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 gain in the 20th percentile of user data rate, gain in median data rate. 1 An RB is a block of 12 subcarriers and 7 OFDM symbols and is the smallest allocatable resource in the frequency-time domain. 1063-6692
In REF , Deb and Monogioudis proposed LeAP, a measurement data-driven machine learning paradigm for power control to manage uplink interference in LTE.
Learning-Based Uplink Interference Management in 4G LTE Cellular Systems
IEEE/ACM Transactions on Networking
IEEE/ACM Transactions on Networking
Computer Science