The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Spectrum Sensing is an intensively studied topic in cognitive radio to locate unoccupied spectrum for improved channel utilization. However, the problem becomes more challenging in wideband spectrum sensing due to the limitation of hardware operational bandwidth. In this paper, we introduce a cooperative compressive spectrum sensing scheme to monitor the wideband spectrum usage in both frequency and...
Automatic modulation classification (AMC) is an important component in cognitive radio and many efforts have been made to improve the AMC's successful classification rate, especially when the environment is noisy. The cyclic feature has excellent resiliency to noise, so it has been frequently adopted as the feature for AMC. In this paper, in order to enhance the reliability of the system, we propose...
Spectrum sensing is one of the most challenging problems in cognitive radio systems. It is frequently impractical to implement theoretical methods due to the limitation of the existing hardware operational bandwidth. To solve this problem, an emerging technique, compressive sensing (CS), is introduced to cognitive radio field so that only compressive measurements are needed in real implementation...
In this paper, we introduce a learning based cognitive radio receiver to automatically demodulate several types of modulated signals without sophisticated digital signal pre-processing. Our embedded learning engine can automatically learn the signal features and then achieve signal demodulation through feature-based classification. The proposed demodulator consists of a neural network (NN) structure...
Automatic modulation recognition (AMR) and demodulation are two essential components in cognitive radio receivers. This paper proposes a novel method based on MSOM neural networks to automatically recognize the modulation type and demodulate the radio signal at the same time. This efficient method is directly applied to the normalized radio signal samples and has relatively low computation complexity...
Automatic modulation recognition (AMR) of communication signals is a critical and challenging task in cognitive radio systems. In this work, classifications of four digital modulation types, including BPSK, QPSK, GMSK and 2FSK, are investigated. From the received radio signal, a set of cyclic spectrum features are first calculated, and a principal component analysis (PCA) is applied to extract the...
Cognitive ultra wideband radio is proposed to exploit the advantages of combining cognitive radio with ultra wideband technologies, so as to solve the problems of coexistence and compatibility between UWB and other existing narrowband wireless systems. A novel adaptive UWB pulse shaping algorithm with low complexity is presented for producing the expected spectral notches right in the frequency band...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.