DocumentCode :
1465607
Title :
Dynamic quantization for maximum likelihood sequence detection of PAM signaling
Author :
Fossorier, Marc P C
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI, USA
Volume :
44
Issue :
11
fYear :
1996
fDate :
11/1/1996 12:00:00 AM
Firstpage :
1444
Lastpage :
1454
Abstract :
Maximum likelihood sequence detection (MLSD) provides optimum detection for intersymbol interference (ISI) channels subject to additive white Gaussian noise (AWGN). This decoding process dynamically searches the most likely path through the intersymbol interference (ISI) channel trellis, using the Viterbi algorithm (VA). In this paper, we exploit the structure of this trellis and, for an M-point pulse amplitude modulation (PAM) constellation, present a new scheme based on dynamic quantization. This scheme becomes extremely efficient for the single memory unit channel 1+f1D, as it achieves optimum MLSD with O(M) computations per decoding step, instead of O(M2) for the VA. Generalization to any finite length channel is also possible and conserves good computational efficiency
Keywords :
Gaussian channels; Viterbi decoding; intersymbol interference; maximum likelihood detection; pulse amplitude modulation; search problems; telecommunication signalling; trellis coded modulation; ISI channels; M-point pulse amplitude modulation; MLSD; PAM signaling; Viterbi algorithm; additive white Gaussian noise; channel trellis; computational efficiency; decoding process; dynamic quantization; finite length channel; intersymbol interference channels; maximum likelihood sequence detection; optimum detection; single memory unit channel; AWGN; Additive white noise; Amplitude modulation; Computational efficiency; Intersymbol interference; Maximum likelihood decoding; Maximum likelihood detection; Pulse modulation; Quantization; Viterbi algorithm;
fLanguage :
English
Journal_Title :
Communications, IEEE Transactions on
Publisher :
ieee
ISSN :
0090-6778
Type :
jour
DOI :
10.1109/26.544461
Filename :
544461
Link To Document :
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