Abstract: Aiming at the challenges of real-time generation of high-precision radio maps (RMs) in low-altitude multi-UAV networks, including the rapidly changing environment, limited computing capability of individual UAVs, and edge computing overload caused by concurrent requests from multiple UAVs, this paper proposes an air-ground collaborative RM shared generation mechanism and a joint optimization method. First, by exploiting the RM generation capability of conditional diffusion models in complex propagation environments, scenario information such as building distributions and radiation-source locations is encoded into conditional prompts. At the ground edge servers, UAV requests are clustered according to the similarity among their scenario conditions. For similar requests within the same cluster, a representative medoid sample is selected as the shared condition to perform the prefix denoising process of the diffusion model, after which each UAV completes the subsequent personalized denoising process based on its own scenario condition. In this way, intermediate computations among similar requests can be reused, thereby reducing redundant inference overhead. Second, by comprehensively considering the air-ground communication latency between UAVs and base stations, the shared denoising computation latency at edge servers, and the impact of differences among request conditions on RM generation quality, a mixed-integer nonlinear programming model is established to coordinate the trade-off between RM generation quality and end-to-end system latency through the joint optimization of the UAV-base station association strategy and the intra-server clustering structure. To address the strong coupling among the decision variables and the high computational complexity of the optimization problem, an Association-Clustering Collaborative Optimization (ACCO) algorithm is proposed. In the outer layer, a genetic algorithm incorporating an elitism-preserving strategy is employed to globally optimize the association strategy. In the inner layer, the optimal number of clusters and medoid samples for each server are adaptively determined based on the silhouette coefficient and hierarchical clustering. Through iterative optimization between the outer and inner layers, the service association strategy and the shared denoising structure are jointly optimized. Simulation results show that, compared with simulated annealing, GAI-IoV, random association, local computing, and STACKING, the proposed method reduces the average system computation latency by approximately 13.7%, 9.5%, 18.4%, 42.2%, and 9.9%, respectively, while maintaining high and stable RM generation quality. Under different numbers of UAVs and edge servers, as well as different server computing capabilities, the proposed method demonstrates favorable latency performance, generation stability, and network scalability. Furthermore, a physical test platform is constructed to evaluate the proposed shared denoising mechanism in real-device and wireless communication environments. The test results verify the feasibility of the proposed air-ground collaborative RM generation method, which can provide a technical reference for real-time electromagnetic situational awareness in highly dynamic low-altitude scenarios and the coordination of large-scale UAV networks.

摘要： 针对低空多无人机网络中环境动态变化导致高精度无线电地图(Radio Map，RM)实时生成困难、无人机单机算力受限以及多机并发请求引起边缘计算过载等问题，本文提出了一种面向低空多无人机的空地协同无线电地图共享生成机制及联合优化方法。首先，利用条件扩散模型在复杂传播环境下的RM生成能力，将建筑物分布、辐射源位置等场景信息构造成条件提示词，并在地面边缘服务器端依据不同无人机请求之间的场景相似性进行聚类。对于同一簇内的相似请求，选取具有代表性的类心样本作为共享条件，统一执行扩散模型前缀去噪过程，再由各无人机结合自身场景条件完成后续个性化去噪，从而复用相似请求之间的中间计算结果，减少重复推理开销。其次，综合考虑无人机与基站之间的空地通信时延、边缘服务器共享去噪计算时延以及请求条件差异对RM生成质量造成的影响，构建了联合无人机-基站关联策略与服务器内部聚类结构的混合整数非线性规划模型，以实现RM生成质量与系统端到端时延之间的协调优化。针对该问题变量耦合紧密、求解复杂度高的特点，提出了一种关联-聚类协同优化算法(Association-Clustering Collaborative Optimization，ACCO)。其中，外层采用引入精英保留策略的遗传算法全局优化关联策略，内层基于轮廓系数和层次聚类自适应确定各服务器的最优簇数及类心样本，通过内外层迭代实现服务关联与共享去噪结构的协同优化。仿真实验结果表明，本文方法生成RM的系统平均计算时延与模拟退火、GAI-IoV、随机关联、本地计算和STACKING等算法相比，平均时延分别降低约13.7%、9.5%、18.4%、42.2%和9.9%，同时保持较高且稳定的RM生成质量。在不同无人机数量、边缘服务器数量及服务器计算能力条件下，所提方法均表现出较好的时延性能、生成稳定性和网络扩展能力。进一步地，本文搭建了实物测试平台，对所提共享去噪机制在真实设备和无线通信环境中的运行效果进行测试，验证了所提空地协同RM生成方法的可行性，可为低空高动态场景下的实时电磁态势感知和大规模无人机网络协同提供技术参考。