Reinforcement Learning for Standards Design

TL;DR

This paper introduces a reinforcement learning-based approach to automate and streamline the design of communication standards, specifically for selecting modulation and coding schemes, reducing the need for lengthy human committee processes.

Contribution

It presents a novel reinforcement learning framework for automating standards design, enabling efficient proposal evaluation and iterative improvement of modulation schemes.

Findings

The proposed method successfully automates modulation scheme selection.

Reinforcement learning accelerates standards development process.

The approach adapts to new higher-level applications and services.

Abstract

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).