Cédric Chevalier

Cédric Chevalier is a Professor at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France, affiliated with the CÉDRIC Laboratory where he conducts research in the Department of Electronics and Signal Processing within the College of Engineering. His work spans over three decades of contributions to signal processing theory and wireless communications systems.

Professor Chevalier's research focuses on advanced signal processing techniques for wireless communications, with particular expertise in widely linear processing, interference cancellation, MIMO systems, and Faster-Than-Nyquist signaling. His work bridges theoretical signal processing with practical communication system implementations, addressing challenges in spectral efficiency, interference mitigation, and receiver design. He has pioneered methodologies for processing non-circular and rectilinear signals, developing widely linear filtering approaches that significantly outperform conventional techniques in interference-limited scenarios.

Analysis of his recent publications (2019-2025) reveals a continued focus on pushing spectral efficiency boundaries through advanced FTN signaling techniques, sophisticated interference cancellation methods using widely linear and Volterra processing, and practical implementations for next-generation wireless systems. His work increasingly addresses industrial implementation challenges while maintaining strong theoretical foundations.

• ANR POSEIDON research project contributor (Deliverables D1.1 and D4.1)
• Technical lead in multiple French national research initiatives
• Author of numerous patents related to signal processing and wireless communications

Professor Chevalier has mentored numerous researchers who appear as co-authors on his publications, including T. Petitpied, R. Tajan, and J-B. Fraisse, who have made significant contributions to FTN signaling research. His laboratory work bridges theoretical signal processing with practical wireless system implementation, focusing on real-world constraints while advancing fundamental techniques.