Meta-ViterbiNet is a DNN-aided symbol detector that tracks channel variations with reduced overhead . It outperforms the previous best approach by a margin of up to 0.6dB in bit error rate in various challenging scenarios . It is based on a model-based/data-driven equalizer that operates with a relatively small number of trainable parameters . It also adopts a decision-directed approach based on coded communications to enable online training with short-length pilot blocks . The authors conclude that the new system outperforms previous best approaches by outperforming the best approach, based on ViterBiNet or conventional recurrent neural networks without meta-learning, by a . margin of . up to 1dB in error rates in some challenging scenarios in various . challenging scenarios. It is now available to download and use

Author(s) : Tomer Raviv, Sangwoo Park, Nir Shlezinger, Osvaldo Simeone, Yonina C. Eldar, Joonhyuk Kang

Links : PDF - Abstract

Code :
Coursera

Keywords : meta - based - scenarios - approach - viterbinet -

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