Decentralized and Dynamic Band Selection in Uplink Enhanced Licensed-Assisted Access: Deep Reinforcement Learning Approach

Fitsum Debebe Tilahun, Chung G. Kang

Research output: Contribution to journalArticle

Abstract

Enhanced licensed-assisted access (eLAA) is an operational mode that allows the use of unlicensed band to support long-term evolution (LTE) service via carrier aggregation technology. The extension of additional bandwidth is beneficial to meet the demands of the growing mobile traffic. In the uplink eLAA, which is prone to unexpected interference from WiFi access points, resource scheduling by the base station, and then performing a listen before talk (LBT) mechanism by the users can seriously affect the resource utilization. In this paper, we present a decentralized deep reinforcement learning (DRL)-based approach in which each user independently learns dynamic band selection strategy that maximizes its own rate. Through extensive simulations, we show that the proposed DRL-based band selection scheme improves resource utilization while supporting certain minimum quality of service (QoS).

Original languageEnglish
Article number5937358
JournalWireless Communications and Mobile Computing
Volume2020
DOIs
Publication statusPublished - 2020 Jan 1

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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