Cellular network planning for heterogeneous network using Geo-Clustering Algorithm

A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations to maximise coverage. In this paper, using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region, we propose an automated network deployment framework. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.

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Cellular network planning for heterogeneous network using Geo-Clustering Algorithm

Cited by 2 publications

References 3 publications

Unsupervised Learning Algorithm for Intelligent Coverage Planning and Performance Optimization of Multitier Heterogeneous Network

Unsupervised Learning Algorithm for Intelligent Coverage Planning and Performance Optimization of Multitier Heterogeneous Network

Relay Placement Algorithms for IoT Connectivity and Coverage in an Outdoor Heterogeneous Propagation Environment

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