We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

A dynamic programming algorithm for simultaneous phase estimation and data decoding on random-phase channels

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

The problem of simultaneously estimating phase and decoding data symbols from baseband data is posed. The phase sequence is assumed to be a random sequence on the circle, and the symbols are assumed to be equally likely symbols transmitted over a perfectly equalized channel. A dynamic programming algorithm (Viterbi algorithm) is derived for decoding a maximum {em a posteriori} (MAP) phase-symbol sequence on a finite dimensional phase-symbol trellis. A new and interesting principle of Optimality for simultaneously estimating phase and decoding phase-amplitude coded symbols leads to an efficient two-step decoding procedure for decoding phase-symbol sequences. Simulation results for binary, 8 -ary phase shift keyed (PSK), and 16-quadrature amplitude shift keyed (QASK) symbol sets transmitted over random walk and sinusoidal jitter channels are presented and compared with results one may obtain with a decision-directed algorithm or with the binary Viterbi algorithm introduced by Ungerboeck. When phase fluctuations are severe and when occasional large phase fluctuations exist, MAP phase-symbol sequence decoding on circles is superior to Ungerboeck's technique, which in turn is superior to decision-directed techniques.

Published in:

Information Theory, IEEE Transactions on  (Volume:27 ,  Issue: 5 )