ML Framework for Wireless MAC Protocol Design

Autor:	Keshtiarast, Navid, Petrova, Marina
Rok vydání:	2024
Předmět:	Computer Science - Networking and Internet Architecture Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
DOI:	10.1109/ICMLCN59089.2024.10624788
Popis:	Adaptivity, reconfigurability and intelligence are key features of the next-generation wireless networks to meet the increasingly diverse quality of service (QoS) requirements of the future applications. Conventional protocol designs, however, struggle to provide flexibility and agility to changing radio environments, traffic types and different user service requirements. In this paper, we explore the potential of deep reinforcement learning (DRL), in particular Proximal Policy Optimization (PPO), to design and configure intelligent and application-specific medium access control (MAC) protocols. We propose a framework that enables the addition, removal, or modification of protocol features to meet individual application needs. The DRL channel access policy design empowers the protocol to adapt and optimize in accordance with the network and radio environment. Through extensive simulations, we demonstrate the superior performance of the learned protocols over legacy IEEE 802.11ac in terms of throughput and latency. Comment: Accepted for Publication in IEEE ICMLCN 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.07208 Zobrazit plný text záznamu View this record from Arxiv