Open Access

Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach

EURASIP Journal on Advances in Signal Processing20042004:863653

DOI: 10.1155/S1110865704407057

Received: 4 September 2003

Published: 29 September 2004


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.

Keywords and phrases

mode identification software-defined radio frequency hopping code division multiple access direct sequence code division multiple access time-frequency analysis pattern recognition

Authors’ Affiliations

Signal Processing and Telecommunication Group (SP&T), Biophysical and Electronic Engineering Department, University of Genoa


© Gandetto et al. 2004