  With the help of a power-domain non-orthogonal multiple access (NOMA) scheme,
satellite networks can simultaneously serve multiple users within limited
time/spectrum resource block. However, the existence of channel estimation
errors inevitably degrade the judgment on users' channel state information
(CSI) accuracy, thus affecting the user pairing processing and suppressing the
superiority of the NOMA scheme. Inspired by the advantages of machine learning
(ML) algorithms, we propose an improved support vector machine (SVM) scheme to
reduce the inappropriate user pairing risks and enhance the performance of NOMA
based satellite networks with imperfect CSI. Particularly, a genetic algorithm
(GA) is employed to optimize the regularization and kernel parameters of the
SVM, which effectively improves the classification accuracy of the proposed
scheme. Simulations are provided to demonstrate that the performance of the
proposed method is better than that with random user paring strategy,
especially in the scenario with a large number of users.
