Use of time-frequency analysis and neural networks for mode identification in a wireless software-defined radio approach

Authors:
Matteo Gandetto;Marco Guainazzo;Carlo S. Regazzoni
Affiliations:
Signal Processing and Telecommunication Group (SP&T), Biophysical and Electronic Engineering Department, University of Genoa, Genoa, Italy;Signal Processing and Telecommunication Group (SP&T), Biophysical and Electronic Engineering Department, University of Genoa, Genoa, Italy;Signal Processing and Telecommunication Group (SP&T), Biophysical and Electronic Engineering Department, University of Genoa, Genoa, Italy
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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Abstract

The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical), are considered: IEEE WLAN 802.11b (direct sequence) and Bluetooth (frequency hopping). Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency.