Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous areas.Therefo...Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous areas.Therefore,the aerial networks are the key framework for the future communications,in which the aerial vehicle could act as the access point.Additionally,Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks.In this paper,we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing,power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks,namely CR-MEC aerial networks.Explicitly,this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems.In the conceived CR-MEC aerial networks,we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory,aiming to minimize the system’s energy consumption.At the first step of UPCOMA,multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs.Moreover,the stable user pairs would match to a high altitude platform who act as the base station,which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA.Additionally,a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines.Furthermore,the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity.Meanwhile,the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.展开更多
基金supported by National Nature Science Foundation of China (No.62101450)in part by the Shenzhen Science Technology Program,China (No.JCYJ20210324121006017)in part by Key R&D Plan of Shaan Xi Province,China (No.2023YBGY037)。
文摘Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous areas.Therefore,the aerial networks are the key framework for the future communications,in which the aerial vehicle could act as the access point.Additionally,Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks.In this paper,we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing,power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks,namely CR-MEC aerial networks.Explicitly,this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems.In the conceived CR-MEC aerial networks,we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory,aiming to minimize the system’s energy consumption.At the first step of UPCOMA,multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs.Moreover,the stable user pairs would match to a high altitude platform who act as the base station,which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA.Additionally,a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines.Furthermore,the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity.Meanwhile,the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.
