Paper Info
Title
Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework.
Abstract
Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.
Year
DOI
Venue
2020
10.3390/s20071855
SENSORS
Keywords
DocType
Volume
multi-Cell,LTE-U,reinforcement learning,cellular broadband IoT
Journal
20
Issue
ISSN
Citations 
7
1424-8220
1
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
José M de C Neto110.34
Sildolfo F G Neto210.34
Pedro M de Santana310.34
Vicente A. de Sousa Jr.4929.52