Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

Unmanned aerial vehicle (UAV) communication has been deemed as a promising technology to collect data for the Internet of Things (IoT) in inaccessible areas. However, due to the limited UAV flight time, traditional UAV communication may not be competent for large-scale IoT data collection. This paper considers integrating non-orthogonal multiple access (NOMA) into UAV communication systems to collect data for large-scale IoT devices within UAV flight time. We aim to minimize the total energy consumption of IoT devices while ensuring data collection, by jointly optimizing UAV trajectory, IoT device scheduling and transmit power. The formulated problem is a mixed integer non-convex problem, which is challenging to solve in general. We propose a data collection optimization algorithm (DCOA) to solve it by applying the Generalized Benders Decomposition (GBD) and successive convex approximation (SCA) techniques. Then, a greedy algorithm (GA) is also proposed to reduce complexity by simplifying the optimization of UAV trajectory and IoT device scheduling. Finally, the numerical results demonstrate that, compared with traditional UAV communication systems, the NOMA-aided UAV system performs better in terms of data collection and lower total energy consumption of IoT devices can be achieved by DCOA.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

In this paper, we propose a novel trajectory design for the unmanned aerial vehicle (UAV) communication systems, where a single UAV moves along with multiple mobile terres-trial users to provide communication services, in the presence of malicious jamming. In our proposed design, we take the users' mobility and the jammer's adaptive jamming power into consideration and model the UAV's trajectory as a Markov decision process. Then, with the goal of optimizing the overall average throughput in our system, we design a twin-delayed deep deterministic policy gradient-based algorithm to judiciously optimize the trajectory of the UAV in a continuous manner, while simultaneously satisfying practical constraints on both the UAV's and users' mobility. We compare the performance of our proposed design with various baselines and show how it can improve the average throughput performance in the presence of malicious jamming and converge at a faster rate.

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

The use of unmanned aerial vehicles (UAVs) is growing rapidly, for an expanding variety of applications. These UAVs range in size from a few centimeters to the size of passenger jet aircraft, and will fly a large assortment of missions that take them from the stratosphere to residential neighborhoods. Control and communications must hence be extremely reliable. In this paper we summarize some key characteristics of UAV communications, describe fundamentals and challenges for reliable UAV communications, some current efforts in the area, and provide a framework for analysis and modeling of a vital component in any UAV communication system: the air-ground channel. Features of this channel are discussed, with example references to the literature. Suggestions for future work in air-ground channel modeling are provided. The conference presentation of this work will include example results.

In this paper, the advantages of joint detection (JD) in a hybrid-duplex unmanned aerial vehicle (UAV) communication system (HBD-UCS) are investigated as a step toward addressing spectrum scarcity in UAV communications. Through extensive outage probability and finite signal-to-noise-ratio diversity gain analysis, we showed that the performance of JD is independent of the strength and the data rate of the inter-UAV interference signal. On the contrary, the successive interference cancellation (SIC) detector requires the data rate of the interfering UAV to be less than the ground station before meaningful performance can be seen. At the system level, it is observed that the half-duplex UAV communication system outperforms the HBD-UCS with JD at moderate and high SNR regimes, as the latter is constrained by self-interference at the full-duplex ground station. Finally, we investigated the multiplexing gain region and showed that the joint detector offers higher diversity gain over a wide range of multiplexing gains over the interference ignorant and the SIC detector.

In this paper, an active reconfigurable intelligent surfaces (RIS)-assisted Unmanned aerial vehicle (UAV) communication system is constructed, and a joint optimization method for UAV trajectory and system energy efficiency is proposed. In this optimization problem, firstly, the optimization problem of the phase shift matrix of the active RIS is solved by using the successive convex approximation algorithm. The transmit power optimization problem is solved by using the improved gradient descent method; Secondly, the communication energy efficiency is taken as the cost function, and the UAV trajectory is optimized by using the improved particle swarm optimization algorithm. Simulation results show that our algorithm is feasible for improving communication energy efficiency and UAV path planning.

