Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices
Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G N...
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9757170/ |
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| author | Vipin Sharma Rajeev Kumar Arya Sandeep Kumar |
| author_facet | Vipin Sharma Rajeev Kumar Arya Sandeep Kumar |
| author_sort | Vipin Sharma |
| collection | DOAJ |
| description | Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is ~66% more efficient as compared to the conventional MMSE method for BPSK mapping. |
| format | Article |
| id | doaj-art-c45ba861ff814d598659d71a6ab2ec60 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c45ba861ff814d598659d71a6ab2ec602025-08-20T03:18:15ZengIEEEIEEE Access2169-35362022-01-0110450534506010.1109/ACCESS.2022.31674429757170Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC DevicesVipin Sharma0https://orcid.org/0000-0003-0007-0079Rajeev Kumar Arya1https://orcid.org/0000-0002-0346-2150Sandeep Kumar2https://orcid.org/0000-0003-4658-4497Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, IndiaEfficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is ~66% more efficient as compared to the conventional MMSE method for BPSK mapping.https://ieeexplore.ieee.org/document/9757170/mMTC5G (NR)AMCBERdeep learningSNR |
| spellingShingle | Vipin Sharma Rajeev Kumar Arya Sandeep Kumar Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices IEEE Access mMTC 5G (NR) AMC BER deep learning SNR |
| title | Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices |
| title_full | Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices |
| title_fullStr | Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices |
| title_full_unstemmed | Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices |
| title_short | Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices |
| title_sort | efficient channel prediction technique using amc and deep learning algorithm for 5g nr mmtc devices |
| topic | mMTC 5G (NR) AMC BER deep learning SNR |
| url | https://ieeexplore.ieee.org/document/9757170/ |
| work_keys_str_mv | AT vipinsharma efficientchannelpredictiontechniqueusingamcanddeeplearningalgorithmfor5gnrmmtcdevices AT rajeevkumararya efficientchannelpredictiontechniqueusingamcanddeeplearningalgorithmfor5gnrmmtcdevices AT sandeepkumar efficientchannelpredictiontechniqueusingamcanddeeplearningalgorithmfor5gnrmmtcdevices |