With the rapid development of the satellite Internet of Things (IoT), a large number of short-burst users are aggravating collisions and interference among users of the access network. To address this issue, several organizations and individuals have put forward some dynamic access schemes. However, for most of the proposed dynamic access schemes, it is necessary to know the exact number of future time slot access applications. At present, some load estimation schemes have been proposed in the literature, but the accuracy of these schemes is not high, and they can only achieve load estimation for current time slot.

To solve this issue, we propose a load estimation method based on the leading code state and parameter estimation. A load prediction method based on machine learning is also proposed. The load estimation method based on leading code status and parameter estimation analyzes the relationship between the probability of leading code in different states within the time slot of the satellite IoT and the number of requests for access to the current time slot. It gives the maximum likelihood parameter estimation expression and uses the maximum likelihood parameter estimation method to estimate the current time slot load. The load prediction method based on machine learning takes the estimated load value as its historical data, combining the Long and Short Term Memory (LSTM) network and the Auto Regressive Moving Average (ARMA) model to predict the future time slot load.

The simulation results show that the estimated error of the load estimation method based on leading code state and parameter estimation is less than 1%. The comprehensive error of the load prediction method based on load estimation results as historical machine learning data is about 6%.

The predicted error of the proposed load estimation and prediction method is within the acceptable range, thus offering accurate future slot access requests for dynamic access schemes.