Energy Detection Method Research Articles

Cognitive (LabVIEW) on FPGA–a novel approach for dynamic spectrum sensing

The spectrum allotted for communications has been distributed to various service providers, who maintain and manage their portion of the spectrum by accommodating as many users as possible through spectral management techniques. Static spectrum management has been replaced by dynamic spectrum management to meet the vastly increasing number of users. Although dynamic spectrum management techniques have enabled the efficient assignment of channels to the user, it has been statistically observed that a huge part of the communications spectrum is scarcely used at all times. Cognitive radio is a useful tool for efficient dynamic spectrum management. The cognitive radio is adaptive in nature and is most effective because of its ability to monitor the spectrum, sense the channel occupancy and mobilise the secondary user to an unused primary user channel, as well as to relocate the secondary user if the primary user wishes to communicate. In this work, the energy-detection method and the cyclostationary method, which are the methods to sense the spectrum, are analysed and the results are compared. Simulation is done using a LabVIEW tool (National Instruments, Bangalore, India) and finally the implementation is done on a Field Programmable Gate Array.

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.

Reconstruction method for pulse position modulation‐ultra wideband communication signal based on compressed sensing

Pulse position modulation-ultra wideband (PPM-UWB) communication adopts ultra narrow pulse as the transmitted signal. Owing to the low-power spectral density and ultra wide bandwidth, it is difficult to detect and sample PPM-UWB signal directly. There are already some researches on using compressed sensing (CS) theory for UWB communication with lower sampling speed. However, these methods take the sparseness of pulse position or transmission channel into account separately and they are unfit for practical communication. To solve these problems, a dual-sparse reconstruction method is proposed in this paper to process PPM-UWB communication signal based on CS theory. Proposed method designs the target signal which needed to be reconstructed as a dual-sparse vector. This vector combines the sparseness of PPM pulse position and UWB channel multi-paths simultaneously, hence it has dual sparseness. The information code can be demodulated from reconstructed dual-sparse vector directly by using energy detection method. Extensive numerical simulations demonstrate the validity and applicability of proposed method.

On Signal Detection in the Presence of Weakly Correlated Noise over Fading Channels

In cognitive radio networks, in order to avoid interference form secondary users to the primary license holders of the spectrum, reliable spectrum sensing is necessary. In scenarios where the noise samples are correlated, the spectrum sensing methods optimized considering impairment by independent noise samples will not provide optimum performances. To address this issue, a locally optimum detection method for random signals under a weakly correlated noise model over fading channels is proposed. The probabilities of false alarm and detection of the proposed detector in the low signal to noise ratio regime are analyzed. The average probabilities are calculated over different channel gains. Numerical and simulation results demonstrate the superiority of the proposed method over the known energy detection method with comparable complexities. Furthermore, we consider the scenario where the estimated correlation is different from the real correlation and investigate the effect of this correlation mismatch on the probabilities of false alarm and detection of the proposed method.

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.

A Novel Blind Event Detection Method for Wireless Sensor Networks

Student'st-distribution is utilized to derive a novel method for event detection in wireless sensor networks. Numerical analysis is used to show that under the same conditions, the proposed event detection method is comparable to likelihood ratio-based detection method and that it significantly outperforms energy detection method in terms of detection performance. Moreover, the proposed method does not require perfect knowledge of noise variance to set up a decision threshold in terms of a false alarm probability as the likelihood ratio based detection and the energy detection do.

On Hybrid Overlay–Underlay Dynamic Spectrum Access: Double-Threshold Energy Detection and Markov Model

In this correspondence, we propose a hybrid strategy that combines overlay and underlay dynamic spectrum access (DSA) schemes. Utilizing a double-threshold energy detection method, unlicensed or secondary users (SUs) can switch between full- and partial-access modes dynamically. A Markov chain model is developed to derive performance metrics for evaluating the proposed strategy. Numerical results show that the proposed strategy can greatly improve the system interfering probability performance. In addition, SUs can adjust the access probability of partial-access mode to tradeoff between throughput and interfering probability performance.

