Conventional Energy Detection Research Articles

Kernel canonical correlation analysis for robust cooperative spectrum sensing in cognitive radio networks

AbstractSpectrum sensing is a key operation in cognitive radio (CR) systems, where secondary users (SUs) are able to exploit spectrum opportunities by first detecting the presence of primary users. In a CR network composed of several SUs, the detection accuracy can be much improved by cooperative spectrum sensing strategies, which exploit the spatial diversity among SUs. However, cooperative detection strategies, which are typically based on energy sensing, do not perform satisfactorily under impairments such as non‐Gaussian noise or interferences. In this paper, we propose a scheme based on kernel canonical correlation analysis (KCCA), which is able to operate in non‐ideal scenarios and in a totally blind fashion. This technique is performed at the fusion centre (FC) by exploiting the non‐linear correlation among the received signals of each SU. In this manner, statistical tests are extracted, allowing the SUs to make decisions either autonomously at each SU or cooperatively at the FC. The performance of the KCCA‐based detector is evaluated by means of simulations and over‐the‐air experiments using a CR testbed composed of several Universal Software Radio Peripheral nodes. Both the simulations and the measurements show that the KCCA‐based detector is able to obtain a significant gain over a conventional energy detector, whose sensing performance is severely degraded by the presence of external interferers. Copyright © 2014 John Wiley & Sons, Ltd.

Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

Magnetic anomaly detection (MAD) is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise) with a power spectral density of 1/fa (0<a<2), which is non-stationary, self-similarity and long-range correlation. Meanwhile the orthonormal wavelet decomposition can play the role of a Karhunen-Loève-type expansion to the 1/f-type signal by its decorrelation abilities, an effective energy detection method based on undecimated discrete wavelet transform (UDWT) is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI) magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT), the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.

GoF-Based Spectrum Sensing of OFDM Signals over Fading Channels

Goodness-of-Fit (GoF) based spectrum sensing of orthogonal frequency-division multiplexing (OFDM) signals is investigated in this paper. To this end, some novel local sensing methods based on Shapiro-Wilk (SW), Shapiro-Francia (SF), and Jarque-Bera (JB) tests are first studied. In essence, a new threshold selection technique is proposed for SF and SW tests. Then, three studied methods are applied to spectrum sensing for the first time and their performance are analyzed. Furthermore, the computational complexity of the above methods is computed and compared to each other. Simulation results demonstrate that the SF detector outperforms other existing GoF-based methods over AWGN channels. Furthermore simulation results demonstrate the superiority of the proposed SF method in additive colored Gaussian noise channels and over fading channel in comparison with the conventional energy detector.

Sequential Cooperative Spectrum Sensing Technique in Time Varying Channel

Cognitive radio opportunistically accesses the spectrum while the licensed user is idle. A spectrum sensing procedure to monitor primary users' existence is therefore vital to cognitive radios. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channels. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We propose the weighted sequential energy detector (SED) in which a fixed number of past observations are taken for decision making. In addition, we propose two adaptive schemes, namely the Two-Stage SED and the Differential SED, where a cognitive radio user uses previously received energy values until it detects a change in primary user's activity. The probability of false alarm and detection for the Weighted SED and Two-Stage SED schemes are derived. The results show that the proposed schemes achieve better detection performance over the conventional cooperative energy detection technique. In particular, the Two-Stage SED and Differential SED schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenarios that conventional techniques cannot accommodate.

A Novel Scheme to Improve the Spectrum Sensing Performance

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 efficiently detect the empty spaces and provide them to the secondary devices without causing any interference to the primary (licensed) users. Spectrum sensing is the foremost function of the cognitive radio which senses the environment for white spaces. Energy detection is one of the various spectrum sensing techniques that are under research. Earlier it was shown that energy detection works better under AWGN channel as compared to Rayleigh channel, however the conventional spectrum sensing techniques have a high probability of false alarm and also show a better probability of detection for higher values of SNR. There is a need for a new technique that shows a reduced probability of false alarm as well as an increase in the probability of detection for lower values of SNR. In the present work the conventional energy detection technique has been enhanced to get better results.

