Signal Detection Method for OTFS System Based on Feature Fusion and CNN

For orthogonal time–frequency space (OTFS) systems in high-mobility scenarios, traditional signal detection algorithms face challenges due to their reliance on channel state information (CSI), requiring excessive pilot overhead. Meanwhile, based on convolutional neural network (CNN) detection suffer from insufficient signal feature extraction, the message passing (MP) algorithm exhibits low efficiency in iterative signal updates. This paper proposes a signal detection method for an OTFS system based on feature fusion and a CNN (MP-WCNN), which employs wavelet decomposition to extract multi-scale signal features, combining MP enhancement for feature fusion and constructing high-dimensional feature tensors through channel-wise concatenation as CNN input to achieve signal detection. Experimental results demonstrate that the proposed MP-WCNN method achieves approximately 9 dB signal-to-noise ratio (SNR) gain compared to the MP algorithm at the same bit error rate (BER). Furthermore, the proposed method operates without requiring pilot assistance for CSI acquisition.