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

作　　者：Chaoqiong Fan Xinyu Wu Bin Li Chenglin Zhao 

机构地区：[1]School of Artificial Intelligence,Beijing Normal University,Beijing,100875,China [2]Guangdong Key Laboratory of Intlligent Information Processing and Shenzhen Key Laboratory of Media Security,Shenzhen,51860,China [3]School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing,100876,China

出　　处：《Digital Communications and Networks》2024年第6期1635-1643,共9页数字通信与网络(英文版)

基　　金：supported by the Natural Science Foundation of China(NSFC)under Grant 62101051;the Guangdong Key Laboratory of Intelligent Information Processing;Shenzhen Key Laboratory of Media Security,Shenzhen 518060,China。

摘　　要：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.

关 键 词：UAV networks Edge computing Task offloading Spectrum allocation Partial observation Regret learning 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN929.5[电子电信—通信与信息系统]