A Machine-Learning-Based Blind Detection on Interference Modulation Order in NOMA Systems

In order to blindly detect the modulation order of interference signals in downlink non-orthogonal multiple access systems, a machine learning (ML) algorithm based on Anderson-Darling test is proposed in this letter. The proposed algorithm adopts ML to determine the modulation order of interference user equipment from the raw received constellation points automatically. In feature extraction, a novel feature is introduced to improve the accuracy of blind detection. To evaluate the performance of blind detection, the detection rate and the throughput are simulated under different scenarios. Simulation results show that the proposed algorithm outperforms conventional algorithm on modulation order detection.