Machine Learning Based Spectrum Sensing for Interference Reduction in 5G Cognitive Radio Networks

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. This research focuses on the design and evaluation of machine learning-based spectrum sensing algorithms for Cognitive Radio 5G networks. A hybrid sequential clustering algorithm, which combines Particle Swarm Optimization (PSO) with the K-means algorithm, is proposed. In this approach, PSO, a population-based optimization technique, determines the initial centroids and provides an optimal starting point for clustering. K-means then partitions the sensed spectrum into two clusters: occupied and unoccupied. Extensive simulations in Python were conducted to evaluate the performance of the PSO-K algorithm in various 5G network scenarios. Analysis of detection accuracy demonstrated a 9.3% improvement compared to traditional energy detection techniques.