Multiple Antenna Spectrum Sensing in Cognitive Radios

Abstract-In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the Generalized Likelihood Ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results provide the available traded-off in using multiple antenna techniques for spectrum sensing and illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given noise variance

On the performance of Hadamard ratio detector-based spectrum sensing for cognitive radios

We consider the problem of multiantenna spectrum sensing (SS) in cognitive radios (CRs) when the receivers are assumed to be uncalibrated across the antennas. The performance of the Hadamard Ratio Detector (HRD) is analyzed in such a scenario. Specifically, we first derive the exact distribution of the HRD statistic under the null hypothesis, which leads to an elaborate but closed-form expression for the false-alarm probability. Then, we derive a simpler and tight closed-form approximation for both the false-alarm and detection probabilities by using a moment-based approximation of the HRD statistical distribution under both hypotheses. Finally, the accuracy of the obtained results is verified by simulations.Peer ReviewedPostprint (author's final draft

Secure robust collaborative spectrum sensing in the presence of smart attackers

In this paper, collaborative spectrum sensing to detect random signals corrupted by Gaussian noise in the presence of smart attackers is studied. Unlike to the most of related works, we consider a blind attacker detection so that there is no information about the number of attackers and attack strengths. Also, in our proposed scheme, no information about the Primary User (PU) signal and noise is considered to detect the misbehaved Secondary Users (SUs). In addition, the attack strengths can be chosen differently for each attacker. It is proposed to get assistance from some trusted SUs. We obtain a blind detector and then, a detection algorithm using derived detector to detect the attackers is proposed. Furthermore, in order to evaluate the performance of the proposed detector, the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed detector outperforms significantly the similar secure spectrum sensing schemes.This research work is funded by Qatar National Research Fund (A member of Qatar Foundation) under grant number NPRP 7-923-2-344. The statements made herein are the sole responsibility of the authorsScopu

Finite-sample size multiple antennas spectrum sensing

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) by exploiting the prior information about unknown parameters. Specifically, we consider a blind spectrum sensing problem when the channel gains and the noise variance are unknown for the Secondary User (SU). Under assumption that additional statistical side-information is available about unknown parameters, we use a novel Generalized Likelihood Ratio (GLR) test, which is optimal under finite number of samples, in order to derive our proposed detector. As it has been shown, this novel GLR test need to obtain the Maximum A-posteriori Probability (MAP) estimation of unknown parameters which it is impossible to obtain them in closed form for our case. Thus, we calculate them based on the Expectation-Maximization (EM) algorithm. The simulation results show that our proposed detector has good performance even for finite number of samples and also outperforms the classical GLR detector. 2012 IEEE.Scopu

Finite-Sample Size Multiple Antennas Spectrum Sensing

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) by exploiting the prior information about unknown parameters. Specifically, we consider a blind spectrum sensing problem when the channel gains and the noise variance are unknown for the Secondary User (SU). Under assumption that additional statistical side-information is available about unknown parameters, we use a novel Generalized Likelihood Ratio (GLR) test, which is optimal under finite number of samples, in order to derive our proposed detector. As it has been shown, this novel GLR test need to obtain the Maximum A-posteriori Probability (MAP) estimation of unknown parameters which it is impossible to obtain them in closed form for our case. Thus, we calculate them based on the Expectation-Maximization (EM) algorithm. The simulation results show that our proposed detector has good performance even for finite number of samples and also outperforms the classical GLR detector

The effect of additional statistical side information on multiple antenna spectrum sensing

In this paper, we consider the problem of multiple antenna spectrum sensing in Cognitive Radios (CR) when some or all parameters are unknown. The Generalized Likelihood Ratio (GLR) test is the convectional method to solve the composite hypothesis testing problem in which the detection and estimation sub-problems are considered separately. In this paper, the multiple spectrum sensing problem is solved using a novel approach in which the the detection and estimation sub-problems considered jointly and the resulted detectors are optimal under finite number of samples. We assume some additional side statistical information is available for unknown parameters and as theoretical results of the novel GLR detector imply, the optimal way of using this additional side information, is to use them in the Maximum A-Posteriori (MAP) or Minimum Mean Square Error (MMSE) estimation of unknown parameters for constructing the GLR tests. The simulation results show that the newly derived GLR detectors outperform traditional GLR detectors significantly. Also for the situation that all parameters are unknown the proposed detectors are compared with the Energy Detector (ED), where the simulation results indicate that the proposed detectors not only have significantly better performance, but also are robust to practical noise mismatch. 2012 IEEE.Scopus2-s2.0-8487765711

Optimized Error Probability for Weighted Collaborative Spectrum Sensing in Time-and Energy-Limited Cognitive Radio Networks

In this paper, a collaborative energy-harvesting cognitive radio (CR) network is considered such that the transmitter of the secondary user (SU) is allowed to harvest signal energy of the primary user (PU) when the presence of the PU is detected. The harvested energy is converted to electrical power in order to supply the sensing and transmission energy of SUs. The time frame (time slot) is divided into two phases allocated to sensing (divided into two durations: spectrum sensing and results reporting) and transmission, respectively. The time spanned by the results reporting duration depends on the number of collaborative sensing users, while the time spent on spectrum sensing duration controls the number of sensing samples. A constrained convex optimization problem of the overall probability of error is formulated incorporating constraints on time and energy resources along with PU interference protection presented as a threshold on probability of collision. We use a soft decision rule scheme while considering two energy harvesting scenarios namely, energy surplus and energy deficit. In each scenario, the convexity of the optimization problem is established analytically and the global optimal solution is obtained. Simulation results are provided to demonstrate the impact of the different parameters on the overall system performance as well as to verify the deduced analytical results. 1 1967-2012 IEEE.Manuscript received January 7, 2017; revised April 30, 2017; accepted May 11, 2017. Date of publication May 18, 2017; date of current version October 13, 2017. This work was supported by the Qatar National Research Fund, QNRF (a member of the Qatar Foundation, QF), under a research grant NPRP 7-923-2-344. The statement made herein are the sole responsibility of the authors. The review of this paper was coordinated by Dr. Yao Ma. (Corresponding author: Abbas Taherpour.) A. Taherpour and H. Mokhtarzadeh are with the Department of Electrical Engineering, Imam Khomeini International University, Qazvin 3414916818, Iran (e-mail: [email protected]; [email protected]).Scopu