Cognitive Radio-Spectrum Sensing Research Papers (original) (raw)

With the rapid development of numerous wireless network technologies and the growing number of wireless devices in use around the world, gaining access to the radio frequency spectrum has become a challenge that must be solved as soon as possible. The ever-increasing wireless traffic and shortage of accessible spectrum necessitate smart spectrum management. Machine learning (ML) is gaining popularity, and its capacity to spot patterns and aid decision-making has found applications in a variety of disciplines. Machine learning approaches have been applied to wireless networking difficulties, such as spectrum efficiency, and have showed superior performance compared to traditional methods. Spectrum sensing enables dynamic spectrum sharing, which improves spectrum efficiency by allowing coexistence of wireless technologies within the same frequency range. This involves the accurate detection and identification of multiple wireless signals sent in the same radio spectrum range. The current state of machine learning algorithms for identifying and classifying radio signals depending on their access technologies, such as Wi-Fi and LTE, is examined in this work. Classifying the RF signals based on their wireless network technologies as opposed to their modulation schemes, especially using machine learning, is an emerging area of study and is becoming a popular research topic. This survey will assist readers to become familiar with the current literature and enable further research in this domain.

As we all are aware about the rapid raise in wireless communication has huge demand. Spectrum sensing acts a key role part in (CR)network to highlight presence of the resource. This project focuses on the issues of Spectrum sensing in detection performance in process is usually compromised with the multipath. Cast of shadow over and receive uncertain errors. To alleviate the consequences of these errors Cooperative been shown that well organized methodology to intensify the detection performance. We also talk about the performance of the CR for 5 th generation & if possible route the frequency allocation .Taking into count many detection performance of cooperative secondary users, we have gather a completely unique reliability based decision combination program where weight is assign to each and every secondary user local conclusion supported its reliability .As the grasp of local prospect of detection and warning for every secondary detector might not be aware of the practice , we also employ a counting process to reckon these probabilities supported past global and native decisions.