Load-Balanced Collaborative Offloading for LEO Satellite Networks

Abstract

Low earth orbit (LEO) satellite networks have become one of the hot research areas as an essential part of satellite communication networks. The dynamic topology and unbalanced traffic demand may lead to inter-satellite link congestion, thus, improving network load balancing performance is one of the key issues to be addressed in LEO satellite networks. We propose a load-balanced collaborative offloading (LBCO) strategy to achieve a balanced traffic distribution in LEO satellite networks. LBCO strategy consists of two algorithms, namely, channel-aware gradient fair association (CAGFA) algorithm and inter-satellite links collaborative offloading (ISLCO) algorithm. The CAGFA algorithm aims to maximize the aggregate weighted utility, and the ISLCO algorithm aims to achieve the traffic offloading and download observation data from the LEO satellite network. Specifically, we first determine the actual downloading satellite set and neighboring satellite set by constructing an earth station (ES) time-share graph and a space-time topology graph. Then, the LBCO strategy uses the CAGFA algorithm to obtain the optimal satellite terminal association indicator and the load of downloading satellites. Finally, the ISLCO algorithm is proposed to achieve proportional offloading of traffic among the neighboring satellites and download massive observation data. Simulations show that the proposed CAGFA algorithm improves the weighted utility by 3.3% and the convergence by 47.6% compared with the benchmark stochastic gradient descent-based association (SGDA) algorithm. We also validate the performance of the LBCO strategy by data download throughput, which performs better than other benchmark algorithms under three different load scenarios.