HOS-based mode classification for infomobility framework

Authors:
Andrea F. Cattoni;Marina Ottonello;Mirco Raffetto;Carlo S. Regazzoni
Affiliations:
DIBE University of Genova, Genova, Italy;DIBE University of Genova, Genova, Italy;DIBE University of Genova, Genova, Italy;DIBE University of Genova, Genova, Italy
Venue:
First International Workshop on Cognitive Wireless Networks
Year:
2007

Citing 12
Cited 0

Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents

The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents
The Behavior Language: User''s Guide

The Behavior Language: User''s Guide
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
Cognitive Radio Technology (Communications Engineering)

Cognitive Radio Technology (Communications Engineering)
Use of time-frequency analysis and neural networks for mode identification in a wireless software-defined radio approach

EURASIP Journal on Applied Signal Processing
An analysis of instantaneous frequency representation usingtime-frequency distributions-generalized Wigner distribution

IEEE Transactions on Signal Processing
A new concept for wireless reconfigurable receivers

IEEE Communications Magazine
Cognitive radio: brain-empowered wireless communications

IEEE Journal on Selected Areas in Communications
Spectrum sensing: A distributed approach for cognitive terminals

IEEE Journal on Selected Areas in Communications
The analysis of decomposition methods for support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

The growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. A possible solution for increasing the frequency reusage within the framework of info-mobility cellular systems is the joint exploitation of Smart Antennas and Cognitive Radio. Inside this framework a key-role is played by Mode Identification and Spectrum monitoring algorithms, useful to provide awareness about the channel conditions. In the paper a Mode Identification algorithm, based on the extraction of higher order statistics from frequency distribution of the involved communication modalities and multiple support vector machine classifiers, for a Cognitive Base Transceiver Station is presented. Simulated results, obtained in a simplified framework, will prove the effectiveness of the proposed approach.