Optimal UWB Waveform Design Based on Radial Basis Function Neural Networks

  • Authors:

  • Bin Li;Zheng Zhou;Weixia Zou;Dejian Li;Lu Feng

  • Affiliations:

  • Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China 100876;Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China 100876;Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China 100876;Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China 100876;Key Lab of Universal Wireless Communications, MOE Wireless Network Lab, Beijing University of Posts and Telecommunications, Beijing, China 100876

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2012

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Abstract

We present a novel ultra-wideband waveform design algorithm based on the radial basis function neural network in this paper. The simplified implementation of this scheme is also put forward. With the aid of our proposed spectrum pruning technique, the produced waveforms can match arbitrary spectrum emission mask much more closely under the regulatory spectral constraint. Moreover, by taking the nonideal response of realistic UWB antennas into waveform designing process, the emission pulses with predistortion can be generated to efficiently compensate for the nonideal UWB antenna property. Two realization structures have also been investigated. Consequently, degradation in frequency utilization caused by any nonideal antenna can be substantially eliminated, and hence the practical transmission performance can be significantly enhanced.