Federated Convolutional Auto-Encoder for Optimal Deployment of UAVs with Visible Light Communications

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association.

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.

In this article, we investigate the content distribution in the hotspot area,\nwhose traffic is offloaded by the combination of the unmanned aerial vehicle\n(UAV) communication and edge caching. In cache-enabling UAV-assisted cellular\nnetworks, the network deployment and resource allocation are vital for quality\nof experience (QoE) of users with content distribution applications. We\nformulate a joint optimization problem of UAV deployment, caching placement and\nuser association for maximizing QoE of users, which is evaluated by mean\nopinion score (MOS). To solve this challenging problem, we decompose the\noptimization problem into three sub-problems. Specifically, we propose a swap\nmatching based UAV deployment algorithm, then obtain the near-optimal caching\nplacement and user association by greedy algorithm and Lagrange dual,\nrespectively. Finally, we propose a low complexity iterative algorithm for the\njoint UAV deployment, caching placement and user association optimization,\nwhich achieves good computational complexity-optimality tradeoff. Simulation\nresults reveal that: i) the MOS of the proposed algorithm approaches that of\nthe exhaustive search method and converges within several iterations; and ii)\ncompared with the benchmark algorithms, the proposed algorithm achieves better\nperformance in terms of MOS, content access delay and backhaul traffic\noffloading.\n

The deployment of UAVs is one of the key challenges in UAV-based communications while using UAVs for IoT applications. In this article, a new scheme for energy efficient data collection with a deadline time for the Internet of things (IoT) using the Unmanned Aerial Vehicles (UAV) is presented. We provided a new data collection method, which was set to collect IoT node data by providing an efficient deployment and mobility of multiple UAV, used to collect data from ground internet of things devices in a given deadline time. In the proposed method, data collection was done with minimum energy consumption of IoTs as well as UAVs. In order to find an optimal solution to this problem, we will first provide a mixed integer linear programming model (MILP) and then we used a heuristic to solve the time complexity problem. The results obtained in the simulation results indicate the optimal performance of the proposed scheme in terms of energy consumption and the number of used UAVs.

In this paper, the optimal deployment of multiple unmanned aerial vehicles (UAVs) acting as flying base stations is investigated. Considering the downlink scenario, the goal is to minimize the total required transmit power of UAVs while satisfying the users' rate requirements. To this end, the optimal locations of UAVs as well as the cell boundaries of their coverage areas are determined. To find those optimal parameters, the problem is divided into two sub-problems that are solved iteratively. In the first sub-problem, given the cell boundaries corresponding to each UAV, the optimal locations of the UAVs are derived using the facility location framework. In the second sub-problem, the locations of UAVs are assumed to be fixed, and the optimal cell boundaries are obtained using tools from optimal transport theory. The analytical results show that the total required transmit power is significantly reduced by determining the optimal coverage areas for UAVs. These results also show that, moving the UAVs based on users' distribution, and adjusting their altitudes can lead to a minimum power consumption. Finally, it is shown that the proposed deployment approach, can improve the system's power efficiency by a factor of 20 χ compared to the classical Voronoi cell association technique with fixed UAVs locations.

In this paper, the problem of the deployment and resource management for visible light communication (VLC)-enabled, reconfigurable intelligent surfaces (RISs)-assisted unmanned aerial vehicle (UAV) networks is investigated. In the considered model, UAVs provide terrestrial users with wireless services and illumination simultaneously. Moreover, RISs are utilized to further improve the channel quality between UAVs and users. This joint placement and resource management problem is constructed aiming at acquiring the optimal UAV deployment, RISs phase shift, user and RIS association that satisfies the users’ needs with minimum consumption of the UAVs’ energy. An iterative algorithm that alternately optimizes continuous and binary variables is proposed to solve this mixed-integer programming problem. Specifically, RISs phase shift optimization is solved by phases alignment method and semidefinite program algorithm. Next, the successive convex approximation algorithm is proposed to settle the UAV deployment problem. The user and RIS association variables are relaxed to the continuous ones before adopting the dual method to find the optimal solution. Moreover, a greedy algorithm is proposed as an alternative to RIS association optimization with low complexity. Simulation results show that the proposed two schemes harvest the superior performance of 34.85% and 32.11% energy consumption reduction over the case without RIS, respectively.

