Diffusion Bridge-Based Joint Estimation and Detection Design for Superimposed-Pilot OFDM Receiver

Superimposed-pilot (SP) transmission improves spectral efficiency but poses significant challenges for receiver design due to pilot–data interference, which conventional algorithms struggle to address effectively under fast-fading conditions. To address this issue, this paper proposes a diffusion bridge–based receiver for joint channel estimation and data detection. By formulating the received signals as one endpoint distribution and the transmitted data together with the channel impulse response as the other endpoint distribution, the conditional probability path between them is learned while implicitly exploiting OFDM structural constraints. To enable more stable and efficient training, the network is designed to directly predict the clean output, and the corresponding flow-velocity loss function for data detection is derived. Furthermore, a two-stage factor scheduler is introduced to balance the training objectives of channel estimation and data detection. Simulation results show that the proposed method achieves lower channel estimation mean square error (MSE) and data detection bit error rate (BER) than existing schemes, while exhibits sufficient generalization ability at higher subcarriers and modulation orders, demonstrating its effectiveness as a data-driven solution for reliable communication in dynamic environments.