LTE radio analytics made easy and accessible
Name
Katabi_LTE radio.pdf
Size
2.14 MB
Format
Adobe PDF
Checksum (MD5)
47bee769fd821ad4b64c51d4d5079e23
Author(s)
Katabi, Dina
Erran Li, Li
Suresh Kumar, Swarun
Hamed, Ezzeldin Omar Hussein
Date Issued
August 2014
Journal
Proceedings of the 2014 ACM conference on SIGCOMM (SIGCOMM '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Swarun Kumar, Ezzeldin Hamed, Dina Katabi, and Li Erran Li. 2014. LTE radio analytics made easy and accessible. In Proceedings of the 2014 ACM conference on SIGCOMM (SIGCOMM '14). ACM, New York, NY, USA, 211-222.
Version
Author's final manuscript
Abstract
Despite the rapid growth of next-generation cellular networks, researchers and end-users today have limited visibility into the performance and problems of these networks. As LTE deployments move towards femto and pico cells, even operators struggle to fully understand the propagation and interference patterns affecting their service, particularly indoors. This paper introduces LTEye, the first open platform to monitor and analyze LTE radio performance at a fine temporal and spatial granularity. LTEye accesses the LTE PHY layer without requiring private user information or provider support. It provides deep insights into the PHY-layer protocols deployed in these networks. LTEye's analytics enable researchers and policy makers to uncover serious deficiencies in these networks due to inefficient spectrum utilization and inter-cell interference. In addition, LTEye extends synthetic aperture radar (SAR), widely used for radar and backscatter signals, to operate over cellular signals. This enables businesses and end-users to localize mobile users and capture the distribution of LTE performance across spatial locations in their facility. As a result, they can diagnose problems and better plan deployment of repeaters or femto cells. We implement LTEye on USRP software radios, and present empirical insights and analytics from multiple AT&T and Verizon base stations in our locality.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2619239.2626320
