Optimizing Adaptive Coding and Modulation for Satellite Network with ML-based CSI Prediction

The introduction of adaptive coding and modulation (ACM) in DVB-S2 improves system capacity. Transmission mode is adapted to receiving conditions which are fed back through return link supported in DVB-RCS2. A big challenge is that ACM scheme has to follow channel variations which are faster than the delay between channel measurement and feedback reception. Channel state information (CSI) for next transmission often differs profoundly from current estimated result. This causes the mismatch between transmission mode and channel quality. To resolve this problem, this paper deploys a CSI prediction framework and implements machine learning (ML) algorithms for accuracy. Compared with conventional prediction algorithms, ML algorithms have advantage on handling the complicate and changeable time series. In ACM scheme, MODCOD selection is based on channel SNR and MODCOD SNR threshold. Therefore, this prediction work takes channel SNR for next transmission as the prediction target and past SNR plus other correlated information as the prediction basis. To verify prediction accuracy, this paper discusses major factors impacting channel situation in detail and proposes a synthesis channel fading model. Numerical experiment results demonstrate the improvement on system performance while the efficiency and complexity of different commonly-used ML algorithms are also included.