Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey

Mahmoud Abbasi, Amin Shahraki, Md Jalil Piran, Amir Taherkordi

Research output: Contribution to journalShort surveypeer-review

5 Scopus citations


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.

Original languageEnglish
Article number104234
JournalEngineering Applications of Artificial Intelligence
StatePublished - Jun 2021


  • Deep Reinforcement Learning
  • Medium Access Control
  • Quality of Service
  • Rate control
  • Resource sharing and scheduling
  • Survey


Dive into the research topics of 'Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey'. Together they form a unique fingerprint.

Cite this