Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
An Introduction to Spread-Spectrum Communications
An Introduction to Spread-Spectrum Communications
Iterative Detection: Adaptivity, Complexity Reduction, and Applications
Iterative Detection: Adaptivity, Complexity Reduction, and Applications
Shift Register Sequences
The generalized distributive law
IEEE Transactions on Information Theory
Rapid Hybrid Acquisition of Ultra-Wideband Signals
Journal of VLSI Signal Processing Systems
Journal of VLSI Signal Processing Systems
Journal of Electrical and Computer Engineering - Special issue on iterative signal processing in communications
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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 efficietly 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 algonthm 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.