Unmanned aerial vehicles (UAVs) can be employed as aerial base stations to support communication for the ground users (GUs). However, the aerial-to-ground (A2G) channel link is dominated by line-of-sight (LoS) due to the high flying altitude, which is easily wiretapped by the ground eavesdroppers (GEs). In this case, a single UAV has limited maneuvering capacity to obtain the desired secure rate in the presence of multiple eavesdroppers. In this paper, we propose a cooperative jamming approach by letting UAV jammers help the UAV transmitter defend against GEs. To be specific, the UAV transmitter sends the confidential information to GUs, and the UAV jammers send the artificial noise signals to the GEs by 3D beamforming. We propose a multi-agent deep reinforcement learning (MADRL) approach, i.e., multi-agent deep deterministic policy gradient (MADDPG) to maximize the secure capacity by jointly optimizing the trajectory of UAVs, the transmit power from UAV transmitter and the jamming power from the UAV jammers. The MADDPG algorithm adopts centralized training and distributed execution. The simulation results show the MADRL method can realize the joint trajectory design of UAVs and achieve good performance. To improve the learning efficiency and convergence, we further propose a continuous action attention MADDPG (CAA-MADDPG) method, where the agent learns to pay attention to the actions and observations of other agents that are more relevant with it. From the simulation results, the rewards performance of CAA-MADDPG is better than the MADDPG without attention.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultra-high resolution immersive VR video with wireless user equipments such as head-mounted displays (HMDs), ultra-low-latency viewport rendering and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multi-access edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich THz communication is expected to enable ultra-high-speed wireless data transmission. In this paper, we propose to minimize the long-term energy consumption of a THz wireless access based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C) based joint optomization algorithm is proposed. Simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

Unmanned aerial vehicles (UAVs) will be extensively utilized in various Internet of Things (IoT) scenarios due to their flexible mobility and rapid on-demand deployment. It becomes necessary and urgent to investigate the secrecy performance of UAV IoT communication systems because of the open characteristics of wireless channels. In this paper, the physical layer security of UAV IoT communication systems is studied, in which a ground IoT device transmits some confidential messages to a UAV hovering in the air, while a random number of eavesdroppers are randomly positioned around the ground source. The ground-to-air channel is assumed to experience Rician fading for the case of line-of-sight propagation and Rayleigh fading for the case of non-line-of-sight propagation, respectively. In addition, in order to improve the security of the system, we also investigate the secrecy performance of the UAV IoT system with a friendly UAV that generates jamming signals to distract the eavesdroppers. Utilizing stochastic geometry theory, the cumulative distribution functions for the signal-to-interference-plus-noise ratioes of the main and eavesdropping links are derived, and then the analytical expressions of the secrecy outage probability and the average secrecy rate are obtained. Finally, the accuracy of the analytical results is verified by Monte Carlo simulation.

Intelligent Reflecting Surface (IRS) holds promise for enhancing wireless channel propagation, especially in high-frequency bands like millimeter-wave (mmWave) that are prone to significant propagation losses and blockage effects. However, channel estimation poses significant challenges due to passive nature of the IRS, high dimension of channel matrix, and need to consider superposition of direct channel between transceivers and reflective channel through the IRS. In this study, we explore adaptive and robust estimator for time-varying mmWave channels, considering the use of more general arbitrarily shaped IRS, in contrast to classical regular IRS elements arranged on a grid. Firstly, we establish a consistent-sparse-subspace relationship between direct and reflective channels using linearly modified atomic norm minimization. Secondly, we formulate joint estimation as a low-dimensional and robustly sparse matrix reconstruction programming, resolving efficiently through alternating optimization and alternating direction method of multipliers. Additionally, to reduce complexity, we propose a learning-based solver using fixed-point iteration and a cascaded deep-learning network framework. This solver can adjust its network depth according to the time-varying channel, achieving linear convergence and reduced complexity. Simulation results demonstrate that both proposed channel estimation methods outperform current state-of-the-art techniques.

Mobile edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and non-orthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this paper, we consider a NOMA-MEC based Internet of Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then we maximize the total energy saving also from two aspects, i.e., the device side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.