Implicit channel estimation for ML sequence detection over finite-state Markov communication channels

Zarko B. Krusevac; Rodney A. Kennedy; Predrag B. Rapajic

Implicit channel estimation for ML sequence detection over finite-state Markov communication channels

Zarko B. Krusevac^*, Rodney A. Kennedy, Predrag B. Rapajic

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel process estimation, but decreases information transmission rate. The optimal input entropy rate (optimal implicit training rate) which achieves the maximum mutual information rate, is found.

Original language	English
Title of host publication	Proceedings - 7th Australian Communications Theory Workshop, 2006
Pages	130-136
Number of pages	7
Publication status	Published - 2006
Event	7th Australian Communications Theory Workshop, 2006 - Perth, Australia Duration: 1 Feb 2006 → 3 Feb 2006

Publication series

Name	Proceedings - 7th Australian Communications Theory Workshop, 2006
Volume	2006

Conference

Conference	7th Australian Communications Theory Workshop, 2006
Country/Territory	Australia
City	Perth
Period	1/02/06 → 3/02/06

Cite this

@inproceedings{32643b6cf99a4951a566599c100f40a1,

title = "Implicit channel estimation for ML sequence detection over finite-state Markov communication channels",

abstract = "This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel process estimation, but decreases information transmission rate. The optimal input entropy rate (optimal implicit training rate) which achieves the maximum mutual information rate, is found.",

author = "Krusevac, {Zarko B.} and Kennedy, {Rodney A.} and Rapajic, {Predrag B.}",

year = "2006",

language = "English",

isbn = "1424402131",

series = "Proceedings - 7th Australian Communications Theory Workshop, 2006",

pages = "130--136",

booktitle = "Proceedings - 7th Australian Communications Theory Workshop, 2006",

note = "7th Australian Communications Theory Workshop, 2006 ; Conference date: 01-02-2006 Through 03-02-2006",

}

Krusevac, ZB, Kennedy, RA & Rapajic, PB 2006, Implicit channel estimation for ML sequence detection over finite-state Markov communication channels. in Proceedings - 7th Australian Communications Theory Workshop, 2006., 1625269, Proceedings - 7th Australian Communications Theory Workshop, 2006, vol. 2006, pp. 130-136, 7th Australian Communications Theory Workshop, 2006, Perth, Australia, 1/02/06.

Implicit channel estimation for ML sequence detection over finite-state Markov communication channels. / Krusevac, Zarko B.; Kennedy, Rodney A.; Rapajic, Predrag B.
Proceedings - 7th Australian Communications Theory Workshop, 2006. 2006. p. 130-136 1625269 (Proceedings - 7th Australian Communications Theory Workshop, 2006; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Krusevac, Zarko B.

AU - Kennedy, Rodney A.

AU - Rapajic, Predrag B.

PY - 2006

Y1 - 2006

N2 - This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel process estimation, but decreases information transmission rate. The optimal input entropy rate (optimal implicit training rate) which achieves the maximum mutual information rate, is found.

AB - This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. Implicit (blind) estimation is based on a measure of how modified is the input distribution when filtered by the channel transfer function and it is shown that there is no modification of an input distribution with maximum entropy rate. Input signal entropy rate reduction enables implicit (blind) channel process estimation, but decreases information transmission rate. The optimal input entropy rate (optimal implicit training rate) which achieves the maximum mutual information rate, is found.

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M3 - Conference contribution

SN - 1424402131

SN - 9781424402137

T3 - Proceedings - 7th Australian Communications Theory Workshop, 2006

SP - 130

EP - 136

BT - Proceedings - 7th Australian Communications Theory Workshop, 2006

T2 - 7th Australian Communications Theory Workshop, 2006

Y2 - 1 February 2006 through 3 February 2006

ER -

Implicit channel estimation for ML sequence detection over finite-state Markov communication channels

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