Computer Vision-based Prediction and Mathematical Optimization of 5G Wireless Cellular Network Parameters

Abstract

<i>Objective</i> - To investigate, analyze and optimize (where needed) the properties and predictive analyses of selected 5G Mobile wireless network parameters (i.e. Signal to interference noise ratio (<I>SINR</I>) and Throughput as measures of network performance) and Interference conditions in the presence of building obstacles; using the novel approach of combining signal data and visual data in wireless communications. <i>Methods</i>- Using a sample set (i.e., 200 data points) of real life 5G Outdoor Micro cellular tests data and urban building image datasets from validated open source data stores; experimental, investigative and comparative analyses were carried out using the novel approach of combining signal data and visual data using Machine Learning (i.e. Computer Vision) Hybrid deep learning artificial intelligence CNN-based model (i.e., High performance CNN), analytical and mathematical optimization algorithms. The key idea is to leverage camera imagery and Machine Learning (Computer Vision) to successfully predict and analyze network parameters like <I>SINR</I>, Throughput and amount of Interference in the presence of signal obstacles which usually attenuate received signals aperiodically. Additionally obstacle related losses were analysed and network parameter optimization was also demonstrated. <i>Results</i> - The predictive analyses in the presence of obstacles (i.e. concrete buildings) of selected 5G wireless network parameters of <I>SINR</I> and Throughput were carried out successfully using the Hybrid High performance CNN model (HP CNN); with the model showing excellent efficiency by using lesser resources and image datasets from a different environment. Furthermore, the analytical and predictive analyses of a representation of the user interference (i.e. <I>I/PG</I>) in the presence of obstacles were also successfully carried out, and a new OPL algorithm was also proposed in relation to important user obstacle penetration losses. Additionally, the 5G network parameter (i.e. <I>SINR</I>) was mathematically optimized with reference to minimal interference as a demonstration of being an effective tool for engineers and network designers to analytically tune and manage network performance in subsystems and systems more efficiently. <i>Conclusions</i> - This work and diverse related works being carried out; gives no doubt that this novel hybrid intelligent approach presents great possibilities and capabilities for the modern wireless communications field and associated technologies for now and in the future; and its a key approach to autonomous, more efficient network performance management and AI-driven network parameter, attenuation, and interference management.