted cellular network where the mobile users located within a circular hot-spot. For that setup, we analyze and compare two scenarios: (i) utilizing uUAV and (ii) utilizing tUAV, for offloading the terrestrial base station (TBS). We capture the aforementioned limitations of each of the uUAV and the tUAV in our analysis. A novel user association analysis is provided given the TBS and the UAV locations. Next, we study the optimal locations of the uUAV and the tUAV to maximize the coverage probability. Multiple useful insights are revealed. For instance, numerical results show that tUAVs outperform uUAVs when the tether length is above 75 m, given that the uUAV is available for 80% of the time due to its battery limitations.Comment: Accepted at the IEEE Transaction on Wireless Communication

Resource allocation, user association and placement for uav-assisted communications

In the past few years, unmanned aerial vehicle (UAV)-assisted heterogeneous network has attracted significant attention due to its wide range of applications, such as disaster rescue and recovery, ground macro base station (MBS) traffic offloading, communications for temporary events, and data collection for further processing in Internet of Things (IoT). A UAV can act as a flying base station (BS) to quickly recover the communication coverage in the disaster area when the regular terrestrial infrastructure is malfunctioned. The UAV-assisted heterogeneous network can effectively provision line of sight (LoS) communication links and therefore can mitigate potential signal shadowing and blockage. The regulation relaxation and cost reduction of UAVs as well as communication equipment miniaturization make the practical deployment of highly mobile wireless relays more feasible than before. In fact, the 3GPP Rel-16 has included UAV-enabled wireless communications in the new radio standard, aiming to boost capacity and coverage of fifth generation (5G) wireless networks. However, the performance of UAV-assisted communications is greatly affected by the resource allocation scheme, user association policy and the UAV placement strategy. Also, the limited on-board energy and flight time of the UAV poses a great challenge on designing a robust and reliable UAV-enabled IoT network. To maximize the throughput in the UAV-assisted mobile access network, an optimization problem which determines the 3D UAV deployment and resource allocation in a given hotspot area under the constraints of user Quality of Service (QoS) requirements and total available resources is formulated. First, the primal problem is decomposed into two subproblems, i.e., the 3D UAV placement problem and the resource allocation problem. Second, a cyclic iterative algorithm which solves the two sub-problems separately and uses the output of one as the input of the other is proposed. An optimization problem that aims to minimize the average latency ratio of all users is formulated by determining the 3D location of the UAV, the user association and the bandwidth allocation policy between the MBS and the drone base station (DBS) with the constraint of each user’s QoS requirement and total available bandwidth. The formulated problem is a mixed integer non-convex optimization problem, a very challenging and difficult problem. To make formulated problem tractable, it is decomposed into two subproblems, i.e., the user association and bandwidth allocation problem and the 3D DBS placement problem. These two subproblems are alternatively optimized until no performance improvement can be further achieved. To address the challenge of limited on-board battery capacity and flight time, a tethered UAV (TUAV)-assisted heterogeneous network where the aerial UAV is connected with a ground charging station (GCS) through a tether is proposed. The objective of the formulated problem is to maximize the sum rate of all users by jointly optimizing the user association, resource allocation and placement of the GCSs and the aerial UAVs, constrained by each user’s QoS requirement and the total available resource. Since the primal problem is highly non-convex and non-linear and thus challenging to solve, it is decomposed into three subproblems, i.e., the TUAV placement problem, the resource allocation problem and the user association problem. Then, the three sub-problems are alternately and iteratively optimized by using the outputs of the first two as the input for the third. The future work comprises two parts. First, IoT devices usually are generally deployed at remote areas with limited battery capacities and computing power. Therefore, the generated data needs to be offloaded to a more powerful computing server for further processing. Unfortunately, the trajectory design in UAV data collection is generally NP-hard and difficult to obtain the optimal solution. Advances of machine learning (ML) provide a promising alternative approach to solve such problems that cannot be solved by traditional optimization methods. Hence, deep reinforcement learning (DRL) is proposed to be explored to obtain a near optimal solution. Second, the low earth orbit (LEO) satellite networks will revolutionize traditional communication networks with their promising benefits of service continuity, wide-area coverage, and availability for critical communications and emerging applications. However, the integration of LEO satellite networks and terrestrial networks will be another future research endeavor

