Target False Alarm Probability Research Articles

Artificial Bee Colony Based Iterative Heuristic Signal Detection Technique for Optical Wireless Communications

In this paper, we consider a problem of optical signal detection and present a novel artificial bee colony (ABC) based heuristic approach for optical spectrum sensing in a free-space optical wireless network that overcomes channel impairment due to noise uncertainty. In particular, we propose a generic ABC-based optimization framework in conjunction with eigenvalue-based detection (EVD). The uniqueness of the proposed algorithm is that it is executed iteratively by comparing various local solutions until an optimum solution is obtained. The channel is characterized as correlated multivariate fading and the received signal is quantified as a continuous waveform wideband detector. We derive the joint statistic of the correlated multivariate channel fading in the multiple-input multiple-output (MIMO) optical network. Subsequently, the marginal probability density function of the channel coefficients is obtained. For comparison purposes with existing optical spectrum sensing techniques, noise uncertainty is considered. It is demonstrated from simulations that the proposed ABC-EVD spectrum sensing yields performance improvement and the analytical results derived are verified. From the comparative study, it is also found that the proposed scheme outperforms existing optical spectrum sensing techniques, particularly in a regime of low signal-to-noise ratio. When compared to EVD, it is shown that, at a received signal-to-noise ratio of -15 dB, the performance gain achieved in terms of the probability of detection is 15% higher over the entire range of target probability of false alarm. When compared with the ED technique, the proposed technique outperforms it with approximately 50% higher probability of detection at the noise uncertainty of 1 dB and received SNR of -10 dB. The proposed algorithm offers fast convergence, strong robustness, and high flexibility.

Threshold Setting for Multiple Primary User Spectrum Sensing via Spherical Detector

It is known that the spherical detector (SD) is very efficient for the multiple primary user spectrum sensing in cognitive radios. However, numerical results showed that the decision threshold obtained by the existing approximation methods has high accuracy in large sample scenarios, but it is not the case in small sample situations. In this letter, we develop SD for the practical applications with small observations. Here, two methods to calculate the decision threshold of the SD are proposed. In the first approach, the accurate expression, in the form of Meijer's G-function, for the probability of false-alarm is derived. Based on this, the accurate threshold can be obtained via the numerical solving when a target false-alarm probability is given. In the second method, an approximation method is presented to lead to a simple expression for the decision threshold, which enables us to directly compute the threshold with low computation complexity and high accuracy. Simulation results verify the effectiveness of the two proposed methods.

Energy Detection Scheme in the Presence of Burst Signals

This letter proposes an energy detection scheme based on short windows to detect the presence of a burst signal that spans over a short period of time. For practical use, the proposed scheme provides a simple computational method for the threshold, with which the measured energy is compared, that satisfies a target false alarm probability and also the optimal length of the short window that minimizes miss detection probability. To evaluate the performance of the proposed scheme, miss detection probability is analyzed as a function of the threshold. Simulation results demonstrate that the proposed scheme provides a lower miss detection probability than the conventional scheme when a burst signal is present.

Hybrid cooperative spectrum sensing scheme based on spatial–temporal correlation in cognitive radio enabled VANET

Cognitive radio enabled vehicular ad-hoc networks (CR-VANETs) are one of the promising architectures in the future vehicular ad-hoc networks. In this study, the joint spatial-temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing (CSS) while reducing overhead introduced by cooperation in the CR-VANETs. Firstly, a theoretical method is presented to analyse the impact of spatial-temporal correlation on cooperative sensing performance when using soft combining in the CR-VANET. Then, the expression of the optimal probability of detection with respect to spatial-temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double-threshold optimisation problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid CSS scheme based on spatial-temporal correlation is designed for the CR-VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial-temporal diversity.

Kernelized Generalized Likelihood Ratio Test for Spectrum Sensing in Cognitive Radio

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. The thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results.

On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio.

In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary and secondary users are better off in terms of spectral efficiency. However, without assigning a proper sensing threshold to each sensing node, cooperation may not be effective unless the received average primary user signal-to-noise ratio (SNR) is identical. We show that equal threshold energy detection severely degrades sensing performance when cooperative sensing nodes experience diverse average SNRs, and it is not unusual for even single-node sensing to be better than cooperative sensing. To this end, based on the Neyman–Pearson criterion we formulate an optimization problem to maximize sensing performance by using optimized thresholds. Since this is a non-convex optimization problem, we provide a condition that convexifies the problem and thus serves as a sufficient optimality condition. We find that, perhaps surprisingly, in all practical cases one may consider this condition satisfied, and thus optimal sensing performance can be obtained. Through extensive simulations, we demonstrate that the proposed technique achieves a globally optimal solution, i.e., it maximizes the probability of detection under practical operating parameters such as the target probability of false alarm, different SNRs, and the number of cooperative sensing nodes.

