The success of LTE Heterogeneous Networks (Het-Nets) with macro cells and pico cells critically depends on efficient spectrum sharing between high-power macros and lowpower picos. Two important challenges in this context are, (i) determining the amount of radio resources that macro cells should offer to pico cells, and (ii) determining the association rules that decide which UEs should associate with picos. In this paper, we develop a novel algorithm to solve these two coupled problems in a joint manner. Our algorithm has provable guarantee, and furthermore, it accounts for network topology, traffic load, and macro-pico interference map. Our solution is standard compliant and can be implemented using the notion of Almost Blank Subframes (ABS) and Cell Selection Bias (CSB) proposed by LTE standards. We also show extensive evaluations using RF plan from a real network and discuss SON based eICIC implementation.

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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 re-use, and the uplink interference can vary considerably across successive time slots. In this work, we propose LeAP, a measurement datadriven 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, 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 4.9× gain in the 20 th % − tile of user data rate, 3.25× gain in median data rate.of User-U in the same frequency block as User-A, and, 1 1 1 U , U ∈ {B, C, D, E} is an 0 − 1 indicator variable denoting whether User-U also transmits over the same time-frequency block as User-A. Note that, since User-B and User-C share the same cell, only one of them can be active in a time-frequency resource and thus 1 1 1 B +1 1 1 C ≤ 1; similarly 1 1 1 D +1 1 1 E ≤ 1. Since MAC of each neighboring cell makes independent scheduling decision on who gets scheduled in a time-frequency block, the interference becomes highly unpredictable from transmission to transmission; managing this unstable interference pattern poses unique research challenges barely addressed in the literature. Indeed, this is unlike 3G systems 2 , where the neighboring cell interference for a similar topology with CDMA technology (over an appropriate CDMA channel) would simply be Int A (CDM A) = P B + P C + P D + P E thus leading to a more stable interference pattern across transmissions. In general, unlike LTE, the overall CDMA uplink interference also has an additional term for self cell interference due to uplink users in the same cell. Of course, for desirable user performance, the uplink interference still has to be managed through power control algorithms that has been the focus of much of the existing research on 3G power control and uplink interference management.Solution requirements: LTE networks are deployed with self organized networking (SON) capabilities [1], [2] to maximize network performances. Today's LTE networks are also heterogeneous (HTNs) that include high power macro cells overlay...

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