Simple Search:

A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system


Citation

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).

Abstract / Synopsis

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.


Download File

[img]
Preview
PDF (Abstract)
A channel quality indicator (CQI) prediction scheme using feed forward neural network (FF-NN) technique for MU-MIMO LTE system.pdf

Download (40kB) | Preview

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
Institute of Gerontology
DOI Number: https://doi.org/10.1109/ISTT.2014.7238169
Publisher: IEEE
Keywords: CQI feedback; Feed forward neural network technique; LTE; MU-MIMO
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 28 Oct 2015 03:15
Last Modified: 01 Dec 2016 08:32
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ISTT.2014.7238169
URI: http://psasir.upm.edu.my/id/eprint/41159
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item