Iterative algorithms for impulsive noise reduction in OFDM-based power line communications

Power line communications (PLC) is a technology that permits data transmission using electrical networks. In the last few years, multimedia transmission (e.g., audio, image and video) over electrical signals has received a huge amount of research interest thanks to the already-existing indoor networks. However, impulsive noise presents the most impairments in PLC systems caused by switching transients in the in-home networking. In this paper, we propose some iterative suppression algorithms based on accurate impulsive noise estimation, using an adaptive threshold under a target false alarm probability. The proposed algorithms detect the amplitudes of impulsive noise, and then cancel them iteratively from the received contaminated signal. The results show that the proposed algorithms provide noticeable improvement in terms of bit error rate, peak signal-to-noise ratio, and visual reconstructed image quality, when compared to that of the conventional methods.

Principal component analysis‐based blind wideband spectrum sensing for cognitive radio

A principal component analysis-based blind wideband spectrum sensing (WSS) algorithm is presented, in which the WSS issue is transformed into a sequential binary hypothesis test under the framework of the general likelihood ratio test. The proposed method operates simultaneously over all the subbands rather than one single subband each time. Furthermore, the new method overcomes the noise uncertainty problem, and can also perform well without information about the channel, the primary signal, and the noise power. Most importantly, unlike the existing classical blind wideband detectors based on the information theoretic criterion, the decision threshold for the proposed detector can be flexibly determined according to the target false-alarm probability. Simulation results verify its effectiveness and superiority to the existing sensing algorithms.

Intelligent selection of threshold in cognitive radio system

To evaluate the performance of cognitive radio (CR) under the energy detection scheme, the proper selection of threshold is an important and a critical task. The threshold is generally selected, either under a target probability of detection $$(\overline{P_d})$$(Pd¯) called as the constant detection rate (CDR) principle, or under the target probability of false alarm $$(\overline{P_{fa}})$$(Pfa¯), called as the constant false alarm rate (CFAR) principle. To ensure sufficient protection to the licensed users (or primary users), the selection of threshold under the CDR principle is best suited. This paper discusses and analyzes the inefficiency of CR under the blind use of CDR principle, mainly, when primary receiver is located at a sufficient distance d from the secondary transmitter where signal to interference plus noise ratio (SINR) is larger than a cut-off value $$SINR_{th}$$SINRth (which is a minimum SINR required to properly decode the intended information of primary user). To overcome this inefficiency, we propose an approach which while considering distance d between the secondary transmitter and primary receiver as an important parameter, makes an interference-aware selection of threshold based on the CDR and CFAR principles. Under the proposed approach, the CR system achieves a significant gain in its throughput even under the low signal to noise ratio conditions.

Spectrum Sensing Optimization and Performance Enhancement of Cognitive Radio Networks

Cognitive radio (CR) is an attractive approach to face the shortage in the electromagnetic spectrum resources and improve the overall spectrum utilization. In this paper, two spectrum sensing techniques are considered for CR networks; one based on energy detection and the other based on multi-taper spectral estimation. Energy detector (ED) is examined in detail for both single user and cooperative multiple users using K out of M fusion rule. Simulation results in both scenarios are evaluated and verified using a Genetic Algorithm optimizer. The results show that cooperative sensing is better in performance than standalone sensing. The superiority of the Multi-taper spectral estimator (MSE) to the ED is comprehensively explained for a single secondary user scenario. The probability of detection at different target false alarm probabilities is evaluated. Simulation results show that MSE outperforms the ED by about 10 dB. Simulation results also show that as the target false alarm probability is decreased, the required Signal-to-Noise Ratio to achieve the same probability of detection gets higher.

Impact of Antenna Correlation on Optimum Improved Energy Detector in Cognitive Radio

This paper investigates the detection performance of an improved energy detector for a secondary user with spatially correlated multiple antennas. In an improved energy detector, an arbitrary positive power operation p replaces the squaring operation in a conventional energy detector, and the optimum value of p that gives the best detection performance may be different from 2. Firstly, for a given value of p, we derive closed-form expressions for the probability of detection and the probability of false alarm when antennas at the secondary user are exponentially correlated. We then find the optimum value of p for two different detection criteria−maximizing the probability of detection for a target probability of false alarm, and minimizing the probability of false alarm for a target probability of detection. We show that the optimum p is strongly dependent on system parameters like number of antennas, antenna correlation coefficient among multiple antennas, and average received signal-to-noise ratio (SNR). From results, we infer that, in low SNR regime, the effect of antenna correlation is less pronounced on the optimum p. Finally, we find the optimum values of p and threshold jointly that minimize the total error rate. key words: cognitive radio; correlation, improved energy detector, multiple antennas, total error rate

An Enhanced Cooperative Spectrum Sensing with Wavelet Denoising and Softened Hard Decision for Cognitive Radio Networks

