No abstract

Synchronization patterns aperiodically embedded in data may result from applications such as transmission of framed data and compression of video. In this work a metric for detection of these patterns is derived. It is assumed that the distribution of the synchronization patterns in the symbol stream is known. By taking advantage of this knowledge, improved synchronization can be realized over traditional correlation with correction. Transmission using BPSK on an AWGN channel is considered first. This derivation is then extended to higher-order modulation schemes, and high-SNR approximations to the exact metrics are derived. Experimental results for BPSK and QPSK signaling are shown. Finally, the effects of inexact knowledge of the channel and a priori probabilities are examined and it is shown that the aperiodic metric is not sensitive to small deviations in the estimates of the channel or the synchronization pattern probabilities.

No abstract

Synchronization patterns aperiodically embedded in data may result from applications such as transmission of framed data and compression of video. In this work a metric for detection of these patterns is derived. It is assumed that the distribution of the synchronization patterns in the symbol stream is known. By taking advantage of this knowledge, improved synchronization can be realized over traditional correlation with correction. Transmission using BPSK on an AWGN channel is considered first. This derivation is then extended to higher-order modulation schemes, and high-SNR approximations to the exact metrics are derived. Experimental results for BPSK and QPSK signaling are shown. Finally, the effects of inexact knowledge of the channel and a priori probabilities are examined and it is shown that the aperiodic metric is not sensitive to small deviations in the estimates of the channel or the synchronization pattern probabilities.

No abstract