Evolving amendments of 802.11 standards feature a large set of physical and
MAC layer control parameters to support the increasing communication objectives
spanning application requirements and network dynamics. The significant growth
and penetration of various devices come along with a tremendous increase in the
number of applications supporting various domains and services which will
impose a never-before-seen burden on wireless networks. The challenge however,
is that each scenario requires a different wireless protocol functionality and
parameter setting to optimally determine how to tune these functionalities and
parameters to adapt to varying network scenarios. The traditional trial-error
approach of manual tuning of parameters is not just becoming difficult to
repeat but also sub-optimal for different networking scenarios. In this paper,
we describe how we can leverage a deep reinforcement learning framework to be
trained to learn the relation between different parameters in the physical and
MAC layer and show that how our learning-based approach could help us in
getting insights about protocol design optimization task.

本文描述了如何利用深度强化学习框架进行物理层和 MAC 层的不同参数之间的关系学习，以优化协议设计任务。