Joint Blind Estimation of PN Codes and Channels for Long-Code DSSS Signals in Multiple Paths at Low SNR

Abstract

In this paper, for the long-code direct sequence spread spectrum (DSSS) signal in multipath channels at the low signal-to-noise ratio (SNR), we propose a joint blind estimation method for pseudorandom (PN) codes and channels without any given code sequences. First, we convert the received long-code DSSS signal to an approximate equivalent short-code DSSS model. Then, based on the new model, we derive the maximum likelihood estimate (MLE) of the information symbols and spreading waveforms. Based on the estimated spreading waveforms, we establish the ML model of PN codes and channels and then obtain their MLE by iteratively transforming the ML model and using the iterative least-squares projection (ILSP) method. The simulation results show that at the low signal-to-noise ratio (SNR), the proposed method provides superior estimation performance of information symbols and PN codes to that of the blind algorithms for Gaussian channels; thus, for channel estimation it outperforms the semiblind estimation method with the sufficient received data.