Research Article
Computer Vision-based Prediction and Mathematical Optimization of 5G Wireless Cellular Network Parameters
Chikezie Kennedy Kalu*
Issue:
Volume 12, Issue 2, December 2025
Pages:
55-71
Received:
6 June 2025
Accepted:
23 June 2025
Published:
19 July 2025
Abstract: Objective - 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 (SINR) 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. Methods- 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 SINR, 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. Results - The predictive analyses in the presence of obstacles (i.e. concrete buildings) of selected 5G wireless network parameters of SINR 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/PG) 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. SINR) 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. Conclusions - 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.
Abstract: Objective - 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 (SINR) and Throughput as measures of network performance) and Interference conditions in the presence of building obstacles; using the novel approach of com...
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Research Article
Robust Radar-driven Gesture Recognition for Contactless Human-computer Interaction Using Support Vector Machine and Signal Feature Optimization
Issue:
Volume 12, Issue 2, December 2025
Pages:
72-80
Received:
15 July 2025
Accepted:
24 July 2025
Published:
8 August 2025
DOI:
10.11648/j.wcmc.20251202.12
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Views:
Abstract: Radar-based gesture recognition has emerged as a reliable alternative to vision-based systems for human-computer interaction, especially in environments with low illumination, occlusion, or privacy constraints. This study explores the implementation of a radar-based gesture recognition system using advanced signal processing and machine learning techniques to classify dynamic hand movements with high precision. The central challenge addressed involves extracting discriminative features from radar signals and developing robust classifiers capable of performing effectively under real-world conditions. The proposed approach includes preprocessing radar data through bandpass filtering (5-50 Hz) and normalization, followed by the extraction of key features such as signal energy, mean Doppler shift (7.6-7.9 Hz), and spectral centroid. A Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel is employed and optimized for gesture classification. Comparative analysis reveals that the SVM model outperforms the K-nearest neighbors (KNN) method, achieving a classification accuracy of 86% and an F1-score of 0.89, compared to 82% accuracy and a 0.84 F1-score obtained with KNN at. These results demonstrate the effectiveness of radar-based systems in detecting and classifying hand gestures accurately, achieving up to 97.3% accuracy in controlled environments. Unlike traditional camera-based systems, radar maintains functionality in poor lighting and occluded conditions while preserving user privacy by avoiding optical recordings. The system also offers low power consumption and real-time processing capabilities, making it suitable for deployment in privacy-sensitive and resource-constrained applications. This work confirms radar’s potential in fine-grained gesture interpretation and aligns with prior studies in crowd tracking and digit recognition, where similar performance metrics were observed. The integration of radar sensing with machine learning offers a promising path toward more secure, responsive, and environment-agnostic interaction systems.
Abstract: Radar-based gesture recognition has emerged as a reliable alternative to vision-based systems for human-computer interaction, especially in environments with low illumination, occlusion, or privacy constraints. This study explores the implementation of a radar-based gesture recognition system using advanced signal processing and machine learning te...
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