Trajectory Aware Macro-cell Planning for Mobile Users

TL;DR

This paper introduces algorithms for macro-cell planning that prioritize mobile user trajectories, considering city geography and network heterogeneity, to optimize base-station upgrades within budget constraints.

Contribution

It presents a novel trajectory-aware macro-cell planning approach, formulates the problem as NP-hard, and develops heuristics evaluated on real and synthetic data.

Findings

Algorithms improve user trajectory QoS 3-8 times over greedy methods.

Trajectory-based planning outperforms location-based upgrades.

Approach effectively handles diverse city geographies.

Abstract

We design and evaluate algorithms for efficient user-mobility driven macro-cell planning in cellular networks. As cellular networks embrace heterogeneous technologies (including long range 3G/4G and short range WiFi, Femto-cells, etc.), most traffic generated by static users gets absorbed by the short-range technologies, thereby increasingly leaving mobile user traffic to macro-cells. To this end, we consider a novel approach that factors in the trajectories of mobile users as well as the impact of city geographies and their associated road networks for macro-cell planning. Given a budget k of base-stations that can be upgraded, our approach selects a deployment that impacts the most number of user trajectories. The generic formulation incorporates the notion of quality of service of a user trajectory as a parameter to allow different application-specific requirements, and operator choices.We show that the proposed trajectory utility maximization problem is NP-hard, and design multiple heuristics. We evaluate our algorithms with real and synthetic data sets emulating different city geographies to demonstrate their efficacy. For instance, with an upgrade budget k of 20%, our algorithms perform 3-8 times better in improving the user quality of service on trajectories in different city geographies when compared to greedy location-based base-station upgrades.