Superimposed Sunscape Pilot Pattern for Channel Estimation in OTFS-Based ISAC

Orthogonal time-frequency space (OTFS) is a promising waveform for future Sixth Generation (6G) networks and beyond, offering robustness to high-mobility scenarios and enabling seamless integration of sensing and communication services by operating in the delay-Doppler (DD) domain. Reliable channel estimation is essential for both functions; however, conventional embedded-pilot (EP) schemes sustain high pilot overhead and an increased peak-to-average power ratio (PAPR). Superimposed training (ST) methods have been proposed as an alternative to reduce these limitations, since pilots and data symbols share the same DD resources. Nevertheless, existing ST-based techniques typically depend on computationally expensive algorithms or exhibit degraded communication and sensing performance. To overcome these restrictions, this work introduces a novel low-power superimposed pilot design, called sunscape pattern, and a corresponding ST-based channel estimation algorithm specifically tailored to this structure. The core design principle of the sunscape pattern is to distribute pilot energy following a structured layout in the DD domain that resembles a seascape with a sun over the sea. The sun enables accurate delay and Doppler detection (both integer and fractional), while the sea inherently supports interference averaging between pilot and data components, thereby mitigating self-interference and noise without requiring computationally expensive iterative interference cancellation. Simulation results validate the proposed scheme. They show that it achieves accurate channel estimation and reliable DD-domain detection with low computational complexity and manageable PAPR, outperforming existing approaches in both communication and sensing performance.