Adaptive BP neural network (ABPNN) based PN code acquisition system via recursive accumulator

Abstract

An adaptive back propagation (BP) neural network based PN code acquisition system is presented. Conventional neural network based acquisition systems are usually trained on PN code, but this system is based on training a back propagation neural network at all possible phases of the output of a correlation detector which is modified by a recursive accumulator. The recursive accumulator can converge the input of the neural network into a limited sample space, and the BP neural network acquires the phase of the received PN code from the converged data. The advantages of this system are that the gain of the system is controllable and the training data sample space is limited. The BP neural network is used to distinguish the transmitted signal and noise. Computer simulations show that the proposed system can acquire the phase of the received PN code correctly at very low signal-to-noise ratio (SNR) in an AWGN channel.