  LTEs uplink (UL) efficiency critically depends on how the interference across
different cells is controlled. The unique characteristics of LTEs modulation
and UL resource assignment poses considerable challenges in achieving this goal
because most LTE deployments have 1:1 frequency re-use, and the uplink
interference can vary considerably across successive time slots. In this work,
we propose LeAP, a measurement data driven machine learning paradigm for power
control to manage up-link interference in LTE. The data driven approach has the
inherent advantage that the solution adapts based on network traffic,
propagation and network topology, that is increasingly heterogeneous with
multiple cell-overlays. LeAP system design consists of the following
components: (i) design of user equipment (UE) measurement statistics that are
succinct, yet expressive enough to capture the network dynamics, and (ii)
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 centralized SON (self organized
networking) server resource (cloud). We perform extensive evaluations using
radio network plans from real LTE network operational in a major metro area in
United States. Our results show that, compared to existing approaches, LeAP
provides a 4.9x gain in the 20th percentile of user data rate, and 3.25x gain
in median data rate.
