Semantic Mobile Base Station Placement
Kritik Soman, K.S. Venkatesh

TL;DR
This paper introduces a semantic-aware base station placement method using aerial imagery, 3D modeling, and multi-objective optimization to improve coverage in complex terrains.
Contribution
It presents a novel placement pipeline combining semantic segmentation, 3D modeling, and genetic algorithms for better base station deployment in challenging environments.
Findings
Irregular BS placement enhances coverage in high elevation and dense building areas.
The proposed method outperforms traditional placement strategies in SINR and throughput.
Semantic and 3D modeling improve the accuracy of optimal base station locations.
Abstract
Location of Base Stations (BS) in mobile networks plays an important role in coverage and received signal strength. As Internet ofThings (IoT), autonomous vehicles and smart cities evolve, wireless net-work coverage will have an important role in ensuring seamless connectivity. Due to use of higher carrier frequencies, blockages cause communication to primarily be Line of Sight (LoS), increasing the importance of base station placement. In this paper, we propose a novel placement pipeline in which we perform semantic segmentation of aerial drone imagery using DeepLabv3+ and create its 2.5D model with the help ofDigital Surface Model (DSM). This is used along with Vienna simulator for finding the best location for deploying base stations by formulating the problem as a multi-objective function and solving it using Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The case with and…
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