Abstract: Influenced by the power allocation and superposition coding at the transmitter side, the power- domain non-orthogonal multiple access (NOMA) symbol detection algorithm based on a single-task neural network is not compatible with the symbol detection task for different users. A symbol detection algorithm based on multi-task neural network is designed for user-assisted cooperative multiple-input multiple-output (MIMO)-NOMA communication system, which can learn the deep shared features of data and detect symbols of different users simultaneously. In cooperative communication, the signal data received by different users are distributed differently, and there is a problem of data island. However, the training data and the test data are required by the machine learning model to be independently and equally distributed. Therefore, the multi-task federal learning framework isproposed to address this problem. The experimental results show that with the improvement of signal-noise-ratio ( SNR), the proposed symbol detection algorithm has better performance than the traditional symbol detection algorithm.

Key words:  cooperative non-orthogonal multiple access ,  symbol detection ,  multi-task federated learning