Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach
Name
1808.05894.pdf
Description
Submitted version
Size
509.88 KB
Format
Adobe PDF
Checksum (MD5)
32a5b281be70b956e4025def0d7aa75b
Author(s)
Win, Moe Z.
Date Issued
December 2018
Journal
2018 IEEE Global Communications Conference (GLOBECOM)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Hayajneh, A. M. “Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach.” Paper presented at the 2018 IEEE Global Communications Conference (GLOBECOM) (Abu Dhabi, United Arab, 9-13 Dec. 2018): INSPEC Accession Number: 18476076.
Version
Original manuscript
Abstract
In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to-interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta-distribution. Our analysis is based on the alpha-beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/GLOCOM.2018.8648045
