Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis

Abstract

Because of numerous dormant application fields, wireless sensor networks (WSNs) have emerged as an important and novel area in radio and mobile computing research. These applications range from enclosed system configurations in the home and office to alfresco enlistment in an opponent’s landmass in a strategic flashpoint. Cognitive radio networks (CRNs) can be created by integrating radio link capabilities with network layer operations utilizing cognitive radios. The goal of CRN design is to optimize the general system operations to meet customer requirements at any location worldwide by much more efficiently addressing CRNs instead of simply connecting spectrum utilization. When compared to conventional radio networks, CRNs are more versatile and susceptible to wireless connections. Recent advancements in wireless communication have resulted in increasing spectrum scarcity. As a modern innovation, cognitive radio aims to tackle this challenge by proactively utilizing the spectrum. Because cognitive radio (CR) technology gives assailants additional possibilities than a normal wireless network, privacy in a CRN becomes a difficult challenge. We concentrate on examining the surveillance system at a societal level, in which both defense and monitoring are critical components in assuring the channel’s privacy. The current state of investigation into spectrum sensing and potential risks in cognitive radios is reviewed in this study.

Advanced Quantum-enhanced Recurrent Neural Network for Efficient Spectrum Sensing in Cognitive 5g Networks

“…But compressive spectrum sensing algorithms reconstruct the original signal through compressed measurements and provides reliable detection with high accuracy. The common wireless channel issues like shadowing, multipath fading can be effectively handled while using compressive spectrum sensing compared to traditional methods [5]. Though the advantages are more, there exist some limitations as it mainly depends on the sparsity information about the signal.…”