Evaluation of Adaptation Methods for Deep Learning-based Wi-Fi Receivers

Abstract

Machine-learning based transceivers have received increasing attention for next-generation wireless systems. We investigate the application of two meta-learning algorithms – Model Agnostic Meta Learning (MAML) and Reptile – to a deep learning-based Wi-Fi channel estimation and tracking system, called DeepWiPHY. The meta-learning algorithms were compared against conventional methods such as random initialization, cross-evaluation, and retraining on multiple channel models with varying severity of multipath fading. Comparisons were made fairly with respect to the complexity of the adaptation of the model necessary for a new environment. The results indicate that perhaps surprisingly, conventional training methods are adequate and in fact can outperform meta-learning methods over a wide variety of channels. The key is to train the receiver using the worst-case (most severe) multipath channel model, which then allows strong performance across a wide class of channels without requiring the additional burden of meta-learning.