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All Journal The Indonesian Journal of Computer Science
Ntuli, Elesa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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RTSO: Comprehensive Framework for Real-Time Frequency Channel Occupancy and Spectrum Hole Detection Ntuli, Elesa; Du Chunling
The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i4.4878

Abstract

Efficient spectrum utilization remains a key challenge in modern wireless communications, especially in dynamic environments with limited spectrum availability. This paper introduces Real-Time Spectrum Optimization (RTSO), a framework that combines Geo-Location Spectrum Databases (GLSDBs) with real-time spectrum sensing to detect frequency channel occupancy and identify spectrum holes. RTSO uses advanced energy detection techniques, including Additive White Gaussian Noise (AWGN) modelling, to distinguish between idle and occupied channels accurately. It incorporates mathematical tools such as occupancy time and Frequency Channel Occupation (FCO) metrics for effective spectrum analysis. A notable feature is a revisit-time-based sensing mechanism that infers channel status during intermittent scans. Practical evaluations demonstrated improved detection accuracy, reduced false alarms, and better decision-making for dynamic access to available channels. Key performance metrics, including latency, bandwidth, and error rate, were compared with baseline methods, showing substantial gains in efficiency. This work provides a valuable contribution to cognitive radio systems and dynamic spectrum access, paving the way for more intelligent and adaptive spectrum management strategies in real-time communication networks.
A Dynamic Framework for Optimizing Spectrum Utilization and Interference Mitigation in White Space Networks Ntuli, Elesa; Du Chunling; Moshe Timothy Masonta
The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i4.4880

Abstract

This study presents a framework for optimizing spectrum utilization and reducing interference in White Space (WS) networks using the Interference Mitigation Decision Framework (IMDF). The IMDF combines Geo-Location Spectrum Databases (GLSDs), reactive spectrum sensing, and Software Defined Radios (SDRs) to address the limitations of traditional spectrum allocation methods. The IMDF enhances allocation, reduces interference, and improves network performance by monitoring real-time spectrum usage. Simulations comparing IMDF with traditional GLSD-based methods show a 70% bandwidth saving, compared to 40% in traditional approaches. Additionally, IMDF reduces interference events by 30%, improving Quality of Service (QoS) and mitigating Cross Network Interference (CNI). With dynamic spectrum management, IMDF achieves 70% spectrum utilization, while traditional systems only reach 40%. These results demonstrate IMDF's effectiveness in dynamic environments, offering a robust solution for wireless service demand and interference mitigation in increasingly WS networks. The IMDF’s adaptability, combined with its efficient resource management, makes it a promising framework for the future of spectrum allocation in increasingly congested network environments.
A Deep Reinforcement Learning for Adaptive Spectrum Access in Geo-Enabled Cognitive Radio Networks Ntuli, Elesa; Du Chunling
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.4958

Abstract

The radio spectrum is getting more crowded as more people and devices connect wirelessly. Cognitive Radio Networks (CRNs), which can find and use empty spectrum, help solve this problem. One common way they do this is by using a Geo-Location Spectrum Database (GLSD) to check which parts of the spectrum are free. But these databases are not always updated in real time, so they sometimes miss chances to use the spectrum or cause interference. This study looks at a better way to handle this by using Deep Reinforcement Learning (DRL). The system we designed learns from the environment and makes smart decisions about when and where to use the spectrum. We used a Deep Q-Network (DQN) to test it in a simulated environment. Our results show that this new method improved spectrum use by 27%, reduced interference by 35%, and made decisions 22% faster than older methods that rely only on the database. The model reached about 91% accuracy in its decisions over many tests. This means that adding DRL to geo-location systems can make spectrum use more efficient, especially in busy areas or places with limited internet access. We suggest trying this setup in the real world using Software Defined Radios (SDRs) to see how it performs outside of simulations.
Co-Authors Du Chunling Moshe Timothy Masonta