Realizing digital-twin services is one of promising applications in 6 G mobile communication and network scenarios. In addition, the use of unmanned aerial vehicles (UAVs) is essential for enabling the services even in the extreme areas where humans cannot reach. In this emerging scenario, it is necessary to design collaborative algorithms for autonomous UAV trajectory control and a centralized computing platform (e.g., cloud) in digital-twin networks. For this system, it is required to build energy-efficient algorithms due to the power-hungry nature in UAVs. Based on this requirements and system characteristics, this paper proposes autonomous UAV charging algorithms and systems where the UAVs are classified into two types, i.e., cluster UAVs (for main image recording operations in digital-twin services, and some of them take the roles of mobile edge computing) and charging UAVs (for charging the cluster UAVs). Our proposed charging should be (i) fully distributed for practical, scalable, and low-overhead operations and (ii) trustworthy for secure and privacy-preserving computation; where these are essential for collaborative operations. Therefore, a novel auction-based charging algorithm for UAV-based digital-twin networks is proposed in order to realize the distributed and truthful operations, which cannot be achieved by the convex optimization-based centralized algorithms in the literature. Our performance evaluation verifies that the proposed algorithm achieves performance improvements (at most 15.53%).

With the rapid advancement of autonomous vehicles, there is a growing demand for infotainment services that require high-quality and delay-sensitive video content. This paper proposes a multi-agent deep reinforcement learning (MADRL) approach for video cache replacement and delivery in mobility-aware vehicular networks. Unlike previous studies, our work focuses on videos of finite lengths and incorporates dynamic cache replacement, optimizing this alongside the delivery of individual video chunks. Considering the challenge of obtaining complete network state information at a central unit (e.g., macro base station), we adopt a MADRL framework to enable roadside units (RSUs) to autonomously decide on video caching and delivery strategies, leveraging partial information from neighboring RSUs. We evaluate the proposed method using various quality-of-service (QoS) metrics. Extensive simulation results demonstrate that our scheme consistently delivers high average video quality while reducing playback stalls, replacement costs, and backhaul usage.