Paper Info
Title
CSIT: channel state and idle time predictor using a neural network for cognitive LTE-Advanced network.
Abstract
Cognitive radio (CR) is a novel methodology that facilitates unlicensed users to share a licensed spectrum without interfering with licensed users. This intriguing approach is exploited in the Long Term Evolution-Advanced (LTE-A) network for performance improvement. Although LTE-A is the foremost mobile communication standard, future underutilization of the spectrum needs to be addressed. Therefore, dynamic spectrum access is explored in this study. The performance of CR in LTE-A can significantly be enhanced by employing predictive modeling. The neural network-based channel state and idle time (CSIT) predictor is proposed in this article as a learning scheme for CR in LTE-A. This predictive-based learning is helpful in two ways: sensing only those channels that are predicted to be idle and selecting the channels for CR transmission that have the largest predicted idle time. The performance gains by exploiting CSIT prediction in CR LTE-A network are evaluated in terms of spectrum utilization, sensing energy, channel switching rate, packet loss ratio, and average instantaneous throughput. The results illustrate that significant performance is achieved by employing CSIT prediction in LTE-A network.
Year
DOI
Venue
2013
10.1186/1687-1499-2013-203
EURASIP J. Wireless Comm. and Networking
Keywords
DocType
Volume
Cognitive radio, LTE-Advanced, Slot(s) state, Idle time, Neural network and multilayer perceptron
Journal
2013
Issue
ISSN
Citations 
1
1687-1499
11
PageRank 
References 
Authors
0.44
16
4
Name
Order
Citations
PageRank
Adnan Shahid113816.71
Saleem Aslam2917.52
Hyung Joo Kim3362.84
Kyung-Geun Lee412814.61