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).
翻译:通信标准是通过人类委员会经过数月甚至数年的反复会议,直到达成共识为止,通过人类委员会设计通信标准,其中包括有关在空气界面上支持的调制和编码计划的决定,我们建议一种“自动化”方式,选择一套调制和编码计划,在特定空气界面上支持,从而简化标准设计过程,并方便扩大标准以支持适用于新的更高级别应用和服务的新调制计划。我们的计划涉及机器学习,即一个建筑商实体向一个评价实体提交提案,该实体的评分为提案的得分。建造商采用强化学习,在其提交的提案上进行循环,直至实现建筑商和评价商先前商定的得分,以表明符合所需的设计标准(包括通过界面传输的性能衡量标准)。