A reinforcement learning-based qam/psk symbol synchronizer

HIGHLIGHTS

• who: Symbol Synchronizer and collaborators from the of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy have published the research work: A Reinforcement Learning-Based QAM/PSK Symbol Synchronizer, in the Journal: (JOURNAL)
• what: The authors propose the design of an RL Agent able to learn the behavior of a Timing Recovery Loop (TRL) through the Q-Learning algorithm. The model is self-adapting using the data collected in the field. In Sect IV the authors provide the experimental results regarding the choice of the Q-Learning hyperparameters and the authors compare the performance . . .
  

   

  

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