Partial observation learning-based task offloading and spectrum allocation in UAV collaborative edge computing

Capable of flexibly supporting diverse applications and providing computation services, the Mobile Edge Computing (MEC)-assisted Unmanned Aerial Vehicle (UAV) network is emerging as an innovational paradigm. In this paradigm, the heterogeneous resources of the network, including computing and communication resources, should be allocated properly to reduce computation and communication latency as well as energy consumption. However, most existing works solely focus on the optimization issues with global information, which is generally difficult to obtain in real-world scenarios. In this paper, fully considering the incomplete information resulting from diverse types of tasks, we study the joint task offloading and spectrum allocation problem in UAV network, where free UAV nodes serve as helpers for cooperative computation. The objective is to jointly optimize offloading mode, collaboration pairing, and channel allocation to minimize the weighted network cost. To achieve the purpose with only partial observation, an extensive-form game is introduced to reformulate the problem, and a regret learning-based scheme is proposed to achieve the equilibrium solution. With retrospective improvement property and information set concept, the designed algorithm is capable of combating incomplete information and obtaining more precise allocation patterns for diverse tasks. Numerical results show that our proposed algorithm outperforms the benchmarks across various settings.