Iterative message passing techniques for rapid code acquisition

Iterative message passing algorithms (MPAs) have found application in a wide range of data detection problems because they can provide near optimal performance and significant complexity reduction. In this paper, we demonstrate that they can be used to efficiently solve the pseudo random code acquisition problem as well. To do this, we represent good pseudo-noise (PN) patterns using sparse graphical models, and then apply the standard iterative message passing algorithm over this graph to approximate maximum likelihood synchronization. Simulation results show that this algorithm achieves better performance than traditional serial search code acquisition in the sense that it works at low signal-to-noise ratios (SNRs) and is much faster. Compared to full parallel search, this approach typically provides significant complexity reduction.