Pulse train deinterleaving: algorithms and cost criteria

TitlePulse train deinterleaving: algorithms and cost criteria
Publication TypeConference Paper
Year of Publication1999
AuthorsLee, K. S. M., M. J. Rowe, and V. Krishnamurthy
Conference NameAcoustics, Speech, and Signal Processing, 1999. ICASSP '99. Proceedings., 1999 IEEE International Conference on
Pagination2475 -2478 vol.5
Keywordsadditive white Gaussian noise, AWGN, communications channel, cost criteria, deinterleaving algorithms, error analysis, forward dynamic programming, heavy-tailed distributions, L1 norm output error cost function, L2 norm output error cost function, least squares approximations, least squares cost function, periods, phases, pulse train deinterleaving, pulse trains transmission, signal processing, simulated annealing, sources

Consider the problem where pulse trains transmitted from a known number of sources are received on a single communications channel. These pulses are corrupted with noise. The deinterleaving problem is to determine which source contributed which pulse and the periods and phases of each source. This paper explores the performance of a number of deinterleaving algorithms. We propose an alternative to the existing forward dynamic programming (FDP) technique: simulated annealing (SA). It can use either the same cost function as for FDP, or an L1 or L2 norm output error cost function. We also investigate modelling the noise by heavy-tailed distributions, in addition to white Gaussian noise (WGN)


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