In this paper, we propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and edge computing services to mobile users (MUs) in proximity, alongside their target sensing capabilities by using multi-input multi-output arrays. For the purpose of enhance the computation efficiency, we consider task compression, where each MU can partially compress their offloaded data prior to transmission to trim its size. The objective is to minimize the weighted energy consumption by jointly optimizing the transmit beamforming, the UAVs' trajectories, the compression and offloading partition, the computation resource allocation, while fulfilling the causal-effect correlation between communication and computation as well as adhering to the constraints on sensing quality. To tackle it, we first reformulate the original problem as a multi-agent Markov decision process (MDP), which involves heterogeneous agents to decompose the large state spaces and action spaces of MDP. Then, we propose a multi-agent proximal policy optimization algorithm with attention mechanism to handle the decision-making problem. Simulation results validate the significant effectiveness of the proposed method in reducing energy consumption. Moreover, it demonstrates superior performance compared to the baselines in relation to resource utilization and convergence speed.

翻译：本文提出了一种多无人机辅助的感知、通信与计算一体化网络。具体而言，具备三重功能的无人机能够利用多输入多输出阵列，在为邻近移动用户提供通信与边缘计算服务的同时，执行目标感知任务。为提升计算效率，我们考虑了任务压缩机制，即每个移动用户可在传输前对卸载数据进行部分压缩以减少数据量。目标是通过联合优化发射波束成形、无人机轨迹、压缩与卸载划分以及计算资源分配，在满足通信与计算间因果关系约束以及感知质量要求的前提下，最小化加权能量消耗。为解决该问题，我们首先将原问题重构为多智能体马尔可夫决策过程，通过引入异构智能体以分解MDP中的巨大状态空间与动作空间。随后，我们提出了一种结合注意力机制的多智能体近端策略优化算法来处理该决策问题。仿真结果验证了所提方法在降低能耗方面的显著有效性。此外，与基线方法相比，该方法在资源利用率和收敛速度方面均展现出更优的性能。