Определение характера мобильности пользователей в гетерогенных беспроводных сетях с использованием многомерной обработки данных
Authors: Ашаев И.П. , Гайсин А.К. , Коробков А.А. , Сафиуллин И.А. , Надеев А.Ф. 
Published: February 2025

В данной работе представлено исследование возможностей экспериментальной платформы по моделированию сетей радиодоступа. Рассмотрены возможности применения методов машинного обучения и искусственного интеллекта для оптимизации параметров беспроводных сетей. Также показано, что данные, поступающие с сети радиодоступа, являются по своей природе многомерными и требуют применения специальных методов тензорного анализа. В работе проведено описание методов обработки и факторизации многомерных данных. Основным результатом является разработанный авторами метод классификации пользователей по характеру мобильности на основе кластеризации результатов факторизации многомерных данных на основе тензорной алгебры. Проведённое моделирование показывает высокую точность определения паттерна мобильности разнородных пользователей в гетерогенных беспроводных сетях.
Introduction. The contemporary mobile communications network displays a heterogeneous structure, both in organization and in the range of supported services. Effectively managing such a network demands the integration of intelligent control nodes and interfaces, typically not outlined in the specifications. In open architecture radio access networks, these functions are undertaken by an intelligent radio network controller. The creation of such a controller entails addressing various tasks, including the development of efficient and reliable data processing methods, the construction of suitable models, and the implementation of adaptive control. The research aimed to explore the capabilities of experimental platforms in developing algorithms for intelligent control of radio access network parameters. The study proposes a method for classifying users based on the nature of their mobility through clustering the results of tensor factorization with multi-dimensional data. Technical setup. The experimental platform utilized in the research integrates an intelligent radio network controller with Network Simulator 3 software. Outcomes.In the initial stage of the research, preprocessing of modeling data was conducted. The order of the multi-dimensional data model was determined through HOSVD (Higher Order Singular Value Decomposition) decomposition, and a singular values profile was constructed. The tensor's rank was established as 4. Subsequently, a CPD (Canonical Polyadic Decomposition) tensor decomposition was executed to derive the factor matrices. Factor scores were normalized to the rank. The clustering algorithm chosen was DBSCAN (Density-Based Spatial Clustering of Applications with Noise), enabling an estimation of the number of clusters based on hyperparameters and data nature. Results indicated that all mobile users were correctly clustered according to the simulation scenario. The algorithm interpreted static users as noise, as they lacked common patterns of SINR (Signal-to-Interference-plus-Noise Ratio) changes. Conclusion. The research has yielded a method for classifying users based on their mobility patterns, employing clustering with the results of tensor decomposition factorization. It has been observed that tensor decomposition results provide new informative features from data sourced from the radio access network. Simulations demonstrated high accuracy in determining the mobility patterns of diverse users, approaching 100%. However, as the similarity of movement trajectories increases and the speed difference between users diminishes, the accuracy of group identification decreases to roughly 80 %.