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Improvement on throughput of cognitive radio networks is possible by spectrum sensing when the required sample number (sensing time) is relatively small. This is due to the fact that when the sample number for sensing is smaller, time for a cognitive radio user to transmit is larger. This paper presents a scheme of sequential detector which has average sample number lower than its counterpart, a quadratic...
Multi-antenna spectrum sensing algorithms for cognitive radio are receiving a lot of attention recently. In this paper, we consider multi-antenna detection when the noise covariance matrix is assumed to be arbitrary and unknown. The studies leading to this paper have been motivated by the existence but typically unknown noise correlation in practice. A multiple primary user detector, derived from...
This paper is concerned with detector design and performance analysis for spectrum sensing of phase-modulated signal in cognitive radio systems. Under the maximum a posteriori probability criterion, the optimal detector which outperforms the popular energy detector is derived, and its detection and false-alarm probabilities that describe the receiver operating characteristic (ROC) are analyzed. These...
In this paper, the problem of multiantenna spectrum sensing in cognitive radio (CR) is addressed within a Bayesian framework. Unlike previous works, our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for...
In Cognitive Radio technology, spectrum sensing is a fundamental component. Secondary users may utilize the frequency bands of primary users when these bands are not being used. To support this spectrum reuse functionality, secondary users are required to sense the radio frequency environment. Eigenvalue ratio based spectrum sensing is one among the best achievable solutions. In this paper, we have...
In this paper we propose a robust energy detector for spectrum sensing in cognitive radio systems. The detector is derived by modelling the noise process as consisting of two components, Gaussian noise and impulsive noise. The impulsive noise is modelled using a uniform distribution that appears with a certain probability. A convenient approximation using the max operator is then applied to the resulting...
In this paper, we obtain approximations for the optimal Log-Likelihood Ratio (LLR) decision rule in cooperative detection when local energy detectors are assumed. Considering conditional independence, we also show under which bandwidth and sampling frequency regimes these approximations hold best. Furthermore, we present simulations where the performance of the approximated LLR decision rule is compared...
In the context of spectrum sensing, we investigate the performance of detectors equipped with M antennas (co-located or distributed) under Rayleigh fading, in terms of detection diversity. Rather than the high-SNR concept of diversity order common in the communications literature, we adopt the notion recently advocated by Daher and Adve in the radar community: the slope of the average probability...
Based on the high probability that primary user is idle in cognitive radio networks, we propose an optimal Bayesian detector structure for spectrum sensing. Although the optimal detector by Neyman-Pearson theorem maximizes the detection probability for a given false alarm probability, Bayesian detector can achieve a higher overall spectrum utilization and SU throughput and at the same time the primary...
Using the multitaper spectrum (MTS) for spectrum sensing in cognitive radio has been investigated by a number of researchers. An MTS detector can be applied to various operational environments with the knowledge of perfect or inaccurate noise variance. So far, however, no systematic procedure is available for the determination of theoretical thresholds and their corresponding detection performance...
In order to avoid packet collisions in wireless communications, cognitive radio users require spectrum sensing to increase their awareness. Spectrum sensing methods must show good performance with low complexity, particularly in low-power wireless sensor networks. Motivated by the non-continuous signal patterns observed from realistic signals, an adaptive spectrum sensing algorithm that exploits information...
Eigenvalue based detection is an ideal solution to spectrum sensing problem in cognitive radio networks. However, results on its sensing performance are very limited. In this paper, we analytically investigate the sensing performance of the eigenvalue ratio based detection. Firstly, a simple and accurate Gaussian approximation to the extreme eigenvalue distribution is proposed. Secondly, a closed-form...
The key enabling functionality of a cognitive radio is to ensure that it would not interfere with primary users, by reliably detecting primary user signals. Due to the low computational complexity, Energy detector is a widely used spectrum sensing method. The performance of energy detector in fading channels has been previously analyzed in some papers. However, these analyses are based on the exact...
We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely unknown noise and signal powers, we derive a GLRT detector based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector...
One of the key problems in cognitive radio (CR) is the detection of primary activity in order to determine which parts of the spectrum are available for opportunistic access. This detection task is challenging, since the wireless environment often results in very low SNR conditions. Moreover, calibration errors and imperfect analog components at the CR spectral monitor result in uncertainties in the...
We study the sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Sequential Energy Detector and provide a comprehensive study of its performance. The sensitivity of the Sequential Test to primary signal variance estimation is addressed for the first time ever in this paper. Specifically, we develop an Iterative Hybrid Bayesian method...
The primary signal detection is an essential operation for the secondary spectrum usage. In this paper we extend the covariance based detection for multiple-antenna receiver. The proposed method uses the noise power estimation and does not suffer from the noise level uncertainty. We analyze the detection algorithm and compute the distribution of the decision variable not only for the pure noise case...
In this paper we investigate the detection of OFDM primary signals using autocorrelation. The conventional model for signal detection in cognitive radio assumes that the primary signal is either absent or present with a fixed level. We show that the conventional model is the source of significant discrepancy between the analytical prediction of the detection algorithm and the simulations. We propose...
This paper addresses the problem of spectrum sensing for cognitive radio, in the case of a primary signal characterized by a discontinuous channel occupation within the considered sensing window. Under such conditions, the performance of two different energy detectors is investigated. The first one (energy average detector) decides whether the channel is free or busy on the basis of the energy sample...
In order to achieve a dynamic spectrum management as in cognitive radio, the first task is to sense the spectrum holes. This paper is trying to make a contribution in the challenge of spectrum sensing for cognitive radios. We consider a case in which the local sensors involved in the spectrum sensing are aware of the primary signal's digital modulation. As a case of study the primary user's signal...
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