Epsilon greedy strategy for hyper parameters tuning of a neural network equalizer

In wireless communications, equalization can be used to remove channel impairments from transmissions. Neural networks (NNs) have proven to be an effective technique against conventional equalizers (i.e. decision-feedback, zero-forcing, etc.). High Frequency (HF) channels require high-performance equalizers to overcome Doppler shifts and large delay spreads. When using a NN equalizer, tuning its structure (i.e. activation function, optimizer, etc...) can be time-consuming. This work proposes using an annealing epsilon greedy algorithm, a reinforcement learning technique, to tune the attributes of a neural network equalizer. Reinforcement learning has been used to tune NNs in different applications, but to the best of our knowledge, it has not been done for NN equalization. The objective of this work is to analyze if using reinforcement learning can improve the performance of a NN equalizer.