Application‐specific spectrum sensing method for cognitive sensor networks

The authors address an important aspect of spectrum sensing that has been often overlooked in the cognitive radio (CR) research. Although CR is supposed to be aware of its surrounding, most existing articles do not consider the characteristics of secondary users in the optimisation of sensing period. In this study, based on a continuous-time Markov chain model for cognitive sensor networks and energy detection method, the authors propose an application-specific spectrum sensing method that obtains the optimal sensing period according to the characteristics of both ‘primary and secondary’ users (hybrid scheme). The authors define and analytically derive two parameters, the interference ratio and the undetected opportunity ratio, and analytically find the optimum sensing period. Numerical and simulation results indicate that our proposed method is able to provide an optimal sensing period, that is customised for different cognitive networks. The proposed method significantly increases the system throughput by up to 11% and reduces the network's power consumption by as low as 33%. Finally, the trade-off between the throughput maximisation and power consumption minimisation is discussed.

Performance evaluation on correlation detection and energy detection for ultra wideband‐impulse radio communication with multi‐pulse position modulation scheme in implant body area networks

Ultra wideband-impulse radio (UWB-IR) transmission, which can provide high data rate for real-time transmission and low-power consumption in transceivers, is one of promising transmission technologies in implant body area networks (BANs). Some studies on UWB-IR propagation characteristics in implant BANs have reported that UWB-IR signals suffer from large attenuation in human body channels. It is therefore necessary to clarify the actual performance of UWB-IR modulation and demodulation methods in implant BANs. In this study, the authors focus on experimental evaluation of the correlation detection and energy detection for UWB-IR transmission with multi-pulse position modulation (MPPM). For this purpose, the authors develop a UWB-IR communication system with MPPM scheme, and experimentally evaluate the transmission performance of the developed systems with the two detection methods. In addition to the experimental evaluation, the authors also theoretically analyse the bit-error rate (BER) performance of the correlation detection and energy detection methods by using Gaussian approximation. From the experimental results, the developed system has achieved a BER of 10 -2 at the propagation loss of 75 dB with a data rate of 2 Mbps in the correlation detection. This result shows the feasibility of reliable UWB-IR communication in actual implant BANs.

인지무선네트워크를 위한 고유값 및 중첩기반의 협력 스펙트럼 센싱 기법

Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum sensing. In addition, Eigenvalue-based spectrum sensing has also drawn a great attention due to its performance improvement over the energy detection method in which the more smoothing factor, the better performance is achieved. However, the more smoothing factor in Eignevalue-based spectrum sensing requires the more sensing time. Furthermore, more reporting time in cooperative sensing will be required as the number of nodes increases. Subsequently, we in this paper propose an Eigenvalue and superposition-based spectrum sensing where the reporting time is utilized so as to increase the number of smoothing factors for autocorrelation calculation. Simulation result demonstrates that the proposed scheme has better detection probability in both local as well as global detection while requiring less sensing time as compared with conventional Eigenvalue-based detection scheme.

Cooperative wideband sensing based on cyclostationary features with multiple malicious user elimination

Spectrum sensing is an essential concept in cognitive radio. To overcome the single node sensing issue that arises due to channel impediments, cooperative/multinode sensing is being used. Although cooperation among multiple cognitive users enhances the sensing performance, presence of few malicious cognitive users may severely degrade the efficiency of the system. In this paper, generalized extreme studentized deviate (GESD) and adjusted box-plot (ABP) methods are introduced to increase the sensing reliability of cooperative network by eliminating multiple malicious cognitive users. The performance of the cyclostationary feature detection method is compared with the energy detection method under different channel impediments. The simulation results are carried out with false alarm probability of 0.01 and a detection probability of 0.9. The simulation results reveal that there is a significant improvement in cooperative sensing performance by elimination of multiple malicious user in the network.

