Research On Task Offloading Strategy For 6G Multi-access Edge Computin

As the key technology of the 6th Generation Mobile Communications(6G),Multiaccess edge computing(MEC)can provide high bandwidth and low latency computing services to better meet the computing needs of emerging 6G applications and achieve microsecond level rapid response.Applying MEC to solve the task offloading problem in6 G networks can effectively reduce network congestion and improve system resource utilization.In the 6G MEC system,designing efficient and reliable task offloading strategy is the key to meeting the service needs of terminal devices,improving the effectiveness of participants,and reducing system costs.Firstly,focusing on the task offloading problem in the overlapping area of 6G MEC network,a comprehensive analysis is conducted on the social connection of terminal devices,task delay constraints and system economic incentives.Using many-to-one matching game theory and Stackelberg game theory,a task offloading game model is established considering the interaction between terminal devices and edge servers during the task offloading process.The Segment Pricing Best Respond algorithm(SPBR)is proposed to obtain the equilibrium strategy of task offloading that maximizes the utility of terminal devices and edge servers.The simulation results show that the SPBR algorithm can improve the average utility of terminal devices and edge servers by approximately 35.5% and 30.3%,respectively.Secondly,focusing on the task offloading problem of edge cloud collaboration in 6G MEC networks,the delay constraints,energy consumption costs and economic incentives are comprehensively analyzed,the strategic interaction between terminal devices,edge servers and cloud servers is established as a three-stage Stackelberg game model.Using Newton interior point method and Stackelberg game theory,a Three-stage Equilibrium algorithm based on Backward Induction method(TEBI)is proposed,to achieve the equilibrium strategy of task offloading that maximizes the utility of system participants.The simulation results show that the TEBI algorithm can effectively reduce task execution latency,resulting in an average improvement of approximately 31% in the utility of system participants.Finally,focusing on the task offloading problem of the integration of space-air-ground in 6G MEC networks,a collaborative optimization model for association,task offloading,and power control is established by comprehensively analyzing the task tolerance delay constraints,terminal device service requirements,and differences in computing capability of aerial equipment.Using generalized Benders decomposition and successive convex approximation methods,a Low-cost Task offloading and Resource allocation Collaborative optimization algorithm(LTRC)is proposed,and a collaborative optimization strategy for minimizing system cost is obtained.The simulation results show that the LTRC algorithm can converge quickly and reduce the system cost by approximately 18%.