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With the growing demand for high-bandwidth applications like video streamingand cloud services, the data transfer rates required for wireline communicationkeeps increasing, making the channel loss a major obstacle in achieving low biterror rate (BER). Equalization techniques such as feed-forward equalizer (FFE)and decision feedback equalizer (DFE) are commonly used to compensate forchannel loss in wireline communication, but they have limitations in terms ofnoise boosting and timing constraints. On the other hand, the forward-backwardalgorithm can achieve better BER performance, but its high complexity makes itimpractical for wireline communication. In this work, we propose a novel neuralnetwork, NeuralEQ, that effectively mimics the forward-backward algorithm andperforms better than FFE and DFE while reducing complexity of theforward-backward algorithm. Performance of NeuralEQ is verified throughsimulations using real channels.

NeuralEQ: Neural-Network-Based Equalizer for High-Speed Wireline Communication