Multi-Dimensional Classification Algorithm for Automatic Modulation Recognition by Ouail Albairat

Multi-Dimensional Classification Algorithm for Automatic Modulation Recognition by Ouail Albairat

This thesis proposes an approach for modulation classification using existing features in a more efficient way. The Multi-Dimensional Classification Algorithm (MDCA) treats features extracted from signals of interest as elements with irrelevant identities, hence eliminating any dependence of the classifier on any particular feature. This design enables the use of any number of features, and the MDCA algorithm provides the capability to classify modulations in higher dimensions. The use of multiple features requires an equal number of data dimensions, and thus classification in as high a dimensional space as possible can improve final classification results. Finally, the MDCA algorithm uses a relatively small number of simple operations, which leads to a fast processing time. Simulation results for the MDCA algorithm demonstrate good potential. In particular, the MDCA consistently performed well (at SNR levels down to -10dB in some cases) and in identifying more modulation types.

SKU	Unavailable
ISBN 13	9781288326761
ISBN 10	1288326769
Title	Multi-Dimensional Classification Algorithm for Automatic Modulation Recognition
Author	Ouail Albairat
Condition	Unavailable
Binding Type	Paperback
Publisher	Biblioscholar
Year published	2012-11-21
Number of pages	142
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable