Spatial pulse position modulation multi-classification detector based on deep learning

In order to effectively avoid high computational complexity when using Maximum Likelihood (ML) detection, a deep learning-based Spatial Pulse Position Modulation (SPPM) multi-classification detector is proposed by combining a Deep Neural Network (DNN) and step detection. In the detector, the DNN is used to establish a non-linear relationship between the received signal and the PPM symbols. Thereafter, the subsequent received PPM symbols are detected according to this relationship, so as to avoid the exhaustive search process of PPM symbol detection. The simulation results show that with the proposed detector, the SPPM system approximately achieves optimal bit error performance on the premise of greatly reducing detection complexity. Meanwhile, it overcomes the error platform effect caused by K-Means Clustering (KMC) step classification detection. When the PPM order is 64, the computational complexity of the proposal is about 95.45% and 33.54% lower than that of ML detectors and linear equalization DNN detectors, respectively.