Reinforcement Learning for Standards Design
This addresses inefficiencies in standards committees for telecommunications, though it appears incremental as it applies existing RL techniques to a new domain.
The paper tackles the slow, human-driven process of designing communication standards by proposing a reinforcement learning method to automate the selection of modulation and coding schemes, aiming to streamline standards design and extension.
Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved. This includes decisions regarding the modulation and coding schemes to be supported over an air interface. We propose a way to "automate" the selection of the set of modulation and coding schemes to be supported over a given air interface and thereby streamline both the standards design process and the ease of extending the standard to support new modulation schemes applicable to new higher-level applications and services. Our scheme involves machine learning, whereby a constructor entity submits proposals to an evaluator entity, which returns a score for the proposal. The constructor employs reinforcement learning to iterate on its submitted proposals until a score is achieved that was previously agreed upon by both constructor and evaluator to be indicative of satisfying the required design criteria (including performance metrics for transmissions over the interface).