Spatial Indexing for System-Level Evaluation of 5G Heterogeneous Cellular Networks

TL;DR

This paper explores the use of spatial indexing to significantly speed up large-scale 5G network simulations, enabling more efficient evaluation of network algorithms and planning.

Contribution

It introduces a multi-level inheritance architecture for spatial indexing of HetNets and demonstrates a 1000x acceleration in location-based searches.

Findings

Spatial indexing accelerates searches by 3 orders of magnitude.

The proposed architecture is implemented as an open source platform.

It facilitates efficient system-level evaluation of 5G networks.

Abstract

System level simulations of large 5G networks are essential to evaluate and design algorithms related to network issues such as scheduling, mobility management, interference management, and cell planning. In this paper, we look back to the idea of spatial indexing and its advantages, applications, and future potentials in accelerating large 5G network simulations. We introduce a multi-level inheritance based architecture which is used to index all elements of a heterogeneous network (HetNet) on a single geometry tree. Then, we define spatial queries to accelerate searches in distance, azimuth, and elevation. We demonstrate that spatial indexing can accelerate location-based searches by 3 orders of magnitude. Further, the proposed design is implemented as an open source platform freely available to all.