Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation
In this article, the challenges of effective channel estimation for the lognormal-Rician turbulence model are addressed. We present a novel maximum likelihood estimation algorithm involving a saddlepoint approximation (SAP) method to estimate the shaping parameters of the lognormal-Rician distributi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9167206/ |
| _version_ | 1829139854362935296 |
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| author | Maoke Miao Xiaofeng Li |
| author_facet | Maoke Miao Xiaofeng Li |
| author_sort | Maoke Miao |
| collection | DOAJ |
| description | In this article, the challenges of effective channel estimation for the lognormal-Rician turbulence model are addressed. We present a novel maximum likelihood estimation algorithm involving a saddlepoint approximation (SAP) method to estimate the shaping parameters of the lognormal-Rician distribution. An additional parameter k needs to be estimated in addition to r and σ<sub>z</sub><sup>2</sup> under the SAP representation. The accuracy of the proposed estimator is investigated by using the mean square error and normalized mean square error. The simulated results show that the proposed estimator exhibits satisfactory performance over a wide range of turbulence conditions, and it can be easily applied to both noiseless and noisy situations. The effect of the estimated shaping parameters errors on the bit error rate (BER) for the on-off key (OOK) modulation is also investigated; it is shown that the BER performance derived with the SAP estimator becomes closer to the system performance with perfect shaping parameters as r increases. In particular, we present a qualitative comparison between the proposed SAP estimator and other algorithms available in the literature. |
| first_indexed | 2024-12-14T19:43:02Z |
| format | Article |
| id | doaj.art-ff5e9aac1eee47c9b0ca6c874c4faeb0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:43:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ff5e9aac1eee47c9b0ca6c874c4faeb02022-12-21T22:49:39ZengIEEEIEEE Access2169-35362020-01-01815292415293110.1109/ACCESS.2020.30166839167206Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint ApproximationMaoke Miao0https://orcid.org/0000-0001-5220-0926Xiaofeng Li1https://orcid.org/0000-0002-0490-1717School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, ChinaIn this article, the challenges of effective channel estimation for the lognormal-Rician turbulence model are addressed. We present a novel maximum likelihood estimation algorithm involving a saddlepoint approximation (SAP) method to estimate the shaping parameters of the lognormal-Rician distribution. An additional parameter k needs to be estimated in addition to r and σ<sub>z</sub><sup>2</sup> under the SAP representation. The accuracy of the proposed estimator is investigated by using the mean square error and normalized mean square error. The simulated results show that the proposed estimator exhibits satisfactory performance over a wide range of turbulence conditions, and it can be easily applied to both noiseless and noisy situations. The effect of the estimated shaping parameters errors on the bit error rate (BER) for the on-off key (OOK) modulation is also investigated; it is shown that the BER performance derived with the SAP estimator becomes closer to the system performance with perfect shaping parameters as r increases. In particular, we present a qualitative comparison between the proposed SAP estimator and other algorithms available in the literature.https://ieeexplore.ieee.org/document/9167206/Maximum likelihood estimation (MLE)SAP methodlognormal-Rician turbulence modelOOK modulationqualitative comparison |
| spellingShingle | Maoke Miao Xiaofeng Li Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation IEEE Access Maximum likelihood estimation (MLE) SAP method lognormal-Rician turbulence model OOK modulation qualitative comparison |
| title | Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation |
| title_full | Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation |
| title_fullStr | Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation |
| title_full_unstemmed | Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation |
| title_short | Parameter Estimation of the Lognormal-Rician Channel Model Using Saddlepoint Approximation |
| title_sort | parameter estimation of the lognormal rician channel model using saddlepoint approximation |
| topic | Maximum likelihood estimation (MLE) SAP method lognormal-Rician turbulence model OOK modulation qualitative comparison |
| url | https://ieeexplore.ieee.org/document/9167206/ |
| work_keys_str_mv | AT maokemiao parameterestimationofthelognormalricianchannelmodelusingsaddlepointapproximation AT xiaofengli parameterestimationofthelognormalricianchannelmodelusingsaddlepointapproximation |