Passive Primary Receiver Detection for Underlay Spectrum Sharing in Cognitive Radio

In this letter, we propose a passive detection method to enable cognitive user to detect active primary receiver. With our method, cognitive user may conduct underlay spectrum sharing without the need of channel state information between primary and cognitive users. Simulation results show that our detector can provide about 100% to 300% more access probabilities in average than conventional energy detector.

A Blind Likelihood-Based Approach for OFDM Spectrum Sensing in the Presence of I/Q Imbalance

We investigate the spectrum sensing problem in orthogonal frequency-division multiplexing-based cognitive radio networks under I/Q imbalance. We start by deriving the likelihood ratio test in the presence of I/Q imbalance at the analog front-ends of both the primary and secondary users. In addition, we derive closed-form expressions for the probabilities of detection and false alarm and the receiver operating characteristics and examine their dependence on both transmit and receive I/Q imbalance levels. Furthermore, we compare the performance of the likelihood ratio test with that of the energy detector and demonstrate the superiority of the former over the latter. Next, we generalize our analysis to the blind case where we derive simple closed-form expressions for the generalized likelihood ratio test and its false alarm probability as a function of the received signal only, i.e. without requiring any knowledge of the primary-to-secondary channel response, noise statistics, or I/Q imbalance parameters. Our results demonstrate that the correlation properties of the primary user's signal induced by transmit I/Q imbalance are signal features that can be exploited in a blind fashion at the secondary user to enhance the detection probability significantly compared to the conventional energy detector.

Non-parametric Blind Spectrum Sensing Based on Censored Observations for Cognitive radio

In cognitive radio, spectrum sensing is a challenging task. In this paper, a spectrum sensing method based on censored observations is proposed. We call it as Censored Anderson Darling (CAD) sensing. We present the performance of the CAD sensing method with receiver operating characteristics (ROC) in fading channels using simulations. It is observed that the proposed method outperforms the conventional energy detection (ED) at lower signal to noise ratio. It also provides better detection performance compared to Ordered Statistics (OS) based sensing method. We also use the CAD sensing method assuming noise uncertainty. It means noise variance at secondary user(SU) is unknown. We call it as Blind CAD sensing (B-CAD). We also show that the B-CAD outperforms the energy detection.

Spectrum sensing method based on goodness of fit test using chi‐square distribution

In cognitive radio, spectrum sensing is a challenging task. Proposed is a new spectrum sensing method based on the goodness of fit test of the energy of the received samples with a chi-square distribution. The test statistic is derived and the performance of the proposed method is evaluated by Monte Carlo simulations. It is shown that the proposed spectrum sensing method outperforms the conventional energy detection method without increasing the complexity of the sensing.

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Spectrum-sensing performance is significantly degraded as the primary users status changes with randomly arriving or leaving signals in a low Signal-to-Noise (SNR) environment in cognitive radio networks. This paper presents a new weight-p Energy Detector (ED) to improve the detection performance in these situations. To reduce the implementation complexities and save energy consumption, the optimum weight operation of weight-p ED is modeled as a Minimum Sampling Time (MST) optimization problem, and the optimum and sub-optimal weights are obtained through our analyses. Numerical simulation results show that the proposed weight-p ED offers better detection performance with a reduced probability of false alarms, and compresses sensing time compared with conventional ED for the same detection performance.

Improved Energy Detector with Weights for Primary User Status Changes in Cognitive Radio Networks

This paper presents an improved energy detector (ED) with weights to improve detection performance in this situation that the primary user's status changes as arriving or leaving randomly in cognitive radio networks. The idea is derived from the concept of unequal scale sampling such that the instantaneous energy statistic of the sampling points in the sensing period is endowed with monotonic weights. The probabilities of false alarm and detection for our ED are deduced under the new detection model. Numerical simulation results show that the proposed ED offers better detection performances with a reduced probability of false alarm compared to conventional ED, and improved energy detector can improve the overall probabilities of detection, when spectrum sensing is performed for a network with high traffic.

