NDLI: Learning-based uplink interference management in 4G LTE cellular systems

Content Provider	ACM Digital Library
Author	Monogioudis, Pantelis Deb, Supratim
Copyright Year	2015
Abstract	LTE's uplink (UL) efficiency critically depends on how the interference across different cells is controlled. The unique characteristics of LTE's modulation and UL resource assignment poses considerable challenges in achieving this goal because most LTE deployments have 1:1 frequency reuse, and the uplink interference can vary considerably across successive time-slots. In this paper, we propose LeAP, a measurement data-driven machine learning paradigm for power control to manage uplink interference in LTE. The data-driven approach has the inherent advantage that the solution adapts based on network traffic, propagation, and network topology, which is increasingly heterogeneous with multiple cell-overlays. LeAP system design consists of the following components: 1) design of user equipment (UE) measurement statistics that are succinct, yet expressive enough to capture the network dynamics, and 2) design of two learning-based algorithms that use the reported measurements to set the power control parameters and optimize the network performance. LeAP is standards-compliant and can be implemented in a centralized self-organized networking (SON) server resource (cloud). We perform extensive evaluations using radio network plans from a real LTE network operational in a major metro area in the US. Our results show that, compared to existing approaches, LeAP provides 4.9 × gain in the 20th percentile of user data rate, 3.25 × gain in median data rate.
Starting Page	398
Ending Page	411
Page Count	14
File Format	PDF
ISSN	10636692
DOI	10.1109/TNET.2014.2300448
Volume Number	23
Issue Number	2
Journal	IEEE/ACM Transactions on Networking (TON)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	4G LTE HetNet Interference management Power control Self-optimizing networks (SONs)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Networks and Communications Software Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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