Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile nodes, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the quality of service (QoS) is a challenging task. In this paper, with recent advances in scalable video coding (SVC), we study energy-efficient secure video streaming in rotary-wing UAV-enabled wireless networks. By jointly optimizing video levels selection, power allocation, and the UAV’s trajectory, we intend to maximize the long-term energy efficiency that is defined as the ratio of video quality to power consumption. Meanwhile, secrecy timeout probability is considered as a constraint cost to guarantee time delays requirements in a long run perspective. Our problem is modeled as a constrained Markov decision process (CMDP) and solved by safe deep Q-learning network (safe-DQN), where a safe policies set induced by constructing a Lyapunov function is dynamically adjusted to satisfy the constraint conditions of the CMDP. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm compared with other existing reinforcement learning algorithms.

The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.

Unmanned aerial vehicle (UAV) relay can effectively improve the coverage and performance of land communications, which makes it prospective to be deployed in various applications. However, there exist some practical challenges we have to face in UAV-enabled communication system designing, such as secrecy and wiretapping threats due to the air-to-ground line-of-sight communication link, and energy consumption issues because of the limitation in energy-constraint. To deal with these two key issues, we develop a novel UAV swarm assisted multi-hop mobile relay system which can be applied in special scenarios with severe blockage or long distance to enhance communication performance. An effective cooperative transmission scheme is proposed to address the security issues in a physical layer security perspective, where several UAVs serve as multi-hop relays forwarding information between ground users and the other UAVs are employed as friendly jammers confusing the ground eavesdroppers. The minimum secrecy energy efficiency (MSEE), namely the minimum achievable secrecy rate per energy consumption unit over legitimate links, is designed as the performance indicator to quantify the secure and energy-efficient transmission. Subject to information-causality, transmit power, and mobility constraints, the UAV swarm assisted MSEE maximization transmission scheme is formulated into a complicated non-convex problem. To solve the non-convex fractional optimization problem, we decouple the original problem into sub-problems and further propose an efficient algorithm by applying the block coordinate descent method, successive convex approximation (SCA) techniques, and Dinkelbach method. Numerical results demonstrate that the proposed scheme achieves considerable performance enhancement on MSEE compared with benchmark schemes.

Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between uncrewed aerial vehicles’ (UAVs) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.

Integrated sensing and communication (ISAC) is considered a key technology supporting Beyond-5G/6G (B5G/6G) networks, which allows the spectrum resources to be used for both sensing and communication. In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled ISAC scenario, where the UAV sends ISAC signals to communicate with multiple users (UEs) and senses potential targets simultaneously, and a reconfigurable intelligent surface (RIS) is deployed to enhance the communication performance. Aiming at maximizing the sum-rate throughput of the system, we formulate the joint optimization problem of the trajectory and the beamforming matrix of the UAV, the passive beamforming matrix of the RIS. Currently, many researchers are working on using deep reinforcement learning (DRL) to address such problems due to its non-convex nature; however, as the environment becomes increasingly complex, high-dimensional state space and action space lead to a decrease in the performance of DRL. To tackle this issue, we propose a novel hierarchical deep reinforcement learning (HDRL) framework to solve the optimization problem. Through decomposing the original problem into the trajectory optimization problem and the sum-rate throughput optimization problem, we adopt a hierarchical twin-delayed deep deterministic policy gradient (HTD3) structure to optimize them alternately. The experimental results demonstrate that the obtained system sum-rate throughputs of the proposed HDRL with an HTD3 structure are 33%, 50%, and 10% higher than those obtained by TD3, twin-TD3 (TTD3), and TD3 with hovering only (TD3HO), respectively.