Abstract-Iterative message passing algorithms on graphs, which are generalized from the well-known turbo decoding algorithm, have been studied intensively in recent years because they can provide near-optimal performance and significant complexity reduction. In this paper, we demonstrate that this technique can be applied to pseudorandom code acquisition problems as well. To do this, we represent good pseudonoise (PN) patterns using sparse graphical models, then apply the standard iterative message passing algorithms over these graphs to approximate maximum-likelihood synchronization. Simulation results show that the proposed algorithm achieves better performance than both serial and hybrid search strategies in that it works at low signal-to-noise ratios and is much faster. Compared with full parallel search, this approach typically provides significant complexity reduction.
-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, 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.
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