Efficient Training of Volterra Series-Based Pre-distortion Filter Using Neural Networks
This work addresses the challenge of improving signal quality in high-speed communication systems, representing an incremental advance in pre-distortion filter training methods.
The authors tackled the problem of training Volterra series-based digital pre-distortion filters by introducing a neural network-based direct learning approach, achieving superior performance in simulations with a 64-QAM 64-GBaud transmitter under varying nonlinearity and noise conditions.
We present a simple, efficient "direct learning" approach to train Volterra series-based digital pre-distortion filters using neural networks. We show its superior performance over conventional training methods using a 64-QAM 64-GBaud simulated transmitter with varying transmitter nonlinearity and noisy conditions.