Feb 24
The IUP Journal of Telecommunications
Focus
This issue contains three papers basically addressing channel performance evaluation in heterogeneous networks, queuing techniques in traffic engineering and image processing for data handling capabilities.
In wireless system, a seamless and reliable connectivity needs an acceptable SNR at the most faded location of the network. In cellular system implementation, mitigation of interference becomes a crucial problem and thus needs a thorough SINR evaluation to ensure an acceptable BER. In this context, the first paper is "SINR Coverage Stability in 6G Networks: Assessing the Impact of Sample Points on Seamless Heterogeneous Integration", by Chigozirim Ajaegbu, Oluwaseyi Adediran and Joshua Opeyemi Adelowo. The paper reports an insight of design and optimization of the network resources under computational tradeoffs.
The second paper, "Application of Kullback-Leibler Divergence Formalism to Stable M/G/1 Queue and Biometrics", by Ismail A Mageed, explores the role of KLDF in a stable queue model to process data and model parameters for a suitable statistical modeling. The model has been applied to biometric identification and plays a crucial role in network authentication for security and reliability purposes. The paper finds a closed form expression of KLDF of a stable M/G/1 queue and presents service time PDF and CDF.
The present network is supporting various scientific industrial and medical research for decision-making efforts. The terrain and geographical data identification decision-making strategies need an integrated approach of image data collection, processing and dissemination in real time. The last paper, "Techniques for Detecting Changes and Identifying High-Threat Zones in Forest Ecosystems: A Comprehensive Review", by A I Hewarathna and Vigneshwaran Palanisamy, examines the role of deep learning segmentation technique in identifying the changes in the satellite images of a given terrain. The investigation focuses on optimization of satellite image data collection and forecasting for predictive models suitable for deforestation risk assessment and related evaluation matrices.
Consulting Editor
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| Article | Price (₹) | ||
| SINR Coverage Stability in 6G Networks: Assessing the Impact of Sample Points on Seamless Heterogeneous Integration |
100
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| Application of Kullback-Leibler Divergence Formalism to Stable M/G/1 Queue and Biometrics |
100
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| Techniques for Detecting Changes and Identifying High-Threat Zones in Forest Ecosystems: A Comprehensive Review |
100
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SINR Coverage Stability in 6G Networks: Assessing the Impact of Sample Points on Seamless Heterogeneous Integration
The paper investigates the impact of sample points on SINR-based m-coverage probability in the context of 6G networks, considering the evolving landscape of mobile network architecture. Signal-Interference-Noise Ratio (SINR) is a critical metric for assessing cellular network performance. Conventionally, it is believed that increasing sample points significantly improves SINR coverage probability, especially within the range of 2^6 to 2^10. To explore this assertion, advanced simulations are conducted in both single-tier and multi-tier networking scenarios, employing the Poisson point process to spatially distribute nodes. The evaluation focuses on SINR values less than 1, using five distinct sample points. Surprisingly, the results reveal a remarkable finding: beyond an increment value of 2^2, increasing sample points has minimal impact on SINR-based m-coverage probability for both single-tier and multi-tier networks. This intriguing observation challenges the traditional understanding of the relationship between sample points and SINR coverage probability. The study's insights have significant implications for designing and optimizing 6G networks, guiding network planners in making informed decisions regarding resource allocation and computational tradeoffs. As 6G technology ushers in a transformative era of interconnectedness, understanding the saturation point of SINR coverage probability becomes pivotal for unleashing its full potential, fostering global connectivity and enabling innovative applications.
Application of Kullback-Leibler Divergence Formalism to Stable M/G/1 Queue and Biometrics
The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to stable queue manifold. More potentially, both service time probability and cumulative functions, which make KLDF exact, are obtained. The credibility of KLDF is justified through consistency axioms. In other words, the current work provides a cutting-edge unification of information theory, combined with applied probability and divergence theory. Accordingly, this paper adds new knowledge, which extends a novel contemporary information theoretic link to some other different mathematical disciplines. The significance of Kullback-Leibler divergence (KLD) is highlighted through references to some potential applications of KLD to biometry.
Techniques for Detecting Changes and Identifying High-Threat Zones in Forest Ecosystems: A Comprehensive Review
Effective monitoring and detection of changes in forest cover are paramount for environmental stewardship and sustainable management practices. However, existing methodologies face challenges in obtaining consistent and high-quality satellite imagery, predicting deforestation-prone areas, optimizing deep learning architectures for change detection, and establishing robust evaluation metrics. This paper makes a comprehensive literature review to examine the progress in deep learning segmentation techniques and their application in change detection using satellite imagery. The key questions guide the investigation, focusing on optimization of satellite image collection, development of predictive models for deforestation risk assessment, enhancement of U-Net architectures for change detection, and establishment of evaluation metrics. Through the review, significant advancements are identified, particularly in integration of attention mechanisms and modified U-Net architectures. Contrastingly, traditional image differencing methods are surpassed by convolutional neural network (CNN) approaches, showcasing superior feature extraction and change detection accuracy. Noteworthy innovations include residual learning, attention gates and spatiotemporal encoders, which enhance segmentation performance. The findings underscore the potential of advanced methodologies to enhance detection and monitoring of changes in forest cover. By drawing insights from related fields such as urban planning and disaster monitoring, the study advocates for a holistic approach to environmental management. Ultimately, this review contributes to the evolution of deep learning techniques for change detection, with a specific focus on advancing forest cover monitoring practices.