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

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.

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Correspondence to Matteo Gandetto.

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Gandetto, M., Guainazzo, M. & Regazzoni, C.S. Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach. EURASIP J. Adv. Signal Process. 2004, 863653 (2004). https://doi.org/10.1155/S1110865704407057

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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