Onboard Interference Prediction for the Cognitive Medium Access in the LEO Satellite Uplink Transmission

Abstract

Cognitive radio (CR) is an efficient way to increase spectrum efficiency for the small low earth orbit (LEO) satellite communication system. Due to the implementation difficulties, we focus on the CR in the uplink transmission. In CR, the cognitive medium access (CMA) is designed to enable the coexistence with the interferences from other systems. However, the CMA schemes designed for the terrestrial system cannot deal well with the global history of interferences in our system. Here, we design the memorized centroid bucket (MCB) scheme that can efficiently utilize the global history of interferences onboard without storing the complete interference samples. With MCB, we can achieve the effective long-term interference prediction to meet the special requirements of the LEO satellite. The key component in MCB is the matching algorithm that can help retrieve the useful historical information. In this paper, we propose three different matching algorithms and the corresponding MCB schemes. The schemes are also compared with the widely used Markovian method and the pair counting-based method. Among all the schemes, the Bayesian scheme MCB-FSNMI-Bayes is the best. The conclusion is validated experimentally with the real data that were collected by an LEO satellite.