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Holistic resource management in UAV-assisted wireless networks

Unmanned aerial vehicles (UAVs) are considered as a promising solution to assist terrestrial networks in future wireless networks (i.e., beyond fifth-generation (B5G) and sixth-generation (6G)). The convergence of various technologies requires future wireless networks to provide multiple functionalities, including communication, computing, control, and caching (4C), necessary for applications such as connected robotics and autonomous systems. The majority of existing works consider the developments in 4C individually, which limits the cooperation among 4C for potential gains. UAVs have been recently introduced to supplement mobile edge computing (MEC) in terrestrial networks to reduce network latency by providing mobile resources at the network edge in future wireless networks. However, compared to ground base stations (BSs), the limited resources at the network edge call for holistic management of the resources, which requires joint optimization. We provide a comprehensive review of holistic resource management in UAV-assisted wireless networks. Integrated resource management considers the challenges associated with aerial networks (such as three-dimensional (3D) placement of UAVs, trajectory planning, channel modelling, and backhaul connectivity) and terrestrial networks (such as limited bandwidth, power, and interference). We present architectures (source-UAV-destination and UAV-destination architecture) and 4C in UAV-assisted wireless networks. We then provide a detailed discussion on resource management by categorizing the optimization problems into individual or combinations of two (communication and computation) or three (communication, computation and control). Moreover, solution approaches and performance metrics are discussed and analyzed for different objectives and problem types. We formulate a mathematical framework for holistic resource management to minimize the linear combination of network latency and cost for user association while guaranteeing the offloading, computing, and caching constraints. Binary decision variables are used to allocate offloading and computing resources. Since the decision variables are binary and constraints are linear, the formulated problem is a binary linear programming problem. We propose a heuristic algorithm based on the interior point method by exploiting the optimization structure of the problem to get a sub-optimal solution with less complexity. Simulation results show the effectiveness of the proposed work when compared to the optimal results obtained using branch and bound. Finally, we discuss insight into the potential future research areas to address the challenges of holistic resource management in UAV-assisted wireless networks

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Computational efficiency maximization for UAV-assisted MEC network with energy harvesting in disaster scenarios

Wireless networks are expected to provide unlimited connectivity to an increasing number of heterogeneous devices. Future wireless networks (sixth-generation (6G)) will accomplish this in three-dimensional (3D) space by combining terrestrial and aerial networks. However, effective resource optimization and standardization in future wireless networks are challenging because of massive resource-constrained devices, diverse quality-of-service (QoS) requirements, and a high density of heterogeneous devices. Recently, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks are considered a potential candidate to provide effective and efficient solutions for disaster management in terms of disaster monitoring, forecasting, in-time response, and situation awareness. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. In this thesis, we consider a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed integer non-linear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power splitting architecture splits the source power for communication and EH. We jointly optimize user association, the transmission power of UE, task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt k-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading with EH, and no-offloading no-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumptio

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

Performance Evaluation of UAV-enabled Cellular Networks with Battery-limited Drones

Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) to offload Macro-BSs in hotspots. However, due to the limited battery on-board, UAVs can typically stay in operation for less than 1.5 hours. Afterward, the UAV has to fly back to a dedicated charging station that recharges/replaces the UAV's battery. In this paper, we study the performance of a UAV-enabled cellular network while capturing the influence of the spatial distribution of the charging stations. In particular, we use tools from stochastic geometry to derive the coverage probability of a UAV-enabled cellular network as a function of the battery size, the density of the charging stations, and the time required for recharging/replacing the battery

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU