AI-Driven Iterative Receiver for Superimposed Pilot Schemes in MIMO-OFDM Systems

The superimposed pilot (SIP) transmission scheme shows great potential for improving spectral efficiency in MIMO-OFDM systems. However, it also introduces complex challenges for receiver design, particularly due to pilot contamination and data interference. To address these issues, the joint channel estimation, signal detection, and decoding (JCDD) framework has emerged as a promising solution, utilizing iterative refinement to enhance receiver performance. Despite this, existing JCDD methods either focus heavily on theoretical analysis, often neglecting practical application scenarios, or experience performance limitations due to inherent design flaws. In this paper, we propose an advanced iterative JCDD receiver that effectively mitigates the negative effects of pilot contamination and data interference. Our approach improves traditional linear minimum mean-square error (LMMSE) channel estimation by incorporating state-of-the-art techniques—specifically variational message passing (VMP) and deep learning (DL)—allowing for better adaptation to varying channel conditions. Extensive empirical evaluations demonstrate that our proposed SIP receiver not only surpasses the conventional orthogonal pilot (OP) scheme but also exhibits outstanding adaptability in mismatched channel environments, thanks to the VMP and DL-based improvements.