Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey
Mahmoud Abbasi, Amin Shahraki, Md. Jalil Piran, Amir Taherkordi
Abstract
Quality of Service (QoS) provisioning is based on various network management techniques including resource management and medium access control (MAC). Various techniques have been introduced to automate networking decisions, particularly at the MAC layer. Deep reinforcement learning (DRL), as a solution to sequential decision making problems, is a combination of the power of deep learning (DL), to represent and comprehend the world, with reinforcement learning (RL), to understand the environment and act rationally. In this paper, we present a survey on the applications of DRL in QoS provisioning at the MAC layer. First, we present the basic concepts of QoS and DRL. Second, we classify the main challenges in the context of QoS provisioning at the MAC layer, including medium access and data rate control, and resource sharing and scheduling. Third, we review various DRL algorithms employed to support QoS at the MAC layer, by analyzing, comparing, and identifying their pros and cons. Furthermore, we outline a number of important open research problems and suggest some avenues for future research.