This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori , the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.

A Design of a Scheduling System for an Unmanned Aerial Vehicle (UAV) Deployment

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users, where a UAV has limited service coverage and needs to fly through users at different locations for serving them locally. The existing UAV deployment studies largely assume the users’ demands do not change during UAV deployment. When the users’ demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users’ activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users, where the UAV decides for itself whether to chase an active user in a distant location (with delayed reward) or to wait for the idle user in the current location to return to the active state (with smaller service probability) over time. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. We also show the UAV is more likely to move if the temporal correlation of each user's idling pattern is stronger or the travel distance between users is shorter. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

There have been increasing interests in employing unmanned aerial vehicles (UAVs) such as drones for telecommunication purpose. In such networks, UAVs act as base stations and provide downloading service to users. Compared with conventional terrestrial base stations, such UAV-BSs can dynamically adjust their locations to improve network performance. However, there exists two important issues in UAV networks, handoff overhead and UAV deployment. The handoff overhead issue is particularly important for UAVs because UAV BSs are connected to cellular BSs via wireless backhaul links, which are costly in terms of spectrum usage and energy consumption. Hence, it is highly desirable to eliminate any unnecessary handoff to minimise the waste of wireless backhaul. The UAV deployment, on the other hand, introduces a new tool for radio resource management, since BS positions are open for network optimisation. In this paper, a smart user association algorithm, named reinforcement learning handoff (RLH), is devised to reduce redundant handoffs in UAV networks and two methods of UAV mobility control are designed to co-operate with the proposed RLH algorithm to optimise the system throughput. In the RLH algorithm, users perform handoffs according to the reward of a reinforcement learning process. In UAV deployment two UAV mobility control methods are proposed respectively base on the SNR estimation and based on the K-Means approach. According to our simulation results, the RLH algorithm can reduce the number of handoffs by 75%.

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV’s signal transmission and illumination will affect other UAVs’ signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

Unmanned aerial vehicles (UAVs), benefit by low cost, fast deployment, and large coverage, are widely applied as aerial relays in providing communication services for the areas with heavy traffic load or disasters. In this paper, the capacity of UAV relaying networks is studied, where the UAVs act as relays for the ground users. Two scenarios of UAVs distributed on an aerial 2-D plane and in a 3-D space are studied. The analysis reveals that in order to control the air-to-ground interference, the altitude of UAVs is at most in the same order to the distance between two adjacent UAVs. Besides, it is discovered that the pathloss exponent value α = 2.5 is a watershed for the capacity of UAV relaying networks, i.e., when α ≤ 2.5, the capacity of UAV relaying networks with the 2-D deployment of UAVs is greater than that with the 3-D deployment of UAVs. Otherwise, the capacity of UAV relaying networks with the 3-D deployment of UAVs is larger than that with the 2-D deployment of UAVs. Furthermore, the mobility of UAVs is studied, and it is verified that the mobility can orderly improve the capacity of UAV relaying networks. The theoretical analysis of this paper provides a guideline for the deployment of UAVs as aerial relays.

Content caching at base stations (BSs), unmanned aerial vehicles (UAVs) and specific user devices is expected to enhance the performance of content fetching significantly. In this paper, we study the UAV deployment problem in UAV-enabled cellular device-to-device (D2D) systems. Stressing the importance of content fetching delay and energy consumption, as well as the cost for deploying UAVs, we define a cost function and formulate the joint UAV deployment and user association problem as a cost function minimization problem. We then propose a heuristic algorithm to solve the optimization problem and determine the joint strategy. Numerical results verify the effectiveness of the proposed algorithm.