Title

Neural Network Based Spread Spectrum Pn Code Acquisition System

Keywords

Additive white noise; Gaussian noise; Matched filters; Neural networks; Phase noise; Recurrent neural networks; Signal generators; Spread spectrum communication; Synchronization; Uncertainty

Abstract

A neural network based direct sequence spread spectrum code synchronization system is proposed. The's system is based on training a recurrent random neural network (RNN) model on all the possible phases of the used spreading code. The trained network can then be used at the receiver for the initial coarse alignment of the local code phase and the received code. One advantage of this technique over the conventional synchronization techniques is that the phase of the received PN code can be decided without searching the potential code phases. Also the RNN, after being trained, can have a simple hardware realization that makes it candidate for implementation as a dedicated chip. This makes the neural network based technique faster and more robust than the conventional techniques. Computer simulations, carried out on maximal length sequences of length N=7 and N=15, show that the proposed system cast effectively indicate the phase of the received code even with very low signal to noise ratios.

Publication Date

1-1-2000

Publication Title

National Radio Science Conference, NRSC, Proceedings

Volume

2000-January

Number of Pages

C30.1-C30.11

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/NRSC.2000.838959

Socpus ID

24544443684 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/24544443684

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