Unsupervised Clustering for 5G Network Planning Assisted by Real Data

Abstract

The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per km 2 has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters k and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of k by fulfilling MNO's requirements on the highest traffic density MB/km 2 and the target deployment area in km 2 . To compare the appropriate value of k and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in km 2 and highest traffic density MB/km 2 with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planning.

Hybrid computational and real data-based positioning of small cells in 5G networks

“…For that matter, by selecting the LTE cells with the greater traffic according to OpenCellid data, and inspired by the data mining process of Khan et al (2022) , a total of 19 LTE cells were selected to provide the highest data traffic areas and aid our deployment proposal. Therefore, by taking learning data from these cells, we have made a hybrid clustering/bioinspired technique that aims to solve the coverage problem in 5G frequency bands of 700 MHz, 2.3 GHz and 3.5 GHz.…”

“…For that, there are some heuristic methods that can be utilized to get approximations of how many clusters one might need to satisfy an application. For instance, Khan et al (2022) have chosen the Elbow heuristic. However, given that one might need a more complex and sophisticated way of deciding how to cluster, and how much to cluster, like in a scenario of network dimensioning and coverage area optimization, these heuristics may not provide optimal solutions.…”

“… Khan et al (2022) , however, improves on their model of 5G planning by assigning a clustering algorithm to the task of deploying 5G base-stations into high data traffic areas. A K-means algorithm has been utilized, using the Elbow heuristic as a benchmark.…”

“…This study also utilizes real-life data of LTE by the methods of Khan, García-Armada & Escudero-Garzás (2020) in order to provide a life-like user deployment in the city of Belém, Brazil—with suburban to urban characteristics and within the Amazon rainforest biome. The works of Khan, García-Armada & Escudero-Garzás (2020) and Khan et al (2022) may be evolved in user selection and data mining but still leave a gap to come up with more developed, faster computational methods to solve 5G coverage problems—the former study used a heuristic created by the authors themselves, and the latter used only a clustering algorithm. The hybrid solution present herein is a better and more developed technique overall, therefore it shall produce better coverage optimization results.…”

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

“…The model can then discover patterns and structures in the data and group them into clusters or categories. In NTN optimization, unsupervised learning can be used for various tasks, such as anomaly detection [73], network clustering [74], and traffic analysis [75]. For example, a self-organizing map can be used to cluster the satellites or drones based on their location and connectivity [76].…”