New Techniques for Word Synchronisation
This paper describes two techniques for identifying a known sequence, when embedded in random data and corrupted by additive noise. The first method is derived from the maximum likelihood algorithm. The original implementation of this ML algorithm uses amplitude-shift-keyed transmission and shows typical improvements of 3 dB over the simple correlator rule. An algorithm for using the maximum likelihood technique with frequency-shift-keyed transmission is derived which, in simulation trial, shows typical improvements of 2 dB at low signal-to-noise ratios, when compared with the simple correlator rule. The second technique presented improves the probability of sequence detection when soft decision information is not available in the detector. Instead of looking for a single peak, this approach searches for the entire auto-correlation function of the known sequence. Under certain conditions, the performance of such a scheme can approach that of the ML algorithm.
KeywordsRandom Data Matched Filter Simulation Trial Hard Decision Noise Standard Deviation
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