Computation Offloading for Rechargeable Users in Space-Air-Ground Networks
Yongkang Gong, Haipeng Yao, Di Wu, Wanmai Yuan, Tao Dong, F. Richard Yu
Abstract
Relying on space-air-ground (SAG)-integrated artificial intelligence of everything (AIoE) networks, massive computation-intensive and latency-sensitive tasks can be efficiently either executed locally by ground AIoE users, or offloaded to SAG servers, such as remote base stations, aerial high altitude platform (HAP) and low earth orbit satellites. However, joint optimization of communication and computation resources becomes a great challenge considering dynamic network environment, large-scale coverage and battery energy backup constraint. Hence, in this paper, we propose a SAG-integrated heterogenous computation offloading architecture for the deep integration of communication and computation resources in order to maximize the sum-rate of all AIoE users. Moreover, we propose a multi-agent proximal policy optimization algorithm with the aid of Lyapunov-based profile to solve the task scheduling and HAP selection. And a convex optimization based communication and computation resource allocation scheme processes the CPU-cycle frequency and transmission power. The battery energy backup is tackled via the linear programming policy. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art baselines in terms of convergence speed, average sum-rate and battery backup level of AIoE users.