ISSN No:2250-3676 ----- Crossref DOI Prefix: 10.64771 ----- Impact Factor: 9.625
(Peer Reviewed, Referred & Indexed Journal)
Design And Implementation Of An Intelligent Spectrum Sensing Framework Using Machine Learning For Cognitive Radio NetworksID: 3624 Abstract :The Exponential Growth Of Wireless Communication Services, Internet Of Things (IoT) Devices, Fifth-generation (5G) And Emerging Sixth-generation (6G) Networks Has Resulted In Unprecedented Demand For Limited Radio Spectrum Resources. Conventional Static Spectrum Allocation Policies Often Lead To Inefficient Spectrum Utilization, Causing Spectrum Scarcity Despite Significant Temporal And Spatial Spectrum Vacancies. Cognitive Radio Networks (CRNs) Have Emerged As An Intelligent Wireless Communication Paradigm Capable Of Dynamically Sensing, Identifying, And Utilizing Underutilized Spectrum Bands Without Causing Harmful Interference To Licensed Primary Users. Spectrum Sensing Serves As The Fundamental Component Of Cognitive Radio Systems Because It Enables Accurate Detection Of Spectrum Occupancy For Efficient Dynamic Spectrum Access. However, Traditional Spectrum Sensing Techniques Such As Energy Detection, Matched Filtering, And Cyclostationary Feature Detection Often Experience Degraded Performance Under Low Signal-to-noise Ratio (SNR), Fading Channels, Shadowing Effects, And Dynamic Wireless Environments. |
Published:24-10-2024 Issue:Vol. 24 No. 10 (2024) Page Nos:500 - 515 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to Cite |