Game-Based Computation Offloading and Power Allocation for LEO Constellation Networks in Distributed and Dynamic Environment

To build the new generation of ubiquitous communication and service integration networks with "network omnipresence and computing ubiquitous," it is urgent to improve the in-orbit computing ability of low earth orbit (LEO) constellation networks and develop intelligent technology for satellite-ground collaborative edge computing. Communication tasks between ground nodes and satellites are increasing, but the satellite-to-ground spectrum resources are limited. The reasonable application of channels determines the performance of the network, which in turn affects the users' experience. An outstanding issue is how to allocate channels rationally and control the power of data transmission to reduce co-channel interference and minimize system overhead effectively. This article studies multiuser computation offloading for low earth orbit (LEO) constellation networks under dynamic environment, wherein the system overhead is minimized by joint offloading strategy and power optimization. First, we propose a generic network architecture for computation offloading of LEO constellation networks under the dynamic environment. Then, from a game-theoretic perspective, we model the overhead minimization problem as a potential game and prove that the Nash equilibrium (NE) minimizes the system overhead. After that, to reach the NE, we design the Synchronous log-linear learning-based power control algorithm and joint offloading strategy and power optimization algorithm based on SLA (JOPAS), and prove the convergence of the algorithms. Finally, the effectiveness of the proposed algorithm is verified through extensive simulations and comparisons with benchmark algorithms, and the proposed algorithm achieves near-optimal performance.