Performance Analysis of Time Reversal UWB Communication with Non-coherent Energy Detector

Non-coherent receivers, such as energy detectors (ED), are the simplest and the most practical alternatives to coherent receivers for low-rate and low-complexity applications in ultra-wideband (UWB) systems. However, these advantages are achieved at the expense of non-negligible performance degradation. One solution to improve the performance is to make use of time reversal (TR) technique. In this study, the performance of TR technique with non-coherent ED is analyzed in UWB systems. First, we derive an approximate analytical formula for the error probability of TR-ED which is based on tapped-delay line (TDL) channel model. Next, we theoretically and by simulations analyze the optimum integration interval which maximizes the performance of TR-ED. The results show that TR technique, by reducing the integration interval, considerably improves the performance compared to the conventional ED scheme.

A Spectrum Sensing Scheme for Partially Polarized Waves over α-μ Generalized Gamma Fading Channels

Schemes for spectrum holes sensing for cognitive radio based on the estimation of the Stokes parameters of monochromatic and quasimonochromatic polarized electromagnetic waves are developed. Statistical information that includes the variations of the polarization state in both cases (present and absent) of Primary User (PU) is accounted for. A detector based on the fluctuation of the Stokes parameters is analyzed, and its performance is compared with that of energy detectors, which use only the scalar amplitude information to sense the PU signal. The cooperative spectrum sensing based on the polarization in which the reporting channels are noisy will be investigated. The cluster technique is proposed to reduce the bit error probability due to channel impairment. A closed-form expression for the polarization detection is derived using α-μ generalized fading model, which provides directly an expression for the special cases of Nakagami-m and Weibull models as well as their derivatives. These expressions are verified using simulation. The results show that the polarization spectrum sensing gives superior performance for a wide range of SNR over the conventional energy detection method.

Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users

Cognitive radio (CR) signaling imposes some threats to the network. One of these common threats is commonly referred to as primary user emulation attack, where some malicious users try to mimic the primary signal and deceive secondary users to prevent them from accessing vacant frequency bands. In this paper, we introduce a smart primary user emulation attacker (PUEA) that sends fake signals similar to the primary signal. We assume a smart attacker, in the sense that it is aware of its radio environment and may choose different transmission strategies and then, we compare it to an always present attacker. In the proposed smart attacker strategy, the occurrence of fake signal is adjusted according to the primary user activity. First, we investigate the received signal at the CR users under such attackers. Then, we formulate and derive cooperative spectrum sensing (CSS) rules for a cognitive network operating in the presence of a PUEA and propose a new spectrum sensing scheme based on energy detection. Simulation results show that our proposed method can mitigate the destructive effect of PUEA in spectrum sensing, compared to conventional energy detection spectrum sensing.

Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise

In this paper, we address the problem of spectrum sensing in the presence of non-Gaussian noise for cognitive radio networks. A novel Rao test based detector, which does not require any a priori knowledge about the primary user (PU) signal and channels, is proposed for the detection of a primary user in non-Gaussian noises that are molded by the generalized Gaussian distribution (GGD). The statistic of the proposed Rao detector is derived and its detection performance is analyzed in the low signal-to-noise ratio regime and compared to that of the traditional energy detection. Furthermore, the Rao-based detection is extended to a multi-user cooperative framework by using the “k-out-of-M” decision fusion rule and considering erroneous reporting channels between the secondary users and the fusion center due to Rayleigh fading. The global cooperative detection and false alarm probabilities are derived based on the cooperative sensing scheme. Analytical and computer simulation results show that for a given probability of false alarm, the Rao detector can significantly enhance the spectrum sensing performance over the conventional energy detection and the polarity-coincidence-array (PCA) method in non-Gaussian noises. Furthermore, the proposed cooperative detection scheme has a significantly higher global probability of detection than the non-cooperative scheme.

A real-time, low-power implementation for high-resolution eigenvalue-based spectrum sensing

In this paper, a novel multiple antenna, high-resolution eigenvalue-based spectrum sensing algorithm based on the FFT of the received signal is introduced. The proposed platform overcomes the SNR wall problem in the conventional energy detection (ED) algorithm, enabling the detection of the weak signals at ?10 dB SNR. Moreover, the utilization of FFT for the input signal channelization provides a simple, low-power design for a high-resolution spectrum sensing regime. A real-time, low-area, and low-power VLSI architecture is also developed for the algorithm, which is implemented in a 0.18 μm CMOS technology. The implemented design is the first eigenvalue-based detection (EBD) architecture proposed to-date capable of detecting weak signals at ?10 dB. Despite having more algorithmic complexity in comparison to the ED, the proposed EBD architecture shows no significant increase in the core area and the power consumption, due to the FFT utilization for the input signal channelization. The proposed design occupies a total area of 3.4 mm2 and dissipates 78 mW for a 40 MHz sensing bandwidth consisting of 32 sub-channels.

