Energy efficient water filling ultra wideband waveform shaping based on radius basis function neural networks

  • Authors:

  • Weixia Zou;Bin Li;Zheng Zhou;Shubin Wang

  • Affiliations:

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

  • Venue:
  • WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
  • Year:
  • 2010

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Abstract

In the emerging energy efficient framework, power allocation for ultra wide band (UWB) is much significant given its extremely large bandwidth. For multi-band UWB, this area has been extensively researched in the context of OFDM resources allocation. For pulse-based UWB, however, there is still an urgent need for efficient waveform design technique to embody arbitrary power allocation strategy. In this paper, we present a UWB waveform design method based on the radius basis network neural networks (RBF). The power density spectrum of emitted waveform is firstly abstract to a general mathematic function. Then based on the interpolation theory, RBF network is adopted to generate UWB waveforms given any spectrum shape. Numerical simulations validate our algorithms through the water filling (WF) waveforms shaping.