Waveform Design With Unit Modulus and Spectral Shape Constraints via Lagrange Programming Neural Network

Junli Liang, Hing Cheung So, Chi Sing Leung, Jian Li, Alfonso Farina

Research output: Contribution to journalArticlepeer-review

82 Scopus citations

Abstract

To maximize the transmitted power available in active sensing, the probing waveform should be of constant modulus. On the other hand, in order to adapt to the increasingly crowed radio frequency spectrum and prevent mutual interferences, there are also requirements in the waveform spectral shape. That is to say, the waveform must fulfill constraints in both time and frequency domains. In this work, designing these waveforms is formulated as a nonlinear constrained optimization problem. By introducing auxiliary variable neurons and Lagrange neurons, we solve it using the Lagrange programming neural network. We also analyze the local stability conditions of the dynamic neuron model. Simulation results show that our proposed algorithm is a competitive alternative for waveform design with unit modulus and arbitrary spectral shapes.

Original languageEnglish
Article number7174964
Pages (from-to)1377-1386
Number of pages10
JournalIEEE Journal on Selected Topics in Signal Processing
Volume9
Issue number8
DOIs
StatePublished - Dec 2015

Keywords

  • Active sensing
  • Lagrange programming neural network
  • nonlinear constrained optimization
  • spectral shape
  • unit modulus
  • waveform design

Fingerprint

Dive into the research topics of 'Waveform Design With Unit Modulus and Spectral Shape Constraints via Lagrange Programming Neural Network'. Together they form a unique fingerprint.

Cite this