With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.

The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.

With the advantages of seamless coverage and ubiq-uitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services to reduce End-to-End (E2E) delay and alleviate the network traffic for future 6G applications including autonomous driving, eHealth, and metaverse. However, the ultra-density of LEO satellites complicates the selection of caching nodes, while the heteroge-neous caching hardware and communication environments make optimization of content deployment highly difficult. To address these issues, we propose an intelligent hierarchical caching and asynchronous updating scheme. Specifically, a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme is first adopted to select the caching LEO satellites to reduce the system propagation delay. Then, the Multi-Agent Reinforcement Learning-based Hi-erarchical Caching and Asynchronous Updating (MARL-HCAU) strategy is proposed to meet caching service demands. Simulation results illustrate that compared with the benchmarks, the overall cache hit ratio increases by 14.2 % with the reduced packet drop rate and transmission delay by 8.78 % and 0.94s, respectively.

Abstract Ultra‐dense networking is considered an effective solution to achieve high capacity in 5G networks. However, the densely distributed base stations (BSs) in ultra‐dense networks (UDNs) make the inter‐cell interference much more serious than that in traditional cellular networks. Therefore, it is important to mitigate inter‐cell interference in the UDNs to improve network performance. To tackle this problem, we propose a novel virtual resource mapping algorithm that includes a resource reservation (RR) algorithm and a real‐time resource embedding (RE) algorithm. Specifically, according to the number of services predicted by a dynamic service model, the RR algorithm is proposed to determine the sets of multiplexing BSs in the next time cycle and reserve channel resource required by each BS. Then, to further reduce inter‐cell interference, the real‐time RE algorithm is proposed to allocate the channel resource in real time. Finally, simulation results show that the proposed algorithm has better performance in terms of signal‐to‐interference‐plus‐noise ratio and acceptance ratio, compared to the existing algorithms, such as the frequency reuse channel allocation algorithm and inter‐cell interference coordination algorithm.