Cognitive Radio-Spectrum Sensing Research Papers (original) (raw)
Next generation networks are expected to operate in licensed, shared as well as unlicensed spectrum to support spectrum demands of a wide variety of services.Due to shortage of radio spectrum, the need for communication systems(like... more
Next generation networks are expected to operate in licensed, shared as well as unlicensed spectrum to support spectrum demands of a wide variety of services.Due to shortage of radio spectrum, the need for communication systems(like cognitive radio) that can sense wideband spectrum and locate desired spectrum resources in real time has increased.Automatic modulation classifier (AMC) is an important part of wideband spectrum sensing (WSS) as it enables identification of incumbent users transmitting in the adjacent vacant spectrum.Most of the proposed AMC work on Nyquist samples which need to be further processed before they can be fed to the classifier.Working with Nyquist sampled signal demands high rate ADC and results in high power consumption and high sensing time which is unacceptable for next generation communication systems.To overcome this drawback we propose to use sub-nyquist sample based WSS and modulation classification. In this paper, we propose a novel architecture call...
As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT secondary users through... more
As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT secondary users through utilizing spectrum holes resulting from the underutilization of frequency spectrum. A CR needs to frequently sense the spectrum to avoid interference with primary users (PUs). Compressive spectrum sensing techniques have been gaining increasing interest in wideband spectrum sensing, as they reduce the need for high-rate analog-to-digital converters, reducing the complexity and energy requirements of the CR. To enhance spectrum sensing performance, researchers proposed to incorporate PU spectrum usage information into the process of spectrum sensing. Spectrum usage information can be obtained through pilot signals, geo-locational databases or through evaluation of previous spectrum sensing results. In this paper, we are studying the effects of compressive sensing parameters namely compression ratio, sensing period, and sensing duration on the estimation of primary user behavior statistics. We achieved an accurate estimation of the primary user's behavior while saving 40% of the sampling rate by using compressive spectrum sensing compared to traditional spectrum sensing with Nyquist rate sampling.
Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and... more
Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and derive certain detection performance. To deal with this uncertainty, learning based approaches are being adopted and more recently deep learning based tools have become popular. Here, we propose an approach of spectrum sensing which is based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN). Use of LSTM facilitates implicit feature learning from spectrum data. The DLN is trained using several features and the performance of the proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto. The testbed is trained to acquire the primary signal of a real world radio broadcast taking place using FM. Experimental data show that even at low signal to noise ratio, our approac...
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... more
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... more
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.