Channel Estimation and Pilot Overhead Reduction in OFDM Systems using Compressed Sensing Dynamic Mode Decomposition

Autoren: F. Haddad, C. Bockelmann, A. Dekorsy

This work investigates the potential of employing the approach Compressed Sensing Dynamic Mode Decomposition (CS-DMD) in the context of time-varying wireless channels. To the best of the authors’ knowledge, this marks the first instance of utilizing CS-DMD for pilot-based channel estima- tion in Orthogonal Frequency Division Multiplexing (OFDM) systems. The effectiveness of this method is compared with two advanced deep learning-based channel estimation techniques: Interpolation-ResNet and Learned Approximate Message Passing (LAMP). Furthermore, we leverage the advantageous character- istics of DMD in analyzing complex nonlinear dynamic systems to predict the future state of the channel, thereby reducing the required pilot signals. Simulation results show that utilizing CS- DMD can achieve superior channel estimation performance with less pilot overhead.

Dokumenttyp: Journal Paper
Veröffentlichung: Februar 2024
Journal: IEEE Communications Letters
Dateien: BibTEX
Zuletzt aktualisiert am 22.02.2024 von F. Haddad
AIT ieee GOC tzi ith Fachbereich 1
© Arbeitsbereich Nachrichtentechnik - Universität BremenImpressum / Kontakt