Adaptive Access Control and Resource Allocation for Random Access in NGSO Satellite Networks

• Published in: IEEE transactions on network science and engineering 2022-07, Vol.9 (4), p.2721-2733
• Main Authors: Jia, Haoge, Jiang, Chunxiao, Kuang, Linling, Lu, Jianhua
• Format: Article
• Language: English
• Subjects: Access control; Adaptation models; adaptive adjustment; Adaptive control; Efficiency; Geosynchronous orbits; Method of moments; non-uniform traffic demands; Nonuniform traffic; Process control; Random access; Resource allocation; Resource management; Satellite broadcasting; Satellite networks; Satellites; Signaling; traffic prediction
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2022.3168988

In non-geostationary orbit (NGSO) satellite networks, users perform random access (RA) to establish connections. Due to the non-uniform traffic demands of RA in time and space, dynamic access control and resource allocation methods with low signaling overhead and high RA efficiency are required, where the RA efficiency is denoted as the number of successfully accessed users per resource block. Existing dynamic adjustment methods provide a feasible approach to improve RA efficiency with short update intervals, but the frequent updates of these methods result in high signaling overhead. Compared with the short-term update, update with a given longer interval could reduce update frequency, while it will lead to a reduction in RA efficiency for the accuracy of long-term prediction decreases. Hence, to improve RA efficiency and decrease signaling overhead at the same time, we propose a new adaptive access control and resource allocation scheme in this paper, where the length of update interval is adaptive to the changes in traffic. First, average random access efficiency (ARAE) is defined to evaluate the RA efficiency between each update. Then, to decrease signaling overhead while keeping high ARAE, adaptive access control and resource allocation scheme is proposed, where the adaptive traffic prediction step is introduced to forecast future traffic with adaptively selected update interval, and the access control and resource allocation step is designed to maximize ARAE based on forecasted traffic. Finally, the effectiveness of the proposed scheme is corroborated by simulations, which indicate our scheme is adaptive to varying demands and outperforms other methods with high ARAE and low signaling overhead.