A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system
In Multi User-Multiple-in Multiple-Out - Long Term Evolution (MU-MIMO-LTE) networks, Channel Quality indicator (CQI) plays a vital role. CQI is crucial in describing the channel information to assign appropriate modulation and coding scheme (MCS). However, obtaining CQI values for each transmission...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41159/ http://psasir.upm.edu.my/id/eprint/41159/1/A%20channel%20quality%20indicator%20%28CQI%29%20prediction%20scheme%20using%20feed%20forward%20neural%20network%20%28FF-NN%29%20technique%20for%20MU-MIMO%20LTE%20system.pdf |
| _version_ | 1848849620598259712 |
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| author | Abdulhasan, Muntadher Qasim Salman, Mustafa Ismael Ng, Chee Kyun Noordin, Nor Kamariah Hashim, Shaiful Jahari Hashim, Fazirulhisham |
| author_facet | Abdulhasan, Muntadher Qasim Salman, Mustafa Ismael Ng, Chee Kyun Noordin, Nor Kamariah Hashim, Shaiful Jahari Hashim, Fazirulhisham |
| author_sort | Abdulhasan, Muntadher Qasim |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In Multi User-Multiple-in Multiple-Out - Long Term Evolution (MU-MIMO-LTE) networks, Channel Quality indicator (CQI) plays a vital role. CQI is crucial in describing the channel information to assign appropriate modulation and coding scheme (MCS). However, obtaining CQI values for each transmission time interval (TTI) inevitably entails use and can lead to an undesirable degradation in spectral efficiency (SE) as well as increasing the error rate. Therefore, providing an accurate and reliable CQI with low overhead is an intricate task. In this paper, a CQI prediction scheme using Feed Forward-Neural Network (FF-NN) algorithm for MU-MIMO-LTE Advanced systems is proposed. Initially, a channel model for MU-MIMO-LTE advanced network is carried out. Through this model, CQI is predicted and the obtained values are compressed using a feedback compression technique. Finally, the proposed technique makes use of FF-NN algorithm to train and achieve enhanced CQI values. Further, an enhanced and accurate CQI values are acquired. Results show that the system SE of single user (SU)-MIMO proportionally increases with the SNR values at the cost of BER. Therefore, a MU-MIMO CQI prediction scheme is recommended to improve the tradeoff between BER and SE. |
| first_indexed | 2025-11-15T09:53:18Z |
| format | Conference or Workshop Item |
| id | upm-41159 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:53:18Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-411592016-12-01T08:32:13Z http://psasir.upm.edu.my/id/eprint/41159/ A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system Abdulhasan, Muntadher Qasim Salman, Mustafa Ismael Ng, Chee Kyun Noordin, Nor Kamariah Hashim, Shaiful Jahari Hashim, Fazirulhisham In Multi User-Multiple-in Multiple-Out - Long Term Evolution (MU-MIMO-LTE) networks, Channel Quality indicator (CQI) plays a vital role. CQI is crucial in describing the channel information to assign appropriate modulation and coding scheme (MCS). However, obtaining CQI values for each transmission time interval (TTI) inevitably entails use and can lead to an undesirable degradation in spectral efficiency (SE) as well as increasing the error rate. Therefore, providing an accurate and reliable CQI with low overhead is an intricate task. In this paper, a CQI prediction scheme using Feed Forward-Neural Network (FF-NN) algorithm for MU-MIMO-LTE Advanced systems is proposed. Initially, a channel model for MU-MIMO-LTE advanced network is carried out. Through this model, CQI is predicted and the obtained values are compressed using a feedback compression technique. Finally, the proposed technique makes use of FF-NN algorithm to train and achieve enhanced CQI values. Further, an enhanced and accurate CQI values are acquired. Results show that the system SE of single user (SU)-MIMO proportionally increases with the SNR values at the cost of BER. Therefore, a MU-MIMO CQI prediction scheme is recommended to improve the tradeoff between BER and SE. IEEE 2014 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41159/1/A%20channel%20quality%20indicator%20%28CQI%29%20prediction%20scheme%20using%20feed%20forward%20neural%20network%20%28FF-NN%29%20technique%20for%20MU-MIMO%20LTE%20system.pdf Abdulhasan, Muntadher Qasim and Salman, Mustafa Ismael and Ng, Chee Kyun and Noordin, Nor Kamariah and Hashim, Shaiful Jahari and Hashim, Fazirulhisham (2014) A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system. In: 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), 24-26 Nov. 2014, Langkawi, Kedah, Malaysia. (pp. 17-22). 10.1109/ISTT.2014.7238169 |
| spellingShingle | Abdulhasan, Muntadher Qasim Salman, Mustafa Ismael Ng, Chee Kyun Noordin, Nor Kamariah Hashim, Shaiful Jahari Hashim, Fazirulhisham A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title | A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title_full | A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title_fullStr | A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title_full_unstemmed | A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title_short | A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system |
| title_sort | channel quality indicator (cqi) prediction scheme using feed forward neural network (ff-nn) technique for mu-mimo lte system |
| url | http://psasir.upm.edu.my/id/eprint/41159/ http://psasir.upm.edu.my/id/eprint/41159/ http://psasir.upm.edu.my/id/eprint/41159/1/A%20channel%20quality%20indicator%20%28CQI%29%20prediction%20scheme%20using%20feed%20forward%20neural%20network%20%28FF-NN%29%20technique%20for%20MU-MIMO%20LTE%20system.pdf |