Cognitive Radio (CR) is a novel technology that can improve the utilization efficiency of the radio spectrum and also address the spectrum scarcity problem by opportunistically using the unused channels (spectrum holes / white spaces) in the Licensed Spectrum of Primary Users. Spectrum sensing is the primary function of CR which determines the spectrum holes for opportunistic usage by Secondary Users. Spectrum sensing plays a critical role in Cognitive Radio. The development of a reliable and accurate spectrum sensing is very essential to propound quick and accurate detection of white spaces for better throughput of Secondary User network under the constraint of the specified Primary System protection. In this paper, a Cooperative Spectrum Sensing using Wavelet Denoising along with Soften Hard Decision technique has been proposed to improve the performance of spectrum sensing. Simulation result shows an improvement of nearly 15% in the Probability of Missed Detection for a target Probability of False Alarm of 0.1 and SNR of -10 dB. Also 50% improvement in error probability has been achieved with 1-D one level wavelet denoising.

Kernel Feature Template Matching for Spectrum Sensing

Feature template matching (FTM) was proposed by Zhang and Qiu in 2011 for spectrum sensing in cognitive radio. Theoretical analysis for FTM is, however, missing in the literature. This paper will address this issue. Another new direction suggested by this paper is a nonlinear version of FTM, which is called kernel FTM (KFTM). The proposed nonlinear algorithm is performed on data mapped to a kernel space usually with higher dimension by a general nonlinear mapping. The high-dimensional data are cheaply operated with the so-called kernel trick. Furthermore, higher order statistics is considered in the proposed algorithm. Simulations using the real-world measurements of digital television (DTV) signal show that a gain of more than 4 dB can be achieved for KFTM over its linear counterpart FTM. Theoretical analysis is conducted for KFTM (that can be directly applied to FTM). For the first time, a theoretical justification is also performed for FTM. The concentration inequalities of KFTM, which are valid for arbitrary dimensions, are established as a result of concentration of measure phenomenon. The closed-form expressions of the probability distributions (under null hypothesis) are derived for both FTM and KFTM under different kernel functions. The obtained closed-form results agree with simulations. The closed-form expressions of the decision thresholds for a target false-alarm probability are obtained as well. The thresholds are noise power independent; thus, the proposed algorithm overcomes the difficulty of noise uncertainty.

Optimal MAC based cooperative spectrum sensing in cognitive radio networks

This paper considers a cognitive radio (CR) system in non-ideal fading wireless channels and proposes cooperative spectrum sensing schemes based on coherent multiple access channels (MAC), serving as an alternative way to improve the cooperative spectrum sensing performance and provide space diversity for spectrum sensing. Sufficient statistics are transmitted using a common channel from the secondary users (SUs) to a fusion center (FC) where the global decision is obtained. The optimal scaling factors of the proposed schemes are obtained by maximizing the detection probability under a target false alarm probability and a transmit power constraint. Because the proposed optimal MAC scheme has high computational complexity, a sub-optimal solution based on maximization of the deflection coefficient (DC) is also proposed. Simulation results show that the proposed algorithms can significantly improve the spectrum sensing performance and approach the detection baseline.

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

Cognitive Transmissions with Multiple Relays in Cognitive Radio Networks

In cognitive radio networks, each cognitive transmission process typically requires two phases: the spectrum sensing phase and data transmission phase. In this paper, we investigate cognitive transmissions with multiple relays by jointly considering the two phases over Rayleigh fading channels. We study a selective fusion spectrum sensing and best relay data transmission (SFSS-BRDT) scheme in multiple-relay cognitive radio networks. Specifically, in the spectrum sensing phase, only the initial spectrum sensing results, which are received from the cognitive relays and decoded correctly at a cognitive source, are selected and used for fusion. In the data transmission phase, only the best relay is utilized to assist the cognitive source for data transmissions. Under the constraint of satisfying a required probability of false alarm of spectrum holes (for the protection of the primary user), we derive an exact closed-form expression of the spectrum hole utilization efficiency for the SFSS-BRDT scheme, which is used as a measure to quantify the percentage of spectrum holes utilized by the cognitive source for its successful data transmissions. For the comparison purpose, we also examine the spectrum hole utilization efficiency for a fixed fusion spectrum sensing and best relay data transmission (FFSS-BRDT) scheme, where all the initial spectrum sensing results are used for fusion without any refined selection. Numerical results show that, under a target probability of false alarm of spectrum holes, the SFSS-BRDT scheme outperforms the FFSS-BRDT scheme in terms of the spectrum hole utilization efficiency. Moreover, the spectrum hole utilization efficiency of the SFSS-BRDT scheme always improves as the number of cognitive relays increases, whereas the FFSS-BRDT scheme's performance improves initially and degrades eventually after a critical number of cognitive relays. It is also shown that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time durations between the spectrum sensing and data transmission phases for both the FFSS-BRDT and SFSS-BRDT schemes.

Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms.