Deep Receiver for Multi-Layer Data Transmission with Superimposed Pilots

We investigate a multi-layer data transmission scheme with superimposed pilots (SIPs) to enhance the throughput of multiple-input multiple-output orthogonal frequency-division multiplexing systems. However, in multi-layer data transmission scenarios, signal coupling between different antennas and layers causes severe interference issues, posing significant challenges for receiver design. To address this issue, we propose a deep learning-based receiver architecture, named SANet, which leverages the parallel processing capabilities of the multi-head self-attention (MHSA) mechanism. Specifically, each head of the MHSA mechanism is used to extract local features from each layer of the received signal, enabling the separation and reception of multi-layer bitstream information. Additionally, a flexible and diverse data augmentation strategy is designed to enhance the generalization capability of the deep receiver. Numerical results show that, compared to traditional schemes, the proposed SANet with orthogonal pilots can improve throughput by 7.01%, while the proposed SANet with SIPs can improve throughput by 37.15%.