TY - JOUR
T1 - Understanding the ecosystem and addressing the fundamental concerns of commercial MVNO
AU - Li, Yang
AU - Zheng, Jianwei
AU - Li, Zhenhua
AU - Liu, Yunhao
AU - Qian, Feng
AU - Bai, Sen
AU - Liu, Yao
AU - Xin, Xianlong
N1 - Publisher Copyright:
© 1993-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.
AB - Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.
KW - MVNO ecosystem
KW - Mobile virtual network operator (MVNO)
KW - base carrier
KW - mobile network operator (MNO)
UR - https://www.scopus.com/pages/publications/85086888933
UR - https://www.scopus.com/inward/citedby.url?scp=85086888933&partnerID=8YFLogxK
U2 - 10.1109/TNET.2020.2981514
DO - 10.1109/TNET.2020.2981514
M3 - Article
AN - SCOPUS:85086888933
SN - 1063-6692
VL - 28
SP - 1364
EP - 1377
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 3
M1 - 9063650
ER -