Abstract

Recently, two near-optimal decoding algorithms [Shao, R.Y., Lin, S., and Fossorier, M.P.C., 2003. Two decoding algorithms for tailbiting codes. IEEE transactions on communications, 51 (10), 1658–1665; Krishnan, K.M. and Shankar, P., 2006. Approximate linear time ML decoding on tail-biting trellises in two rounds. In IEEE international symposium on information theory, Seattle, WA, USA, pp. 2245–2249] have been proposed for convolutional tail-biting codes. Both algorithms iterate the Viterbi algorithm twice, but use different metrics in the second iteration. Simulations showed that the latter algorithm (Krishnan and Shankar 2006) improved on the earlier one (Shao et al. 2003) in word error rates at the price of additional storage consumption. In this work, we prove that with a proper modification to the earlier one, the two algorithms can be made to have exactly the same survivor path at each state in the trellis, and hence are equivalent in error performance. One can consequently adopt the modified algorithm to alleviate the need for extra storage consumption of the later algorithm and, at the same time, achieve equally good performance.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.