Joint UAV Deployment, Power Allocation, and Coalition Formation for Physical Layer Security in Heterogeneous Networks

UAV-enabled reliable mobile relaying under the time-varying Rician fading channel

This paper investigates a new scenario of spectrum sharing between unmanned\naerial vehicle (UAV) and terrestrial wireless communication, in which a\ncognitive/secondary UAV transmitter communicates with a ground secondary\nreceiver (SR), in the presence of a number of primary terrestrial communication\nlinks that operate over the same frequency band. We exploit the UAV's mobility\nin three-dimensional (3D) space to improve its cognitive communication\nperformance while controlling the co-channel interference at the primary\nreceivers (PRs), such that the received interference power at each PR is below\na prescribed threshold termed as interference temperature (IT). First, we\nconsider the quasi-stationary UAV scenario, where the UAV is placed at a static\nlocation during each communication period of interest. In this case, we jointly\noptimize the UAV's 3D placement and power control to maximize the SR's\nachievable rate, subject to the UAV's altitude and transmit power constraints,\nas well as a set of IT constraints at the PRs to protect their communications.\nNext, we consider the mobile UAV scenario, in which the UAV is dispatched to\nfly from an initial location to a final location within a given task period. We\npropose an efficient algorithm to maximize the SR's average achievable rate\nover this period by jointly optimizing the UAV's 3D trajectory and power\ncontrol, subject to the additional constraints on UAV's maximum flying speed\nand initial/final locations. Finally, numerical results are provided to\nevaluate the performance of the proposed designs for different scenarios, as\ncompared to various benchmark schemes. It is shown that in the quasi-stationary\nscenario the UAV should be placed at its minimum altitude while in the mobile\nscenario the UAV should adjust its altitude along with horizontal trajectory,\nso as to maximize the SR's achievable rate in both scenarios.\n

In this paper, we propose an interference management mechanism for physical layer security in a two-tier heterogeneous network (HetNet) system to maximize the secrecy rate of a user under eavesdropping attack. The HetNet system model includes a macro base station and several small base stations, several users and one eavesdropper. Massive multiple-input multiple-output technology is applied in all the base stations. The users and the eavesdropper are each armed with a single antenna. The proposed interference management mechanism takes consideration of transmit beamforming, spectrum reuse and D2D communications. D2D communications are applied around the eavesdropper in the macro cell. An optimization problem is formulated for the secrecy rate maximization under the constraints of quality of services of users associated with base stations and power limitation of base stations. Numerical results show that our proposed interference management mechanism for physical layer security can utilize the interference to improve the secrecy rate of the user under eavesdropping attack.

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively.

In this paper, we consider an unmanned aerial vehicle (UAV) assisted communications system, including two cooperative UAVs, a wireless-powered ground destination node leveraging simultaneous wireless information and power transfer (SWIPT) technique, and a terrestrial passive eavesdropper. One UAV delivers confidential information to destination and the other sends jamming signals to against eavesdropping and assist destination with energy harvesting. Assuming UAVs have partial information about eavesdropper's location, we propose two transmission schemes: friendly UAV jamming (FUJ) and Gaussian jamming transmission (GJT) for the cases when jamming signals are known and unknown a priori at destination, respectively. Then, we formulate an average secrecy rate maximization problem to jointly optimize the transmission power and trajectory of UAVs, and the power splitting ratio of destination. Being non-convex and hence difficult to solve the formulated problem, we propose a computationally efficient iterative algorithm based on block coordinate descent and successive convex approximation to obtain a suboptimal solution. Finally, numerical results are provided to substantiate the effectiveness of our proposed multiple-UAV schemes, compared to other existing benchmarks. Specifically, we find that the FUJ demonstrates significant secrecy performance improvement in terms of the optimal instantaneous and average secrecy rate compared to the GJT and the conventional single-UAV counterpart.

Due to the air-to-ground line-of-sight communication links, it is challenging to deal with the security threats in the wireless system with unmanned aerial vehicles (UAVs) integrated. In a UAV-enabled mobile relay system, the paper proposes a novel cooperative communication scheme from a physical layer security perspective to address the security issue. Specifically, in a mobile relay system where a UAV relay is employed to forward confidential information between two ground users, an eavesdropper exists on the ground wiretapping the relay UAV. To enhance the security performance of the system, we introduce a friendly UAV jammer transmitting interference signals and confusing the eavesdropper. A secrecy rate maximization problem is then formulated, subject to mobility constraints, power constraints on both UAVs and the information-causality constraint on the relay UAV. To solve the non-convex optimization problem with closely coupled variables, we decouple the problem into more tractable subproblems. An iterative algorithm is thus proposed to optimize the transmit power and flight trajectory of both UAVs alternately via the update-rate-assisted block coordinate descent and successive convex approximation techniques. Simulation results demonstrate that proposed cooperative design can significantly improve the secrecy rate of the UAV-enabled mobile relay system compared to benchmark schemes.

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.

With the rapid advancement of communication technologies, space‐air‐ground integrated networks (SAGIN) have become a pivotal research frontier in current and future communication domains. To tackle critical challenges in SAGIN scenarios, such as excessive task‐related energy consumption and insufficient communication‐computing resources, this paper proposes a three‐tier edge computing architecture integrating satellites, unmanned aerial vehicle (UAV) swarms, and ground systems. Aiming to minimize the system's weighted energy consumption and latency, we investigate the joint optimization of task allocation, user‐UAV association, UAV deployment, and resource allocation between UAVs and low‐earth orbit (LEO) satellites. Formulated as a non‐convex mixed‐integer nonlinear combinatorial optimization problem, this work integrates the branch‐and‐bound method, multi‐start global optimization, and gray wolf optimization (GWO) to develop a suboptimal solution based on block coordinate descent (BCD), which decouples the original problem into three subproblems for independent solving and iterative approximation of the optimal solution. Experimental results show that the proposed algorithm reduces the total system cost by 7.81%, 11.99%, and 45.69% compared with baseline algorithms with random user‐UAV association, random UAV positioning, and random task assignment, respectively, effectively cutting down overall energy consumption and task latency.

