Tongxin xuebao (Jul 2024)
Dynamic computing offloading strategy in LEO constellation edge computing network
Abstract
In satellite edge computing networks, when too many ground users access the satellite through the same channel, the resulting co-channel interference will lead to edge computing performance degradation. To address this problem, a multi-user computing offloading strategy based on stochastic game was proposed under the system model of dynamic environment low earth orbit constellation edge computing network. On the premise of considering the selfishness of users, the stochastic characteristics of the satellite-ground channel and the dynamic nature of ground user access, from the perspective of game theory, the offloading decision-making process of ground users in the dynamic environment was formulated as a stochastic game. It was proved that the formulated stochastic game was equivalent to a weighted potential game with at least one Nash Equilibrium (NE), and the NE minimized the system overhead. In order to achieve NE in a distributed manner under dynamic environment, an intelligent stochastic learning algorithm based on the stochastic learning was designed to efficiently achieve NE for the proposed stochastic game. Simulation results show that compared to the benchmark algorithm, the proposed algorithm can significantly reduce the co-channel interference and the system overhead, and achieve near-optimal performance.
