A Compressed Sensing Approach for Distribution Matching

@article{Dia2018ACS,
  title={A Compressed Sensing Approach for Distribution Matching},
  author={Mohamad Dia and Vahid Aref and Laurent Schmalen},
  journal={2018 IEEE International Symposium on Information Theory (ISIT)},
  year={2018},
  pages={1266-1270}
}
  • Mohamad Dia, Vahid Aref, Laurent Schmalen
  • Published in
    IEEE International Symposium…
    2018
  • Mathematics, Computer Science
  • In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary source via position modulation (PM). We then present a simple and exact matcher based on Gaussian signal quantization. At the receiver, the dematcher exploits the sparsity in the source and performs low-complexity dematching based on generalized… CONTINUE READING

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