Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM
Multiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are...
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MDPI AG
2022-11-01
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| Online Access: | https://www.mdpi.com/1099-4300/24/11/1601 |
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| author | Dhanasekaran S SatheeshKumar Palanisamy Fahima Hajjej Osamah Ibrahim Khalaf Ghaida Muttashar Abdulsahib Ramalingam S |
| author_facet | Dhanasekaran S SatheeshKumar Palanisamy Fahima Hajjej Osamah Ibrahim Khalaf Ghaida Muttashar Abdulsahib Ramalingam S |
| author_sort | Dhanasekaran S |
| collection | DOAJ |
| description | Multiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are highly complicated. A channel estimation algorithm is needed to estimate the received signal’s correctness. In order to resolve this complexity in C.E. methodologies, this paper developed an Improved Channel Estimation Algorithm integrated with DFT-LS-WIENER (ICEA-DA). The Least Square (L.S.) and Minimum Mean Square Error (MMSE) algorithms also use the Discrete Fourier Transform (DFT)-based channel estimation method. The DFT-LS-WIENER channel estimation approach is recommended for better BER performance. The input signal is modulated in the transmitter module using the Quadrature Phase Shift Keying (QPSK) technique, pulse modeling, and least squares concepts. The L.S. Estimation technique needs the channel consistent throughout the estimation period. DFT joined with L.S. gives higher estimation precision and limits M.S.E. and BER. Experimental analysis of the proposed state-of-the-art method shows that DFT-LS-WIENER provides superior performance in terms of symbol error rate (S.E.R.), bit error rate (BER), channel capacity (CC), and peak signal-to-noise (PSNR). At 15 dB SNR, the proposed DFT-LS-WIENER techniques reduce the BER of 48.19%, 38.19%, 14.8%, and 14.03% compared to L.S., LS-DFT, MMSE, and MMSE-DFT. Compared to the conventional algorithm, the proposed DFT-LS-WIENER outperform them. |
| first_indexed | 2024-03-09T19:05:34Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T19:05:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj.art-842cb7e96571426caf41652a7170fee12023-11-24T04:36:55ZengMDPI AGEntropy1099-43002022-11-012411160110.3390/e24111601Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDMDhanasekaran S0SatheeshKumar Palanisamy1Fahima Hajjej2Osamah Ibrahim Khalaf3Ghaida Muttashar Abdulsahib4Ramalingam S5Department of E.C.E., Sri Eshwar College of Engineering, Coimbatore 641202, IndiaDepartment of E.C.E., Coimbatore Institute of Technology, Coimbatore 641014, IndiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaAl-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad 64074, IraqDepartment of Computer Engineering, University of Technology, Baghdad 10066, IraqDepartment of E.C.E., Sri Eshwar College of Engineering, Coimbatore 641202, IndiaMultiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are highly complicated. A channel estimation algorithm is needed to estimate the received signal’s correctness. In order to resolve this complexity in C.E. methodologies, this paper developed an Improved Channel Estimation Algorithm integrated with DFT-LS-WIENER (ICEA-DA). The Least Square (L.S.) and Minimum Mean Square Error (MMSE) algorithms also use the Discrete Fourier Transform (DFT)-based channel estimation method. The DFT-LS-WIENER channel estimation approach is recommended for better BER performance. The input signal is modulated in the transmitter module using the Quadrature Phase Shift Keying (QPSK) technique, pulse modeling, and least squares concepts. The L.S. Estimation technique needs the channel consistent throughout the estimation period. DFT joined with L.S. gives higher estimation precision and limits M.S.E. and BER. Experimental analysis of the proposed state-of-the-art method shows that DFT-LS-WIENER provides superior performance in terms of symbol error rate (S.E.R.), bit error rate (BER), channel capacity (CC), and peak signal-to-noise (PSNR). At 15 dB SNR, the proposed DFT-LS-WIENER techniques reduce the BER of 48.19%, 38.19%, 14.8%, and 14.03% compared to L.S., LS-DFT, MMSE, and MMSE-DFT. Compared to the conventional algorithm, the proposed DFT-LS-WIENER outperform them.https://www.mdpi.com/1099-4300/24/11/1601MIMOOFDMleast square estimatorchannel estimationminimum mean square errordiscrete Fourier transform |
| spellingShingle | Dhanasekaran S SatheeshKumar Palanisamy Fahima Hajjej Osamah Ibrahim Khalaf Ghaida Muttashar Abdulsahib Ramalingam S Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM Entropy MIMO OFDM least square estimator channel estimation minimum mean square error discrete Fourier transform |
| title | Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM |
| title_full | Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM |
| title_fullStr | Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM |
| title_full_unstemmed | Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM |
| title_short | Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM |
| title_sort | discrete fourier transform with denoise model based least square wiener channel estimator for channel estimation in mimo ofdm |
| topic | MIMO OFDM least square estimator channel estimation minimum mean square error discrete Fourier transform |
| url | https://www.mdpi.com/1099-4300/24/11/1601 |
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