Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique

Kentaro IKEMOTO; Ryuji KOHNO

doi:10.1093/ietfec/e88-a.3.669

Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique

Kentaro IKEMOTO, Ryuji KOHNO

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In order to achieve adaptive channel coding and adaptive modulation, the main causes of degradation to system performance are the decoder selection error and modulator estimation error. The utilization of supplementary information, in an estimation system utilizing channel estimation results, blind modulation estimation, and blind encoder estimation using several decoders information and encoder transitions have been considered to overcome these two problems. There are however many issues in these methods, such as the channel estimation difference between transmitter and receiver, computational complexity and the assumption of perfect Channel State Information (CSI). Our proposal, on the other hand, decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM). In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns using both encoder and modulator transition probability based on the HMM obtained by CSI and also Decoder and Demodulator Selection Error probabilities. Therefore, the decoder and demodulation results can be achieved efficiently without any restraint on the pattern of switching encoder and modulation.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.3 pp.669-675

Publication Date: 2005/03/01

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DOI: 10.1093/ietfec/e88-a.3.669

Type of Manuscript: Special Section PAPER (Special Section on Adaptive Signal Processing and Its Applications)

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Kentaro IKEMOTO, Ryuji KOHNO, "Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique" in IEICE TRANSACTIONS on Fundamentals, vol. E88-A, no. 3, pp. 669-675, March 2005, doi: 10.1093/ietfec/e88-a.3.669.
Abstract: In order to achieve adaptive channel coding and adaptive modulation, the main causes of degradation to system performance are the decoder selection error and modulator estimation error. The utilization of supplementary information, in an estimation system utilizing channel estimation results, blind modulation estimation, and blind encoder estimation using several decoders information and encoder transitions have been considered to overcome these two problems. There are however many issues in these methods, such as the channel estimation difference between transmitter and receiver, computational complexity and the assumption of perfect Channel State Information (CSI). Our proposal, on the other hand, decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM). In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns using both encoder and modulator transition probability based on the HMM obtained by CSI and also Decoder and Demodulator Selection Error probabilities. Therefore, the decoder and demodulation results can be achieved efficiently without any restraint on the pattern of switching encoder and modulation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.3.669/_p

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@ARTICLE{e88-a_3_669,
author={Kentaro IKEMOTO, Ryuji KOHNO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique},
year={2005},
volume={E88-A},
number={3},
pages={669-675},
abstract={In order to achieve adaptive channel coding and adaptive modulation, the main causes of degradation to system performance are the decoder selection error and modulator estimation error. The utilization of supplementary information, in an estimation system utilizing channel estimation results, blind modulation estimation, and blind encoder estimation using several decoders information and encoder transitions have been considered to overcome these two problems. There are however many issues in these methods, such as the channel estimation difference between transmitter and receiver, computational complexity and the assumption of perfect Channel State Information (CSI). Our proposal, on the other hand, decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM). In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns using both encoder and modulator transition probability based on the HMM obtained by CSI and also Decoder and Demodulator Selection Error probabilities. Therefore, the decoder and demodulation results can be achieved efficiently without any restraint on the pattern of switching encoder and modulation.},
keywords={},
doi={10.1093/ietfec/e88-a.3.669},
ISSN={},
month={March},}

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TY - JOUR
TI - Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 669
EP - 675
AU - Kentaro IKEMOTO
AU - Ryuji KOHNO
PY - 2005
DO - 10.1093/ietfec/e88-a.3.669
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2005
AB - In order to achieve adaptive channel coding and adaptive modulation, the main causes of degradation to system performance are the decoder selection error and modulator estimation error. The utilization of supplementary information, in an estimation system utilizing channel estimation results, blind modulation estimation, and blind encoder estimation using several decoders information and encoder transitions have been considered to overcome these two problems. There are however many issues in these methods, such as the channel estimation difference between transmitter and receiver, computational complexity and the assumption of perfect Channel State Information (CSI). Our proposal, on the other hand, decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM). In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns using both encoder and modulator transition probability based on the HMM obtained by CSI and also Decoder and Demodulator Selection Error probabilities. Therefore, the decoder and demodulation results can be achieved efficiently without any restraint on the pattern of switching encoder and modulation.
ER -