Aerial platform-based network has been invoked as an appealing assistance for terrestrial cellular networks. Caching at edge UAVs is an effective emerging solution for relieving the wireless backhaul congestion and reducing content fetching latency for hotspot areas. This paper investigates a non-orthogonal multiple access (NOMA)-enabled multi-UAV collaborative caching network, where the multimedia contents are delivered from UAVs to ground users. UAVs cache popular contents and collaboratively share with each other. The optimization problem of joint caching decision, 3D trajectory planning and spectrum resource allocation is formulated for minimizing the system content retrieving delay. It is an NP-hard mixed integer nonconvex optimization issue with extra difficulty that the content popularity is highly dynamic. To tackle this challenging problem, we decouple the original issue into two subproblems: (1) UAV collaborative caching decision making and (2) joint optimization of UAV trajectory planning, power allocation, and channel reusing. We develop a multi-agent proximal policy optimization (MAPPO)-based algorithm for the former to achieve the maximum weighted content hit ratio and propose a matching-DRL solution for the latter. Numerical evaluations confirm that the proposed approach is capable of rapid convergence and achieves superior performance in terms of content hit ratio, content retrieving delay, and system throughput compared to other benchmark algorithms.

Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations (HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a feasible solution to achieve seamless network coverage for remote IoT devices in future 6 G era. However, since the number of terminals increases exponentially, it is essential to improve spectrum efficiency and throughput of the system. In addition, the limited on-board energy storage of UAVs and finite battery of IoT terminals make the system energy efficiency (EE) a new concern. To cope with the above mentioned challenges, in this study we first put forward a clustered-NOMA (C-NOMA)-enabled heterogeneous AGIN model for remote areas including one HAPS for backhaul and multiple UAVs for access, where C-NOMA is used to obtain both improved system throughput and terminal complexity. Then, we study the joint UAV trajectory plan and resource allocation problem in order to maximize the system EE. Since it is a mixed integer nonlinear programming (MINLP) issue coupled with transmission power, subchannel allocation, UAV trajectory and speed control, this problem is decoupled into two subproblems and solved iteratively. For the first one, the optimal channel and power strategy is obtained by our subchannel allocation and power control for AGIN EE (SAPAE) maximum algorithm according to Lagrange dual decomposition method. For the second, for transforming the nonconvex problem into a convex one, we provide the successive convex approximation-based UAV trajectory and speed optimization for AGIN EE (SUTSAE) maximum algorithm, and then the near-optimal UAV trajectory and flight speed are obtained. We conduct extensive experiments to compare with other benchmark methods. It is shown that the proposed approach is better in EE and spectrum efficiency.

The explosion of information data in the power Internet of Things (PIoT) poses difficulties for data acquisition and computation of power devices located in remote areas with limited computing capability and energy. Therefore, we propose a multi-unmanned aerial vehicle (UAV) assisted wireless power transmission (WPT) enabled space-air-ground PIoT framework, in which the UAVs provide energy for data acquisition of power devices through WPT, and use mobile edge computing (MEC) technology to calculate the data. Finally, they act as relays to forward the data to the low earth orbit (LEO) satellite. For keeping the data fresh, we jointly optimize the number of UAV hovering positions, the hovering positions, the connection between UAVs and power devices, the energy transmission and data acquisition time of power devices, the computing resources, flight speed and trajectories of UAVs, aiming to minimizing the average age of information (AoI) of power devices. Through theoretical derivation the problem is decoupled into five independent subproblems, which are solved using improved K-means algorithm, Lagrangian dual decomposition method, interior point method, adaptive optimization, and Q-Learning algorithm, respectively. Extensive numerical simulations demonstrate the superior properties of our solution comparing to benchmark algorithms.

Accompanied by the construction of new power system with renewable energy, like the offshore wind power and desert photovoltaic power, terrestrial ground 5G is no longer able to fulfill the communication requirement of industrial power IoT (IPIoT) with tremendous equipment in remote areas. Under this circumstances, we put forward the NOMA-enabled space–air–ground integrated IPIoT network (SAGIN-IPIoT) model, with a satellite to achieve wide coverage, and multiple UAVs to strengthen hot spot communication. NOMA is leveraged to improve system throughput by allowing the common frequency resource shared among multiple users. An energy-efficient (EE) maximization problem is formulated to jointly optimize subchannel and terminal power. However, since the objective involves both continuous and binary variables, it is a mixed integer nonlinear programming (MINLP) issue. Thus, we decompose it into two subproblems, which are respectively solved by matching game and Lagrange dual method with low complexity. According to the theoretical analysis and simulations, we can conclude that the method has better performance than the benchmark method.

The isolation between the microstrip patches has a great significance to examine the performance of the multiple-input-multiple-output (MIMO) antennas. The patch antennas are placed on the top of 1.46 mm thick Rogers RO3003 substrate having a length of 60 mm, a width of 50 mm, and relative permittivity of 3. The distance between the resonators is 0.06λ and they are stimulated by two coaxial probes extended from the bottom ground layer. The defective ground structure of the H-shape slot is inserted on the bottom ground layer to achieve high isolation (mutual coupling reduction). The proposed MIMO antenna operates at 5.3 GHz frequency, which can be used for WiMAX, Wi-Fi, and future 5G services all over the world. The results of the designed structure have been simulated in a finite element method-based solver high-frequency structure simulator (HFSS). The simulated results show that the reflection coefficient (S11) and isolation (S21) at the desired frequency are −32 dB and −41 dB, respectively.

The emerging reconfigurable intelligent surface (RIS) is a prospective technique to modulate the wireless channel and improve performance, in which large amounts of passive elements manipulate independently, inevitably resulting in a high-dimensional optimization problem that is intractable to solve. With the aim to strike a balance between optimality and complexity for RIS assisted multi-user systems, in this paper, we formulate the achievable sum rate maximization problem under a novel RIS segmentation structure, where the distributions and sizes of each segmentation can be adaptively adjusted. Since the formulated optimization problem considering the quality of service (QoS) requirements for the users is non-convex, we suggest a computationally-efficient approach to derive an optimal solution by exploiting fractional programming, successive convex approximation (SCA), greedy algorithm, and alternating optimization. Finally, numerical simulations reveal that the proposed optimization design enables RIS to configure by grouping elements into some sub-surfaces without significant performance degradation while with much lower computational complexity than conventional element-wise optimization RIS. Moreover, our proposed adjustable segmentation outperforms the fixed one employing the determined positions and equal number of reflecting elements in each sub-surface. Additionally, the results demonstrate that the optimization of segmentation is much more significant than the phase shift optimization, showing the superiority and practical significance of the sub-surface segmentation strategy.