Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2011
• Main Authors: Srivastava, Vikas, Singh, Parulpreet, Malik, Praveen Kumar, Singh, Rajesh, Tanwar, Sudeep, Alqahtani, Fayez, Tolba, Amr, Marina, Verdes, Raboaca, Maria Simona
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.02.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Bandwidths; Business metrics; Cognitive radio; cognitive radio network; Communication; Delay; Energy consumption; Genetic algorithms; Intelligent networks; Machine learning; Optimization; red deer algorithm; Signal to noise ratio; Spectrum allocation; spectrum handoff; spectrum sensing; Stable channels; support vector machine; Support vector machines; red deer algorithm; support vector machine; cognitive radio network; spectrum handoff; spectrum sensing
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23042011

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed "support vector machine-based red deer algorithm" (SVM-RDA) is resilient and has low complexity. The suggested algorithm's experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.