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Reinforcement Learning for Standards Design

2021-10-13 17:46:27
Shahrukh Khan Kasi, Sayandev Mukherjee, Lin Cheng, Bernardo A. Huberman

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).

Abstract (translated)

URL

https://arxiv.org/abs/2110.06909

PDF

https://arxiv.org/pdf/2110.06909


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