Learning-Aided Iterative Receiver for Superimposed Pilots: Design and Experimental Evaluation

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to enhance robustness under time-varying and mismatched channel conditions. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.