"Machine LLRning": Learning to Softly Demodulate

  title={"Machine LLRning": Learning to Softly Demodulate},
  author={O. Shental and J. Hoydis},
  journal={2019 IEEE Globecom Workshops (GC Wkshps)},
  • 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
8 Citations
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer
  • PDF
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
  • PDF
Neural Network MIMO Detection for Coded Wireless Communication with Impairments
  • 3
  • PDF
Neural Network-Based Soft-Demapping for Nonlinear Channels
  • 4
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization
  • Highly Influenced
  • PDF
DeepRx: Fully Convolutional Deep Learning Receiver
  • 6
  • PDF


A Universal Low-Complexity Symbol-to-Bit Soft Demapper
  • 48
  • PDF
Simplified soft-output demapper for binary interleaved COFDM with application to HIPERLAN/2
  • F. Tosato, P. Bisaglia
  • Computer Science
  • 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333)
  • 2002
  • 372
  • Highly Influential
  • PDF
Reduced complexity symbol detectors with parallel structure for ISI channels
  • 161
  • PDF
A low-complexity soft QAM de-mapper based on first-order linear approximation
  • 1
A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain
  • 1,759
  • PDF
training feedforward networks with the marquardt algorithm
  • 1,665
  • PDF
Approximation by superpositions of a sigmoidal function
  • G. Cybenko
  • Mathematics, Computer Science
  • Math. Control. Signals Syst.
  • 1989
  • 4,169
  • PDF