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 $4.9times$ gain in the 20th percentile of
user data rate, $3.25times$ gain in median data rate
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