mso-ansi-language:EN-US">With the rapid evolution of wireless communication technologies, ensuring reliable and efficient data transmission in high-mobility scenarios has become a critical challenge. In particular, accurate channel estimation is essential to maintain communication quality in environments characterized by significant Doppler effects and dynamic propagation conditions.
mso-ansi-language:EN-US">This thesis explores the design of waveforms for efficient sparse delay-Doppler channel estimation in high-mobility wireless communication systems. As future wireless networks demand robust and accurate channel estimation techniques, particularly in the presence of high Doppler shifts, the study focuses on both on-grid and off-grid approaches for doubly sparse linear time-varying (DS-LTV) channels.
mso-ansi-language:EN-US">In the first part, we investigate on-grid DS-LTV channel estimation and introduce three different sparsity models that characterize practical propagation environments. We propose an optimized estimation framework leveraging the minimum mean squared error (MMSE) criterion and basis expansion models (BEMs). Through theoretical analysis and simulations, we demonstrate that Affine Frequency Division Multiplexing (AFDM) outperforms traditional waveforms such as Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Time Frequency Space (OTFS) in terms of pilot overhead reduction and estimation accuracy.
mso-ansi-language:EN-US">In the second part, we extend our study to off-grid DS-LTV channel estimation, addressing the issue of mismatches between actual Doppler shifts and predefined grid points. By employing novel off-grid approximation techniques based on multiple shifted elementary BEMs, we enhance estimation robustness and improve channel prediction capabilities. Our findings confirm AFDM’s efficiency in handling off-grid Doppler shifts and its potential for adaptive transmission strategies.
mso-ansi-language:EN-US">Beyond channel estimation, we explore the broader implications of our research for radar and sensing applications, demonstrating the feasibility of sub-Nyquist radar techniques that optimize sampling rates while maintaining detection accuracy. This interdisciplinary approach highlights the impact of our work beyond wireless communication systems.
mso-ansi-language:EN-US">The conclusions drawn from this research provide valuable insights for the development of next-generation communication technologies. Future work could explore adaptive sparsity-aware estimation techniques, machine learning-based approaches, and real-world experimental validations to further enhance the practical deployment of AFDM in high-mobility scenarios.
