In this work, we investigate content delivery and target sensing problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks where UAVs are allowed storing user-requested contents, delivering the content to users and performing target sensing as well. To jointly address the performance of content transmission and target sensing, we define utility function and formulate the UAV deployment, communication and sensing precoder design problem as a constrained utility maximization problem. As the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we transform it into two subproblems, namely, user grouping and UAV deployment subproblem, and communication and sensing precoder design subproblem, and solve the two subproblems by using an alternate iteration-based algorithm. Specifically, we first design a mean-shift-based user grouping strategy which divides users into different groups and then propose a UAV deployment strategy based on successive convex approximation (SCA)-based iterative algorithm and the first order Taylor expansion method. To solve communication and sensing precoder design subproblem, we propose a two-layer penalty-based SCA algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this work, we investigate content caching problem in unmanned aerial vehicle (UAV)-enabled networks where UAVs are allowed to store user requested content so as to enhance the content transmission performance of users. To predict user content request, we propose a bidirectional long short-term memory-based algorithm. Then, the content transmission delay of users is examined and the UAV deployment, content caching and resource allocation problem is formulated as an overall content fetching delay minimization problem. As the formulated problem is difficult to solve, we transform it into two subproblems which are solved iteratively. To tackle the UAV deployment and content caching subproblem, we first design a modified K-means-based clustering scheme and then propose a UAV deployment strategy by using quadratic transformation. To solve subcarrier and power allocation subproblem, we apply Lagrangian dual method to determine power allocation strategy and propose a Kuhn-Munkres algorithm-based subcarrier allocation strategy. Simulation results demonstrate the effectiveness of the proposed algorithms.

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.

In this research, we investigate the long-term secure content delivery problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks. We consider that ISAC-assisted UAVs are allowed to store user-requested contents, provide content delivering service to users, and perform eavesdropper detection. However, the openness of UAV-enabled networks makes the content delivery network more susceptible to security threats. To address the eavesdropper detection, we propose a Cramér-Rao Lower Bound (CRLB) and an extended Kalman Filter (EKF)-based location estimation algorithm. We then examine the secrecy throughput of users and formulate the joint user association, UAV deployment, content caching, communication, and sensing beamforming problem as a long-term secure throughput maximization problem. As the formulated problem is a mixed-integer non-linear programming problem (MINLP) and cannot be solved conveniently, we decompose it into two subproblems, namely, a long-timescale content caching subproblem, and a short-timescale user association, UAV deployment, communication and sensing beamforming subproblem. To solve the subproblems, we transform it into a Markov decision process (MDP) and we propose an attention-based hierarchical deep reinforcement learning (HDRL) with an action mask and design a double deep Q-network (DDQN) algorithm to obtain the long-timescale and an attention-based DDQN with an action mask for short-timescale strategies. Specifically, we first obtain a long-timescale strategy for content caching. Given the long-timescale strategy, we then obtain the short-timescale user association, UAV deployment, communication, and sensing beamforming strategy. Simulation results demonstrate the effectiveness of the proposed algorithms.