AI-Native Collaborative Content Sharing in Blockchain-Empowered UAV-Assisted D2D Networks

The increasing demand for high-quality digital content has driven the growth of content exchange among mobile users (MUs) via device-to-device (D2D) communication. However, MUs often face challenges such as limited storage, low computational power, and short battery life, making it very difficult to meet the rising demands for content sharing. UAV-assisted D2D communication has emerged as a promising solution, integrating aerial and ground networks to enable efficient content caching and distribution while reducing latency and communication costs. However, the high mobility of MUs and increasing content size make it challenging to maintain stable communication links between MUs. This increases the complexity of content distribution, caching, and resource allocation in D2D content-sharing frameworks, resulting in higher latency, fluctuating resource demands, and lower QoS, ultimately affecting system efficiency and reliability. To address these challenges, we propose an adaptive and collaborative content-sharing and resource allocation framework integrating multi-agent twin delayed deep deterministic policy gradient (MATD3), blockchain, and a multiple-round distributed double auction (MDDA). MATD3 enables dynamic decision-making for content caching and resource allocation based on user behavior and mobility, while blockchain ensures secure, transparent, and tamper-proof content-sharing transactions. Furthermore, we propose the MDDA-based incentive scheme that allows content sellers, buyers, and the auctioneer to interact and establish optimal pricing strategies. This optimizes the content-sharing capability of MUs and edge devices, enhancing the cache hit rate and average system utility. Finally, the extensive simulation results demonstrate that our proposed scheme outperforms the benchmarks in enhancing cache hit rates, communication latency, and average system utility.