Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network

Authors:
Zan Yang;Xiaoxin Zhang;Yuping Zhao
Affiliations:
State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China 100871;QUALCOMM, San Diego, USA;State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China 100871
Venue:
Wireless Personal Communications: An International Journal
Year:
2013

Citing 5
Cited 0

Interference Cancellation and Avoidance for Secondary Users Co-existing with TDD-based Primary Systems

Wireless Personal Communications: An International Journal
On the complex backpropagation algorithm

IEEE Transactions on Signal Processing
Cyclostationarity-based filtering for narrowband interference suppression in direct-sequence spread-spectrum systems

IEEE Journal on Selected Areas in Communications
Robust joint interference detection and decoding for OFDM-based cognitive radio systems with unknown interference

IEEE Journal on Selected Areas in Communications
Recurrent neural networks and robust time series prediction

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a narrowband interference (NBI) suppression algorithm for Direct Sequence-Code Division Multiple Access systems. The NBI is considered from heterogeneous networks, and predicted based on its cyclostationary characteristic using a nonlinear feed-forward neural network predictor which eliminates the nonlinearity of the spread spectrum (SS) signal in the NBI prediction. To further improve the suppression performance, this paper exploits the structure of the spreading code, and proposes an iterative code-aided algorithm to jointly estimate the NBI and the SS signal. Simulation results reveal that the proposed algorithm largely outperforms the conventional linear prediction filtering and linear-conjugate linear polyperiodically time-varying filtering methods in both the signal to interference plus noise ratio improvement and the bit error rates, when it operates in NBI-contaminated environments.