Predicting Noise and User Distances from Spectrum Sensing Signals Using Transformer and Regression Models
Source abstract
Abstract
Frequency spectrum allocation has been a subject of dispute in recent years. Cognitive radio dynamically allocates users to spectrum holes using various sensing techniques. Noise levels and distances between users can significantly impact the efficiency of cognitive radio systems. Designing robust communication systems requires accurate knowledge of these factors. This paper proposes a method for predicting noise levels and distances based on spectrum sensing signals using regression machine learning models. The proposed methods achieved correlation coefficients of over 0.98 and 0.82 for noise and distance predictions, respectively. Accurately estimating these parameters enables adaptive resource allocation, interference mitigation, and improved spectrum efficiency, ultimately enhancing the performance and reliability of cognitive radio networks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Blind Source Separation Techniques · Speech and Audio Processing · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for R&D in Electronic and Information Technology (CETELI), Department of Electronics and Computing (DTEC), Federal University of Amazonas (UFAM), Manaus 69067005, Brazil
Sidi—Institute for Research, Development, and Innovation, Manaus 69037000, Brazil
National Telecommunications Agency (ANATEL), Manaus 69057070, Brazil
Electronic and Telecommunications Engineering, Faculty of Electrical Engineering (FEELT), Federal University of Uberlândia (UFU), Uberlândia 38408288, Brazil
References (36)
Show source-provided references
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