Communication-channel tracking aided by reinforcement learning

Abstract

A digital circuit for implementing a channel-tracking functionality, in which an adaptive (e.g., FIR) filter is updated based on reinforcement learning. In an example embodiment, the adaptive filter may be updated using an LMS-type algorithm. The digital circuit may also include an electronic controller configured to change the convergence coefficient of the LMS algorithm using a selection policy learned by applying a reinforcement-learning technique and based on residual errors and channel estimates received over a sequence of iterations. In some embodiments, the electronic controller may include an artificial neural network. An example embodiment of the digital circuit is advantageously capable of providing improved performance after the learning phase, e.g., for communication channels exhibiting variable dynamicity patterns, such as those associated with aerial copper cables or some wireless channels.