Iterative SPBA Framework for Enhanced Channel Estimation and Data Detection in Zak-OTFS

Zak transform-based orthogonal time-frequency space (Zak-OTFS) modulation demonstrates robustness and low complexity in channels with high Doppler spreads, making it a promising candidate for future 6G standards. However, existing channel estimation methods for Zak-OTFS systems reserve blank resource grids in the delay-Doppler domain as guard symbols, which reduces spectral efficiency (SE). To address this issue, we propose a high SE superimposed perfect binary array-aided (SPBA) channel estimation and data detection framework for Zak-OTFS systems. The proposed framework includes an SPBA-based channel estimator capable of real-time estimation of complex channel gains and delay-Doppler taps for each frame, without the need for guard symbols. To further enhance the accuracy of channel estimation and data detection, we develop a joint channel estimation and detection algorithm that combines the SPBA-based estimator with iterative interference cancellation, effectively mitigating interference between pilots and data. Additionally, we analyze the impact of power allocation on both channel estimation and data detection, deriving the lower bound of the signal-to-interference-plus-noise ratio for the proposed framework and determining the optimal power allocation ratio. The Simulation results demonstrate that the proposed framework significantly improves SE. Compared to the conventional embedded pilot scheme, our algorithm improves spectral efficiency by 31.2% in certain scenarios.