Secondary Terrestrial Use of Broadcasting Satellite Services below 3 GHz

Secondary use of the satellite spectrum by a terrestrial system is studied in this paper, focusing on broadcasting satellite services. Both spectrum sensing based access and database based access are discussed. Link budget analysis is used to define operational limits for spectrum sensing and transmission power control when the primary system is a digital video broadcasting – satellite services to handheld devices (DVB-SH) system. The results show that cognitive radio techniques should be applied with caution in satellite bands. The energy detection method does not support well spectrum sharing in the studied band. Rather the sensing should be based on the feature detection or matched filter detection. The results show that only short-range transmission can be used on a secondary basis in many environments when the secondary spectrum use is based on the sensing.

Reducing the Sampling Complexity of Energy Detection in Cognitive Radio Networks under Low SNR by Using the Optimal Stochastic Resonance Technique

We propose a novel noncooperative technique in cognitive radio (CR) networks, which is based on the optimal stochastic resonance (SR) technique. By introducing the dynamic system approach of SR into the noncooperative spectrum sensing process, the defect of high sampling complexity of traditional energy detector can be reduced efficiently and thus can guarantee the applicability of the optimal SR-based energy detection method. The optimization of the signal-to-noise ratio (SNR) improvement of the system ensures the lowest sampling complexity needed to reach certain performance requirement. Computer simulations show that it can reduce the sampling complexity compared with traditional energy detector used in IEEE 802.22 draft especially under low SNR environments. It can certainly be extended to other wide application areas.

The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral

Based on the inherent capability of automatic modulation classification (AMC), a new spectrum sensing method is proposed in this paper that can detect all forms of primary users’ signals in a cognitive radio environment. The study presented in this paper focuses on the sensing of some combined analog and digitally primary modulated signals. In achieving this objective, a combined analog and digital automatic modulation classifier was developed using an artificial neural network (ANN). The ANN classifier was combined with a GNU Radio and Universal Software Radio Peripheral version 2 (USRP2) to develop the Cognitive Radio Engine (CRE) for detecting primary users’ signals in a cognitive radio environment. The detailed information on the development and performance of the CRE are presented in this paper. The performance evaluation of the developed CRE shows that the engine can reliably detect all the primary modulated signals considered. Comparative performance evaluation carried out on the detection method presented in this paper shows that the proposed detection method performs favorably against the energy detection method currently acclaimed the best detection method. The study results reveal that a single detection method that can reliably detect all forms of primary radio signals in a cognitive radio environment, can only be developed if a feature common to all radio signals is used in its development rather than using features that are peculiar to certain signal types only.

Comments and Corrections Comments on "Spectrum Sensing in Cognitive Radio Using Goodness-of-Fit Testing"

In this paper, we verify goodness-of-fit testing through the use of the Anderson-Darling (AD) test [1] for spectrum sensing in cognitive radio. In [1], it was shown that spectrum sensing based on the AD test outperforms the energy detection method. However, this positive result can only be obtained when the primary signal is assumed to be static during sensing interval, which is a very rare case in cognitive radio. This assumption reduces the generality of the proposed test in [1]. The verification results of the AD test with some more general and practical primary signals in this paper show that the application of the AD sensing scheme for spectrum sensing in cognitive radio is still a challenge and requires further research.

Automated entropy-based bird phrase segmentation on sparse representation classifier

An automated system capable of reliably segmenting and classifying bird phrases would help analyze field recordings. Here we describe a phrase segmentation method using entropy-based change-point detection. Spectrograms of bird calls are often very sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time- frequency window on the spectrogram, the entropy dips when detecting a signal and rises back up when the signal ends. Rather than a simple threshold on the entropy to determine the beginning and end of a signal, a Bayesian recursion-based change-point detection(CPD) method is used to detect sudden changes in the entropy sequence. CPD reacts only to those statistical changes, so generates more accurate time labels and reduces the false alarm rate than conventional energy detection methods. The segmented phrases are then used for training and testing a sparse representation(SR) classifier, which performs phrase classification by a sparse linear combination of feature vectors in the training set. With only 7 training tokens for each phrase, the SR classifier achieved 84.17% accuracy on a database containing 852 phrases from Cassins Vireo (Vireo casinii ) phrases that were hand-classified into 32 types. [This work was supported by NSF.]

