| dc.description.abstract | The rapid increase in global population has driven a surge in users of radio technology,
leading to a shortage of available frequency spectrum for wireless systems. To optimize
the use of limited spectrum, secondary (unlicensed) users can access the spectrum of
primary (licensed) users when it is temporarily unused. These unused portions of
spectrum are called spectrum holes or white spaces. Cognitive radios play a key role by
performing spectrum sensing to detect when the spectrum is available for secondary
users. Real-time spectrum detection is essential for allowing secondary users to access the
spectrum without interfering with primary users. However, existing spectrum sensing
methods often suffer from poor detection accuracy due to channel fading and noise. The
work addressed the creation of machine learning-related algorithms that perform
effective user classifications and spectrum sensing in 5G wireless cognitive radio
networks to maximize spectrum usage and minimize interference. To classify users
according to the patterns of activities and spectral behaviour, a hybrid sequential
algorithm that merges Particle Swarm Optimization (PSO) and K-Means clustering were
created to classify users. To perform spectrum sensing, a PSO-K Means-based algorithm
has also been used to identify the spectrum holes through clustering sensed data in
occupied and unoccupied frequency bands. This method employed PSO a population
based optimization method to compute the initial centroids and give an optimal starting
point in the clustering. K-means then grouped the sensed spectrum into two occupied and
unoccupied. The hybrid PSO-K algorithm increased the primary user detection and
allowed cognitive radios the opportunity to access the spectrum without resulting in
interruptions. Extensive simulations in Python were conducted to evaluate the
performance of the PSO-K algorithm in various 5G network scenarios. Results showed
that PSO-K significantly outperformed traditional energy detection methods in terms of
both performance and detection accuracy. The algorithm notably enhanced the
probability of detection while reducing the probability of false alarms and missed
detections. Analysis of detection accuracy demonstrated a 9.3% improvement compared
to traditional energy detection techniques. | en_US |
