The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A multiplexação por divisão de frequência ortogonal (OFDM) é muito sensível ao deslocamento de frequência da portadora (CFO). A precisão da estimativa do CFO tem grande impacto no desempenho do OFDM. Neste artigo, é proposto um novo rastreamento de CFO conjunto bayesiano assistido por aprendizagem e estimativa de resposta ao impulso do canal. O algoritmo proposto é modificado a partir de um algoritmo Bayesiano de estimativa assistida por aprendizagem (BLAE) da literatura. O BLAE é baseado na maximização da expectativa (EM) e exibe o erro quadrático médio (MSE) do estimador inferior ao limite de Cramer-Rao (CRB) quando o valor do CFO está próximo de zero. No entanto, o seu valor MSE pode aumentar rapidamente à medida que o valor CFO se afasta de zero. Assim, o estimador CFO do BLAE é substituído para resolver o problema. Originalmente, o critério de projeto do estimador CFO de amostra única (STS) na literatura é baseado em máxima verossimilhança (ML). Seu desempenho MSE pode chegar ao CRB. Além disso, sua faixa de estimativa de CFO pode atingir a faixa mais ampla necessária para um estimador de rastreamento de CFO. Para um CFO normalizado pelo espaçamento da subportadora, a faixa de rastreamento mais ampla necessária é de -0.5 a +0.5. Aqui, aplicamos o método de design do estimador STS CFO à estrutura de aprendizagem bayesiana baseada em EM. O algoritmo STS Bayesiano assistido por aprendizagem resultante exibe o desempenho do MSE inferior ao CRB, e sua faixa de estimativa do CFO está entre ± 0.5. Com esse critério de projeto de aprendizagem Bayesiano, a potência adicional de ruído do canal e o perfil de atraso de potência devem ser estimados, em comparação com o critério de projeto baseado em ML. Com as informações estatísticas adicionais do canal, o algoritmo derivado apresenta desempenho do MSE melhor que o CRB. Dois canais seletivos de frequência são adotados para simulações computacionais. Um tem pesos fixos e o outro é o desvanecimento Rayleigh. Também são fornecidas comparações com os algoritmos mais relacionados.
Hong-Yu LIU
Fu Jen Catholic University
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Hong-Yu LIU, "Bayesian Learning-Assisted Joint Frequency Tracking and Channel Estimation for OFDM Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 10, pp. 1336-1342, October 2023, doi: 10.1587/transfun.2022EAP1167.
Abstract: Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem. Originally, the design criterion of the single-time-sample (STS) CFO estimator in the literature is maximum likelihood (ML)-based. Its MSE performance can reach the CRB. Also, its CFO estimation range can reach the widest range required for a CFO tracking estimator. For a CFO normalized by the sub-carrier spacing, the widest tracking range required is from -0.5 to +0.5. Here, we apply the STS CFO estimator design method to the EM-based Bayesian learning framework. The resultant Bayesian learning-assisted STS algorithm displays the MSE performance lower than the CRB, and its CFO estimation range is between ±0.5. With such a Bayesian learning design criterion, the additional channel noise power and power delay profile must be estimated, as compared with the ML-based design criterion. With the additional channel statistical information, the derived algorithm presents the MSE performance better than the CRB. Two frequency-selective channels are adopted for computer simulations. One has fixed tap weights, and the other is Rayleigh fading. Comparisons with the most related algorithms are also been provided.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1167/_p
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@ARTICLE{e106-a_10_1336,
author={Hong-Yu LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Bayesian Learning-Assisted Joint Frequency Tracking and Channel Estimation for OFDM Systems},
year={2023},
volume={E106-A},
number={10},
pages={1336-1342},
abstract={Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem. Originally, the design criterion of the single-time-sample (STS) CFO estimator in the literature is maximum likelihood (ML)-based. Its MSE performance can reach the CRB. Also, its CFO estimation range can reach the widest range required for a CFO tracking estimator. For a CFO normalized by the sub-carrier spacing, the widest tracking range required is from -0.5 to +0.5. Here, we apply the STS CFO estimator design method to the EM-based Bayesian learning framework. The resultant Bayesian learning-assisted STS algorithm displays the MSE performance lower than the CRB, and its CFO estimation range is between ±0.5. With such a Bayesian learning design criterion, the additional channel noise power and power delay profile must be estimated, as compared with the ML-based design criterion. With the additional channel statistical information, the derived algorithm presents the MSE performance better than the CRB. Two frequency-selective channels are adopted for computer simulations. One has fixed tap weights, and the other is Rayleigh fading. Comparisons with the most related algorithms are also been provided.},
keywords={},
doi={10.1587/transfun.2022EAP1167},
ISSN={1745-1337},
month={October},}
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TY - JOUR
TI - Bayesian Learning-Assisted Joint Frequency Tracking and Channel Estimation for OFDM Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1336
EP - 1342
AU - Hong-Yu LIU
PY - 2023
DO - 10.1587/transfun.2022EAP1167
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2023
AB - Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem. Originally, the design criterion of the single-time-sample (STS) CFO estimator in the literature is maximum likelihood (ML)-based. Its MSE performance can reach the CRB. Also, its CFO estimation range can reach the widest range required for a CFO tracking estimator. For a CFO normalized by the sub-carrier spacing, the widest tracking range required is from -0.5 to +0.5. Here, we apply the STS CFO estimator design method to the EM-based Bayesian learning framework. The resultant Bayesian learning-assisted STS algorithm displays the MSE performance lower than the CRB, and its CFO estimation range is between ±0.5. With such a Bayesian learning design criterion, the additional channel noise power and power delay profile must be estimated, as compared with the ML-based design criterion. With the additional channel statistical information, the derived algorithm presents the MSE performance better than the CRB. Two frequency-selective channels are adopted for computer simulations. One has fixed tap weights, and the other is Rayleigh fading. Comparisons with the most related algorithms are also been provided.
ER -