Name
Abstract | ||
|---|---|---|
A neural network model is described which simulate the Parallel Matching Filtering (PMF) for Direct Sequence Spread Spectrum (DSSS) signal acquisition. This system is based on training the Counter Propagation Network (CPN) in all half chip phase shifts of the Pseudo Noise (PN) code. The trained network can be used at the receiver for the signal acquisition. The CPN performance in Additive Wight Gaussian Noise (AWGN) channel is evaluated. Computer simulations carried on maximal length sequences of length N=256, show that the proposed system can effectively decide the half chip phase shift of the received code even at much lower Signal to Noise ration (S/N) until S/N = -27.74dB. This model has a simple architecture, so can be realized in a simple hardware. This makes the neural network based acquisition technique faster and more robust than the other conventional acquisition techniques. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28648-6_44 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2 |
Keywords | Field | DocType |
Neural Network,Matching Filtering,Direct Sequence Spread Spectrum Acquisition,Counter Propagation Network | Computer science,Artificial intelligence,Artificial neural network,Pseudorandom noise,Pattern recognition,Signal-to-noise ratio,Filter (signal processing),Algorithm,Speech recognition,Chip,Direct-sequence spread spectrum,Gaussian noise,Additive white Gaussian noise | Conference |
Volume | ISSN | Citations |
3174 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tarek Elhabian | 1 | 1 | 0.76 |
| Zhang Bo | 2 | 43 | 7.59 |
| Dingrong Shao | 3 | 5 | 1.90 |