Unmanned aerial vehicle (UAV) communication is recognized as a promising technology in next generation wireless communication system. As air-to-ground (A2G) communication is vulnerable to eavesdropping attacks by malicious ground adversaries, UAV communication systems face severe security challenges. This paper investigates the secrecy energy efficiency (SEE) maximization problem for UAV enabled communication systems. We aim to minimize energy consumptions required for secure communication via jointly optimizing scheduling, power allocation and UAV trajectory over a certain period of time. However, The formulated problem is non-convex and difficult to solve. To make the problem more tractable, we decompose it into three subproblems and propose an efficient iterative algorithm to obtain an appropriate solution. Simulation results show that compared with the benchmark schemes, our proposed method significantly improves the secrecy energy efficiency of UAV communication systems.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

With the growing popularity of unmanned aerial vehicles (UAVs), spectrum management is a pressing issue, particularly for multi-UAV systems. To this end, a hybrid-duplex (HBD) UAV communication system (UCS) consisting of a full-duplex (FD) enabled ground station (GS), and legacy half-duplex (HD) UAVs is proposed in this paper. To model the fading and shadowing environment commonly encountered in UAV communications, a mix of Rician and Rician shadowed fading is assumed. In particular, novel power series approximations of the Rician shadowed fading power probability density function (PDF), and cumulative distribution function (CDF) are presented, along with closed-form outage probability expressions. Performance analysis shows that the proposed HBD-UCS exhibits lower outage probability than the HD-UCS when shadowing is encountered at low signal-to-noise ratios (SNRs). Also, inter-UAV interference has a stronger influence on outage probability decay at low SNR regimes, with lower inter-UAV interference corresponding to a sharper decline in outage probability.

Consumer electronics can support sixth-generation (6G) systems and their services, including ultra-reliable and low-latency communications (URLLC). In this context, unmanned aerial vehicles (UAVs) have become increasingly popular because they can: be dynamically positioned, take advantage of channel gains, and communicate directly via line-of-sight. UAVs, on the other hand, are unable to maintain a stable flight for prolonged periods of time and suffer from jittering impairments caused by strong winds. As a result of atmospheric conditions and environmental interference, the perfect channel state information (CSI) becomes obsolete. The aim of this study is to propose a power-efficient resource allocation scheme for URLLC-enabled UAV communication systems under finite block lengths, imperfect CSIs, and adverse jittering effects caused by wind. This involves optimizing UAV positioning and blocklength distribution together. Additionally, we propose a perturbation-based semidefinite programming (SDP) approach to reduce the sum power and demonstrate that it outperforms fixed benchmark algorithms. As such, it can reach power savings up to 77.18% compared to fixed benchmark algorithms. Our extensive simulation results demonstrate that our approach performs similarly to the exhaustive search and has low complexity. Thus, the proposed method thus provides a practical power-efficient URLLC implementation for memory-constrained UAV networks.

Caching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems in multi-UAV-aided networks, where multiple UAVs can transmit contents to the assigned RUs. To minimize the energy consumption of the system, we develop a constrained optimization problem that simultaneously designs UAV trajectory, power allocation, content fetching, and content placement. Since the original minimization problem is a mixed-integer nonlinear programming (MINLP) problem that is difficult to solve, the optimization problem was first transformed into a semi-Markov decision process (SMDP). Next, we developed a new technique to solve the joint optimization problem: option-based hierarchical deep reinforcement learning (OHDRL). We define UAV trajectory planning and power allocation as the low-level action space and content placement and content fetching as the high-level option space. Stochastic optimization can be handled using this strategy, where the agent makes high-level option selections, and the action is carried out at a low level based on the chosen option’s policy. When comparing the proposed approach to the current technique, the numerical results show that it can produce more consistent learning performance and reduced energy consumption.

This paper investigates an unmanned aerial vehicle (UAV)-base station (BS) integrated network, where a UAV transmits downlink secrecy data to multiple ground cognitive users while a ground BS utilizes jamming beamforming to help the UAV counter the eavesdropping attack of a ground eavesdropper. In particular, we consider the imperfect eavesdropping and jamming channel state information (CSI) related to the eavesdropper. To maximize the minimum sum secrecy rate of the cognitive users, a robust secure transmission scheme is proposed. The UAV trajectory, UAV transmit power, BS beamforming, and user scheduling are jointly optimized with the constraints of the communication quality of the primary users served by the BS and the UAV available propulsion energy. We formulate a non-convex optimization problem which is challenging to be solved mathematically, and we utilize an alternating optimization technique to divide the original problem into three sub-problems, i.e., UAV trajectory sub-problem, transmit power sub-problem, and user scheduling sub-problem. Besides, they can be solved by the successive convex approximation, semi-definite relaxation and S-procedure, and bivariate relaxation methods, respectively. Moreover, we explore the impact of different parameters of the proposed transmission scheme on the minimum sum secrecy rate of the cognitive users, and verify the superiority of the proposed robust secure transmission scheme design.