Learning-Based Uplink Interference Management in 4G LTE Cellular Systems

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.