Recent advances in satellite technologies and non-terrestrial networks have made multi-satellite cooperative communications a promising approach for enhancing quality of service in satellite networks. To meet the ever-increasing demands for global coverage, ultra-reliable connectivity, and low-latency services, cooperation among multiple satellites is a key enabler for overcoming the limitations of single-satellite service systems. With its growing importance, both applications and opportunities are simultaneously manifesting. This paper presents a comprehensive survey of research on multi-satellite cooperative communications. In particular, we first introduce architectures for multi-satellite cooperative networks, including intralayer, inter-layer, and cross-layer architectures. Subsequently, we comprehensively analyze and discuss their operating modes, including cooperative relaying, coordinated joint transmission, cooperative reception, beamforming, and cooperative interference management. Additionally, optical inter-satellite links are being considered for cooperative communications to enhance satellite interoperability. We then introduce the challenges and corresponding solutions for implementing multi-satellite cooperative communications. Furthermore, we explore enabling technologies for multi-satellite networks, including multi-connectivity, cooperative satellite beam-hopping, multi-satellite beamforming, traffic offloading, and management of satellite topology and constellation graphs. To further enhance the evaluation of the multi-satellite network, we discuss performance metrics, analysis tools, optimization methods, and validation. Finally, we outline several compelling research directions for future multi-satellite cooperative communication networks.

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay.