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A Novel Machine Learning Approach to Estimating KPI and PoC for LTE-LAA-Based Spectrum Sharing
- Source :
- ICC Workshops
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Machine learning (ML) approaches have been extensively exploited to model and to improve wireless communication networks in the past few years. Nonetheless, the estimation of key performance indicators (KPIs) and their uncertainties in Long Term Evolution License Assisted Access (LTE-LAA) based coexistence systems is not adequately addressed. For example, it is not clear if an ML method can accurately predict achievable KPIs (e.g. throughput) and the probability of coexistence (PoC) of LTE-LAA coexistence systems based on partial or no information of MAC and physical layer protocols and parameters. In this paper, we develop a novel ML method by combining a neural network with a logistic regression algorithm to track and estimate KPIs and PoC of coexisting LTE-LAA and wireless local area network (WLAN) links. This ML method can be applied when KPI samples at the base stations (BSs) and access points (APs) are available, without using knowledge of MAC and physical layer parameters. Comparison between the ML and simulation results indicate that the proposed ML method can track the system KPIs and predict the system PoC with good accuracy.
- Subjects :
- Artificial neural network
Computer science
business.industry
ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
05 social sciences
Physical layer
050801 communication & media studies
Throughput
Machine learning
computer.software_genre
Term (time)
law.invention
Base station
0508 media and communications
law
0502 economics and business
Wireless
050211 marketing
Wi-Fi
Artificial intelligence
Performance indicator
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE International Conference on Communications Workshops (ICC Workshops)
- Accession number :
- edsair.doi...........0f5d708fed817f4d7ec584aa21f6d7da