Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning

2026-05-28

AI Summary

This work addresses the performance degradation and high computational complexity of superimposed pilot schemes in time-varying channels, caused by strong coupling between pilots and data. To overcome these limitations, the authors propose a low-overhead hybrid pilot transmission framework that synergistically combines the interference-free nature of orthogonal pilots with the zero-overhead advantage of superimposed pilots. The approach innovatively integrates data-dependent superimposed training, an algebraic-structure-driven non-iterative decoupling method, and a hybrid resource allocation strategy. Furthermore, a Vision Transformer-based neural receiver is designed to effectively model temporal channel correlations beyond conventional quasi-static assumptions. Experimental results demonstrate that the proposed scheme significantly outperforms existing methods at low-to-moderate signal-to-noise ratios, achieving higher demodulation reliability, stronger interference resilience, and lower computational complexity.

Abstract

Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers from severe pilot-data coupling, which leads to a critical performance-complexity bottleneck at the receiver. To address this issue, this paper proposes a low-overhead transmission framework that revitalizes data-dependent superimposed training (DDST) with enhanced interference mitigation strategies. First, for quasi-static block-fading channels, an enhanced DDST receiver is developed to achieve non-iterative pilot-data decoupling by exploiting data-dependent algebraic structures. Second, to overcome the sensitivity of conventional DDST to channel variations and symbol misidentification in fast time-varying environments, a mix transmission scheme is developed. By strategically applying DDST to a subset of resource elements, the proposed scheme combines the interference-free transmission property of OP with the zero-pilot-overhead advantage of SIP, thereby improving demapping reliability and interference suppression. Furthermore, under the proposed mix scheme, a Vision Transformer-based neural receiver is designed to capture the orthogonal structure between pilots and perturbation-bearing data, as well as the underlying channel correlations, thereby relaxing the stringent quasi-static assumption required for interference disentanglement. Simulation results demonstrate that the proposed framework achieves significant performance gains in the low-to-medium SNR regime under time-varying channels while providing superior computational efficiency compared with state-of-the-art SIP receivers.

Problem

• superimposed pilot
• pilot-data coupling
• data-dependent superimposed training
• time-varying channels
• receiver complexity

Innovation

• data-dependent superimposed training
• low-overhead transmission
• Vision Transformer
• pilot-data decoupling
• mixed pilot scheme