The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and edge model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing cloud model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.

翻译：未来的第六代无线网络预计将通过通信与计算领域空-地一体化设施的部署，提供无处不在的覆盖，从而超越其前代网络。在此网络中，无人机等空中设施基于多模态数据进行人工智能计算，以支持包括监控和环境构建在内的多样化应用。然而，这些跨领域的推理与内容生成任务需要大型AI模型，对计算能力要求极高，从而给无人机带来了重大挑战。为解决此问题，我们提出了一种集成的边缘-云模型演进框架，其中无人机作为边缘节点进行数据收集和边缘模型计算。通过无线信道，无人机与地面云服务器协作，为边缘无人机提供云模型计算和模型更新。在有限的无线通信带宽下，所提框架面临着边缘无人机与云服务器之间信息交换调度的挑战。为此，我们提出了联合任务分配、传输资源分配、传输数据量化设计以及边缘模型更新设计，通过最大化平均精度均值来提升空-地一体化边缘-云模型演进框架的推理精度。我们推导了所提框架mAP的闭式下界，并据此优化了mAP最大化问题的解。基于视觉分类实验结果的仿真一致表明，在各种通信带宽和数据规模下，所提框架的mAP均优于集中式云模型框架和分布式边缘模型框架。