Based on current capabilities, we examine the feasibility of creating a carbohydrate-based regenerative fuel cell (CRFC) as the primary power source for unmanned aerial vehicles (UAV) for long endurance missions where station keeping is required. The CRFC power system evaluated in this research is based on a closed-loop construct where carbohydrates are generated from zooxanthellae, algae which create excess carbohydrates during photosynthesis. The carbohydrates are then fed to a carbohydrate fuel cell where electric power is generated for the UAV’s propulsion, flight control, payload, and accessory systems. The waste products from the fuel cell, carbon dioxide and water, are used by the zooxanthellae to create more carbohydrates, therefore mass is conserved in the process of power generation. The overall goal of this research is to examine the potential of CRFCs as a viable power source for UAV systems, to look at scaling issues related to different vehicle sizes and missions, and to identify sensitivities in the CRFC system to different system parameters, indicating the areas where technology improvements may make CRFCs a viable technology. Through simulations, a UAV is sized to determine if greater than 24 hour endurance flight is possible and these results are compared to UAVs using more traditional photo-cell based power systems. The initial results suggest that CRFCs have potential as a power system for long endurance UAVs, and could offer significant improvements to the overall system performance. The final outcome of this research is to identify the most important areas for more detailed follow-on work in designing a production-ready CRFC power system for long endurance UAVs.

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.

Unmanned Aerial Vehicles (UAVs) have emerged as crucial components of 5G Internet of Things (IoT) networks, expanding coverage and capacity in various applications. While prior research has extensively examined the impact of power control factor adjustments on UAV uplink transmission, limited attention has been devoted to UAV downlink transmission, which is vital for seamless connectivity and quality of service. In this study, we address this research gap by investigating the effects of reduced power control factors on UAV downlink transmission. We use MATLAB simulations to model how user equipment (UE) is randomly placed in a UAV network. We then check the coverage probability and spectral efficiency for UAV altitudes between 200 and 500 m, taking into account different power control factors in the downlink transmission. Our findings show that a certain threshold of 0.05 for the power control factor keeps coverage probabilities at different heights, making sure that served UEs have enough signal strength. Surprisingly, there is no significant change in spectral efficiency when compared to the baseline results. This means that lowering the power control factor does not hurt spectral efficiency in UAV downlink transmission. These results help us figure out how to make UAV communication work better in 5G IoT networks. They also help us make smart choices about how to change the power to make up for lost downlink signals while keeping the quality of service high. Moreover, our study lays the groundwork for future research in UAV deployment and communication optimization within advanced wireless networks.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.

In order to fully utilize the spectrum resources, this work considers a unmanned aerial vehicle (UAV) uplink communication system based on non-orthogonal multiple access technology (NOMA), in which the UAV receives information from the ground users with a certain flying altitude. As an initial study, we consider a simplified setup with two ground users to draw some insightful results. Explicitly, we first formulate an optimization problem that maximizes the sum throughput subject to each user's transmit power constraint and their corresponding minimum transmission rate requirement. Then, both the optimal transmit power and UAV's deployment location are derived with the aid of employing the Karush-Kuhn-Tucher (KKT) conditions. Simulation results show that the proposed UAV's deployment scheme with the users' power allocation can achieve a higher sum throughput compared with two existing benchmark schemes.

Unmanned aerial vehicles (UAVs) have been applied to not only military missions like surveillance and reconnaissance but also commercial missions like meteorological observation, aerial photograph, communication relay, internet network build and disaster observation. Fuel cells make UAVs eco-friendly by using hydrogen. Proton exchange membrane fuel cells (PEMFCs) show low operation temperature, high efficiency, low noise and high energy density and those characterisitcs are well fitted with UAVs. Thus Fuel cell based UAVs have been actively developed in the world. Recently, fuel cell UAVs have started to develope for high altitude UAVs because target altitude of UAVs is expanded upto stratosphere altitude. Long endurance of UAVs is essential to improve effects of the missions. Improvement of UAV endurance time could be fulfilled by developing a hydrogen fuel storage system with high energy density and reducing the weight of UAVs. In this paper, research trend and analysis of fuel cell UAVs are introduced in terms of their altitude and endurance time and then the prospect of fuel cell UAVs are shown.

Unmanned aerial vehicles (UAVs) have attracted attentions due to their mobility and high possibility of the line of sight (LoS) channel. We can fully use these two properties by carefully optimizing the UAVs' trajectories and cooperating with some facilities. A UAV working as a mobile relay now attracts many interests due to its low cost and reliable performance. In this paper, we study a relaying system, where a UAV works as an aerial mobile relay to help some ground base stations send information to ground users periodically by using time division multiple access (TDMA). We aim to maximize the minimum average user rate through solving a non-convex and information causality constraints involved problem by jointly optimizing the UAV trajectory, nodes scheduling, and power allocation to ensure the fairness among all users. Finally, we propose an efficient iterative algorithm to solve a derived mixed-integer non-convex optimization problem to achieve this target by using block coordinate descent (BCD) and successive convex approximation (SCA) methods and prove the convergence of our algorithm. Simulations show the effectiveness of our proposed algorithm, some useful trade-offs and insights about the structure of our optimized trajectory, and the influence of two widely used trajectory initialization methods.