The purpose of this research is to develop and evaluate an adaptive cooperative spectrum-sharing framework for Hybrid Satellite-Terrestrial Networks (HSTNs), incorporating realistic hardware impairments and flexible Amplify-and-Forward (AAF) and Decode-and-Forward (DAF) relaying to enhance reliability, spectral efficiency, and power allocation. This study employs a mathematical and simulation-based approach to evaluate Overlay Cognitive Hybrid Satellite-Terrestrial Networks (OCHSTNs) under realistic hardware impairments and fading conditions. Adaptive Relay Protocol (ARP) is analyzed for both AAF and DAF schemes, incorporating Shadowed-Rician and Nakagami-m channels with Additive Gaussian Noise. Closed-form Outage Probability (OP) expressions for primary and secondary networks are derived, and Monte-Carlo simulations validate analytical results. The method also examines Relay Cooperation Ceiling (RCC) and Data Link Ceilings (DLC) to assess network reliability and spectrum-sharing efficiency. The research demonstrates that ARP significantly improves outage performance and spectral efficiency in HSTNs under realistic hardware impairments. DAF relaying exhibits superior resilience compared to AAF, maintaining higher data rates and reliability. Critical ceiling effects, including RCC and DLC, are identified, highlighting performance limitations at high thresholds. Flexible relaying protocols effectively mitigate Hardware Deficiencies (HDs), ensuring robust primary and secondary network operation while optimizing power allocation and spectrum sharing. The study provides practical guidance for designing resilient hybrid satellite-terrestrial networks, showing that adaptive DAF relaying and flexible spectrum-sharing strategies enhance reliability, mitigate hardware impairments, and optimize network efficiency.

The emergence of unmanned aerial vehicles (UAVs) ushers in a new era for mobile edge computing (MEC), significantly expanding its range of service and potential applications. Due to the limited storage capacity and energy budget of UAVs, it is crucial to determine a reasonable service caching and task offloading strategy. Service caching means that task-related programs and the associated databases are cached on edge servers. In this paper, we consider the time latency and energy consumption caused by frequent changes to the service caching, aiming to jointly optimize the computational offloading, resource allocation, and service caching in multi-UAV assisted MEC systems at different time scales. The objective of this optimization is to reduce the overall system delay while staying within the energy limitations of both the UAVs and ground devices. An improved service caching policy (SCP) is proposed, which is based on task popularity and utilizes the greedy dual size frequency (GDSF) algorithm. The SCP is combined with the twin delayed deep deterministic policy gradient (TD3) algorithm to propose an innovative dual timescale TD3 (DTTD3) algorithm. The numerical outcomes obtained from a substantial number of simulation experiments demonstrate that DTTD3 outperforms existing benchmark methods in terms of convergence and parameter optimization.

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context of mobile edge computing (MEC) has garnered significant interest within the sector. However, UAVs also face constraints in terms of resources like storage and computational power. Therefore, it is vital to develop effective UAV assistance solutions to provide long-term demands of in-network services. The dynamic scheduling and computation offloading of UAVs is the subject of this paper. Specifically, we propose a deep deterministic policy gradient algorithm based on a greedy strategy (DDPGG) to jointly optimize dynamic scheduling, device association, and task allocation of UAVs, with the goal of minimizing the weighted sum of total system energy consumption and time delay. The problem is formulated as a nonlinear programming problem involving mixed integers. The simulation results demonstrate that the DDPGG algorithm we have proposed exhibits a higher level of performance in comparison to its competitors.

Guaranteeing network transmission is one of the most challenging issues in industrial informatization. In the industrial sites without proper networking infrastructure, by deploying unmanned aerial vehicles (UAV), transmission-oriented cyber-physical system (CPS) is an excellent candidate to exploit to provide transmission. In this article, we focus on establishing deterministic network transmission (DNT) using UAV-based CPS, complying with the principles in time sensitive network/deterministic networking in industrial internet. The software-defined networking (SDN) paradigm is adopted for UAVs-based CPS to achieve global optimization. First, we build the coordinate-based global topology view in the SDN controller to manage and control UAVs by integrating UAVs into the view and applying the network positioning method. Then, we introduce a hop-limited time synchronization approach to improve accuracy by reducing synchronization deviation. Last, based on the view, a geometric multipath generating method is proposed to enhance reliability by reducing the joint degree of multiple paths and facilitating convergence. The extensive simulation experiments show that our proposed UAV-CPS allows DNT to provide better reliability with reduced latency.