Title :
Rejection of narrowband interference in PN spread-spectrum systems using neural networks
Author :
Bijjani, R. ; Das, P.K.
Author_Institution :
Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
Abstract :
A multilayer back-propagation perceptron model is presented as a means of detecting a wideband signal in the presence of narrowband jammers and additive white noise. The performance of the neural network is compared with that of the estimation-type filter, which uses a least-mean-squared (LMS) adaptive filter, in terms of the interference rejection (notching) capability, the bit error probability, and the overall robustness of the system. The nonlinear neural network filter is shown to offer a faster convergence rate and overall better performance than the LMS Widrow-Hoff filter
Keywords :
adaptive filters; interference suppression; neural nets; signal detection; spread spectrum communication; telecommunications computing; LMS Widrow-Hoff filter; LMS adaptive filter; PN spread-spectrum systems; additive white noise; bit error probability; convergence rate; estimation-type filter; interference rejection; multilayer back-propagation perceptron model; narrowband interference; narrowband jammers; nonlinear neural network filter; signal processing; wideband signal; Adaptive filters; Interference; Least squares approximation; Multi-layer neural network; Multilayer perceptrons; Narrowband; Neural networks; Signal detection; Spread spectrum communication; Wideband;
Conference_Titel :
Global Telecommunications Conference, 1990, and Exhibition. 'Communications: Connecting the Future', GLOBECOM '90., IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
0-87942-632-2
DOI :
10.1109/GLOCOM.1990.116660