Cooperative Spectrum Sensing with Adaptive Double-Threshold Based Energy Detector in Cognitive Radio Networks

Spectrum sensing is one of the key functionalities in the implementation of cognitive radio. It is used to sense the unused spectrum in an opportunistic manner. In this paper, we propose an energy detector with adaptive double-threshold for spectrum sensing, to optimize the detection performance at a fixed probability of false alarm $$(\text{ P }_\mathrm{f})$$ ( P f ) i.e. 0.1, which also overcomes sensing failure problem. In the present work, the detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of cognitive radio (CR) user. Simulation results show that proposed scheme optimizes better detection performance and outperforms both conventional energy detector and cooperative spectrum sensing (CSS) method by 12.8 and 3.3 % at $$-$$ ? 8 dB signal to noise ratio (SNR), respectively. While utilizing CSS with proposed adaptive double-threshold scheme, where each CR user use a double threshold detectors for local detection and send detection decisions to fusion center (FC) to give the final decision based on hard decision rule. It is further found that CSS with adaptive double-threshold improves detection performance around 26.8 and 7.6 % as compare to CSS with single threshold and Hierarchical with quantization method at $$-$$ ? 10 dB SNR, respectively, under the case when a small number of sensing nodes are used in spectrum sensing.

Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks

In cognitive radio networks, spectrum sensing is used to sense the unused spectrum in an opportunistic manner. In this paper, we propose an energy detector utilizing adaptive double threshold (ED_ADT) for spectrum sensing, which improves detection performance as well as overcomes sensing failure problem. The detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of secondary user. Numerical results show that proposed ED_ADT scheme outperforms conventional energy detector by 12.8% at –8 dB signal-to-noise ratio (SNR) in terms of probability of detection alarm (P d). While utilizing cooperative spectrum sensing (CSS) with ED_ADT scheme, it is further found that adaptive double threshold improves detection performance around 26.8% and 7.6% as compared to CSS with single threshold and hierarchical with quantization method at –10 dB SNR, respectively, under the case when a small number of sensing nodes are used in spectrum sensing.

Detection of Temporally Correlated Signals over Multipath Fading Channels

An optimal detection method along with two reduced-complexity methods, modified energy detection (MED) and equal gain detection (EGD), under low signal-to-noise ratio (SNR) condition are proposed in this paper for detection of temporally correlated signals over multipath fading channels. By incorporating resolvable multipaths and multiple antennas into system model, these detection methods are derived based on maximum log-likelihood ratio (LLR) test principal and using the same low SNR LLR approximation. Analytical performance expressions for MED and EGD are also given. Simulation results show that, when signal exhibits temporal correlation, the proposed optimal detection and EGD achieve better performance than conventional generalized likelihood ratio test through utilizing multipath propagation. Further, the proposed MED is superior to conventional energy detection if it a priori signal temporal correlation information is exploited. It is also revealed that multipath tap correlation can have either constructive or destructive effect to spectrum sensing. The proposed EGD is proven to be a practical technique for reliable spectrum sensing over multipath fading channels as it approaches optimal performance with low complexity.

Chaotic Stochastic Resonance Energy Detection Fusion Used in Cooperative Spectrum Sensing

A cooperative spectrum-sensing technique in cognitive radio (CR) networks based on the data fusion of various chaotic stochastic resonance (CSR) energy detectors is proposed in this paper. Due to noisy uncertainty in unpredictable wireless communication environments, detection performance of conventional energy detection cannot be guaranteed. By introducing the CSR system with various CSR noise types, and with the data fusion on these CSR-based energy detectors, the detection probability can be improved under the same constant false-alarm rate (CFAR), particularly under low signal-to-noise ratio (SNR) circumstances. Theoretical analyses and computer simulation results verify the effectiveness of the proposed novel cooperative spectrum-sensing approach under serious wireless conditions.