TY - GEN
T1 - Momentum-based ICA for Self Interference Cancellation in In-Band Full-Duplex Systems
AU - Lee, Chi
AU - Shen, Chung-An
AU - Fouda, Mohammed E.
AU - Eltawil, Ahmed
N1 - KAUST Repository Item: Exported on 2023-03-10
PY - 2023/3/7
Y1 - 2023/3/7
N2 - Recently, Independent Component Analysis (ICA) has proven its effectiveness as a self-interference cancellation method for in-band full duplex systems. However, ICA could suffer slow convergence due to the iterative estimation of the independent components which limits its usage in real-time applications. In this paper, we introduce a momentum-based ICA to accelerate convergence via incorporating gradient history. The proposed momentum-based ICA is evaluated and tested on different ICA algorithms including real-valued and complexvalued FastICA and entropy bound minimization based ICA. The results show significant speedup improvement compared to native ICA based on gradient descent approach. The proposed algorithm shows consistent results under different transceiver non-linearity and for different frame lengths.
AB - Recently, Independent Component Analysis (ICA) has proven its effectiveness as a self-interference cancellation method for in-band full duplex systems. However, ICA could suffer slow convergence due to the iterative estimation of the independent components which limits its usage in real-time applications. In this paper, we introduce a momentum-based ICA to accelerate convergence via incorporating gradient history. The proposed momentum-based ICA is evaluated and tested on different ICA algorithms including real-valued and complexvalued FastICA and entropy bound minimization based ICA. The results show significant speedup improvement compared to native ICA based on gradient descent approach. The proposed algorithm shows consistent results under different transceiver non-linearity and for different frame lengths.
UR - http://hdl.handle.net/10754/690216
UR - https://ieeexplore.ieee.org/document/10051915/
U2 - 10.1109/ieeeconf56349.2022.10051915
DO - 10.1109/ieeeconf56349.2022.10051915
M3 - Conference contribution
BT - 2022 56th Asilomar Conference on Signals, Systems, and Computers
PB - IEEE
ER -