"Machine LLRning": Learning to Softly Demodulate

@article{Shental2019MachineLL,
  title={"Machine LLRning": Learning to Softly Demodulate},
  author={O. Shental and J. Hoydis},
  journal={2019 IEEE Globecom Workshops (GC Wkshps)},
  year={2019},
  pages={1-7}
}
  • O. Shental, J. Hoydis
  • Published 2019
  • Computer Science, Mathematics
  • 2019 IEEE Globecom Workshops (GC Wkshps)
Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver. In this paper, a trainable universal neural network-based demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an improved performance with significantly reduced overall computational complexity. For instance for the commonly used quadrature amplitude modulation (QAM), LLRnet demonstrates LLR estimates… Expand
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