A novel blind detection algorithm for successive interference cancellation in non-orthogonal multiple access system

This paper considers a Non-Orthogonal Multiple Access (NOMA) network, in which base station transmits a data packet by multi-user superposition to a destination. In this system, Successive Interference Cancellation (SIC) receiver is used to eliminate the co-channel interference. Some physical layer parameters, such as modulation order and precoding matrix indicator (PMI), are required for the SIC receiver to separate the overlapped signals. However, an abundance of parameters would bring a large number of signalling overhead. To reduce the signalling overhead, some of these parameters can be blindly detected instead of signalling notification. To detect these kinds of parameters, a novel blind detection algorithm is proposed in this paper. Firstly, feature extraction based on wavelet cluster is introduced to obtain feature information from received data. Then a filter is designed to reduce the interference among these features. Theoretical analysis and simulation results show that the proposed algorithm achieves high detection performance under the computation complexity of approximate the max-log likelihood algorithm. This paper proposes an AIT-WC-based blind detection algorithm for successive interference cancellation in Non-Orthogonal Multiple Access system. For the authors' application scenario, theoretical analysis and simulation results show that the proposed algorithm achieves higher detection performance but with an approximate computation complexity, compared with the traditional max-log likelihood algorithm. The proposed AIT-WC algorithm is effective for the joint blind detection of modulation order and precoding matrix indicator (PMI). When Signal-to-Noise Ratio (SNR) is the same, compared with the max-log likelihood algorithm, the proposed algorithm has an advantage before the blind detection accuracy reaches 100%. Meanwhile, the proposed AIT-WC algorithm can reach 100% blind detection accuracy and the peak throughput in advance than the max-log likelihood algorithm, and it also demonstrates that the proposed algorithm is more stable than the max-log likelihood algorithm when the PMI is different.image