標題: Narrow-band interference suppression in spread-spectrum CDMA communications using pipelined recurrent neural networks
作者: Chang, PR
Hu, JT
電信工程研究所
Institute of Communications Engineering
公開日期: 1-三月-1999
摘要: This paper investigates the application of pipelined recurrent neural networks (PRNN's) to the narrow-band interference (NBI) suppression over spread-spectrum (SS) code-division multiple-access (CDMA) channels in the presence of additive white Gaussian noise (AWGN) plus non-Gaussian observation noise. Optimal detectors and receivers for such channels are no longer linear. A PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules,vith less computational complexity is conducted to introduce best nonlinear approximation capability into the minimum mean-squared error nonlinear predictor model in order to accurately predict the NBI signal based on adaptive learning for each module from previous non-Gaussian observations, Once the prediction of the NBI signal is obtained, a resulting signal is computed by subtracting the estimate from the received signal, Thus, the effect of the NBI can be reduced. Moreover, since those modules of a PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in its total computational efficiency. Simulation results show that PRNN-based NBI rejection provides a superior signal-to-noise ratio (SNR) improvement relative to the conventional adaptive nonlinear approximate conditional mean (ACM) filters, especially when the channel statistics and exact number of CDMA users are not known to those receivers.
URI: http://dx.doi.org/10.1109/25.752570
http://hdl.handle.net/11536/31466
ISSN: 0018-9545
DOI: 10.1109/25.752570
期刊: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume: 48
Issue: 2
起始頁: 467
結束頁: 477
顯示於類別:期刊論文


文件中的檔案:

  1. 000079254200014.pdf