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To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feed-forward structure. The latter shows 38% latency decrease, while impacting the Q-factor by only 0.5 dB.
© 2023 The Author(s)
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