Research On Revenue Optimization Strategy Of MEC Operator In The Satellite-Terrestrial Double Edge Computing System

The fifth generation(5G)mobile communication network is being put into commercial use worldwide,yet its coverage and performance indices are still insufficient to meet service requirements in some application scenarios.One of the core visions of the future 6th Generation(6G)mobile communication network is satellite-terrestrial integrated communication aimed at realizing global seamless coverage of communication services.Due to its advantages in communication capabilities,Low Earth Orbit(LEO)satellite network has become a key component of the satellite-terrestrial integrated network.It not only eliminates blind spots of terrestrial network coverage,but also acts as an alternative when terrestrial network is congested or malfunctions.In order to further meet the Quality of Experience(QoE)requirements of various emerging services for data processing,storage,the deployment of Mobile Edge Computing(MEC)resources is entailed in the satellite-terrestrial integrated network.In the satellite-terrestrial integrated network,terrestrial base-station MEC servers are generally deployed by telecom operators,while LEO satellite MEC servers are deployed by satellite communication operators.Since the relationship between satellite communication network and terrestrial communication network is currently in the stage of integration interwoven with competition,there exists "cooperative" or "non-cooperative" relationship between terrestrial MEC and LEO satellite MEC.Abundant pieces of literature at home and abroad have studied MEC-related issues like computing offloading,edge caching,whereas the research on revenue optimization of MEC operator is relatively scarcer,especially for the scenario of satellite-terrestrial integrated network.In view of the importance of pricing strategy to the MEC market,this dissertation studies the revenue optimization problem of MEC operators in the satelliteterrestrial double edge "cooperative" and "non-cooperative" sub-scenarios.For the satellite-terrestrial double edge "cooperative" sub-scenario: First,with respect to the MEC operator’s revenue,base station and satellite computing,the MEC operator’s revenue optimization function and constraints of computing,communication resource and task execution delay are established respectively.Secondly,a joint optimization strategy of base station task execution order and LEO satellite resource allocation is proposed to guarantee more tasks are completed at the base station and reduce satellite computing cost while meeting the QoE requirements of computing tasks.Finally,simulation results indicate that the proposed strategy can effectively improve the MEC operator’s revenue.For the satellite-terrestrial double edge "non-cooperative" sub-scenario: First,with respect to MEC operators’ revenue as well as base station computing and satellite computing,MEC operators’ revenue functions and cost functions of Internet of Things(IoT)nodes are established respectively.Secondly,the Stackelberg game model is adopted to formulate the dynamic game relationship between IoT nodes and base station MEC as well as LEO satellite MEC operator.Then backward induction method is used to tackle the problem of operator pricing strategy and node offloading strategy,and convex optimization method is used to tackle the problem of computing resource allocation.Finally,simulation results demonstrate the applicability of satellite-terrestrial double edge computing to reduce IoT node costs and the pricing strategy of MEC operators in this scenario.