Related papers: Eigenvalue based Spectrum Sensing Algorithms for C…
Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at…
Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning, such as support vector machine (SVM) and kernel principal component analysis…
Spectrum sensing enables cognitive radio systems to detect unused portions of the radio spectrum and then use them while avoiding interferences to the primary users. Energy detection is one of the most used techniques for spectrum sensing…
Spectrum Sensing (SS) constitutes the most critical task i n Cognitive Radio (CR) systems for Primary User (PU) detection. Cooperative Spectrum Sensing (CSS) is introduced to enhance the detection reliability of the PU in fading…
In this paper we develop a complete analytical framework based on Random Matrix Theory for the performance evaluation of Eigenvalue-based Detection. While, up to now, analysis was limited to false-alarm probability, we have obtained an…
Cognitive radio that supports a secondary and opportunistic access to licensed spectrum shows great potential to dramatically improve spectrum utilization. Spectrum sensing performed by secondary users to detect unoccupied spectrum bands,…
Due to limited availability of spectrum for licensed users only, the need for secondary access by unlicensed users is increasing. Cognitive radio turns out to be helping this situation because all that is needed is a technique that could…
Spectrum sensing is a fundamental component in cognitive radio. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing model is presented that…
Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces…
We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there may be multiple primary users. A noise-uncertainty-free detector that is optimal in the low signal to noise ratio regime is analyzed in such a…
Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by…
Powerful spectrum decision schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. One of the key effecting factor on the CR network throughput is the…
Spectrum sensing is a key problem in cognitive radio. However, traditional detectors become ineffective when noise uncertainty is severe. It is shown that the entropy of Gauss white noise is constant in the frequency domain, and a robust…
Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper,…
With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a…
Spectrum sensing is the major challenge in the cognitive radio (CR). We propose to learn local feature and use it as the prior knowledge to improve the detection performance. We define the local feature as the leading eigenvector derived…
We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there may be multiple primary users. A detector based on the spherical test is analyzed in such a scenario. Based on the moments of the distributions…
This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting an unknown non-random signal by capitalizing on the leading eigenvalue of the whitened sample…
Spectrum sensing is the challenge for cognitive radio design and implementation, which allows the secondary user to access the primary bands without interference with primary users. Cognitive radios should decide on the best spectrum band…
Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrum sensing methods, the information theoretic criteria (ITC) based method is a promising blind method…