Unboxing MAC Protocol Design Optimization Using Deep Learning
This work addresses the problem of manual and sub-optimal parameter tuning in wireless networks for network engineers, but it appears incremental as it applies an existing deep learning method to a known bottleneck without demonstrating concrete performance gains.
The paper tackles the challenge of optimizing MAC protocol design for varying wireless network scenarios by leveraging a deep reinforcement learning framework to learn relationships between physical and MAC layer parameters, showing that this approach provides insights for protocol design optimization.
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.