Myoelectric activity detection during a Sit-to-Stand movement using threshold methods

Myoelectric signals recorded via surface electrodes contain rich muscle activity information that is beneficial for both clinical diagnosis and biomedical research. When synchronized with the kinematic data, these signals provide investigators an insight into muscle activation sequence, onset, levels, and periods. A primary difficulty with the analysis and interpretation of electromyography (EMG) signals lies in the inherent stochastic nature of the EMG process, which arises from its biological variability as well as noise added during the collection process. Various techniques for muscle onset and activity detection from the myoelectric signal have been proposed in the literature. Our focus in this study is myoelectric activity detection from EMG signals collected during Sit-to-Stand (STS) and Stand-to-Sit (STST) movements. We explore a previously established double threshold detection method, and compare its results with a novel detection scheme based on the energy of the signal. Accordingly, EMG signals from four lower extremity muscles, and synchronized kinematic data, were collected for 180 trials of STS and STST movements performed in the laboratory. Detection thresholds above baseline in the case of both algorithms were computed and analyzed using a 2 (detectors) × 4 (activity thresholds) repeated measures analysis of variance. Our statistical analysis revealed that the energy detection method performed similarly to the double threshold method, while both methods required a considerably higher threshold above baseline for detection.

인지무선 시스템을 위한 거리기반 가중결합을 이용한 협력 광대역 스펙트럼 센싱

In this paper, we analysis wideband spectrum sensing with distance based weight combining for Cognitive Radio (CR) systems. CR systems is implemented the spectrum of the Primary User(PU) by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of PU is BPSK signal and the wireless channel between a PU and CR systems is modeled as Gaussian channel. From the simulation results, the wideband sensing with distance based and Distance based weight Combing (DWC) methods shows higher spectrum sensing performance than single CR user spectrum sensing.

Non-coherent Power Decomposition-Based Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

As the fundamental component of dynamic spectrum access, implementing spectrum sensing is one of the most important goals in cognitive radio networks due to its key functions of protecting licensed primary users from harmful interference and identifying spectrum holes for the improvement of spectrum utilization. However, its performance is generally compromised by the interference from adjacent primary channels. To cope with such interference and improve detection performance, this paper proposes a non-coherent power decomposition-based energy detection method for cooperative spectrum sensing. Due to its use of power decomposition, interference cancellation can be applied in energy detection. The proposed power decomposition does not require any prior knowledge of the primary signals. The power detection with its interference cancellation can be implemented indirectly by solving a non-homogeneous linear equation set with a coefficient matrix that involves only the distances between primary transmitters and cognitive secondary users (SUs). The optimal number of SUs for sensing a single channel and the number of channels that can be sensed simultaneously are also derived. The simulation results show that the proposed method is able to cope with the expected interference variation and achieve higher probability of detection and lower probability of false alarm than the conventional method in both hard combining and soft combining scenarios.

A high accuracy femto-/picosecond laser damage test facility dedicated to the study of optical thin films

A laser damage test facility delivering pulses from 100 fs to 3 ps and designed to operate at 1030 nm is presented. The different details of its implementation and performances are given. The originality of this system relies the online damage detection system based on Nomarski microscopy and the use of a non-conventional energy detection method based on the utilization of a cooled CCD that offers the possibility to obtain the laser induced damage threshold (LIDT) with high accuracy. Applications of this instrument to study thin films under laser irradiation are presented. Particularly the deterministic behavior of the sub-picosecond damage is investigated in the case of fused silica and oxide films. It is demonstrated that the transition of 0-1 damage probability is very sharp and the LIDT is perfectly deterministic at few hundreds of femtoseconds. The damage process in dielectric materials being the results of electronic processes, specific information such as the material bandgap is needed for the interpretation of results and applications of scaling laws. A review of the different approaches for the estimation of the absorption gap of optical dielectric coatings is conducted and the results given by the different methods are compared and discussed. The LIDT and gap of several oxide materials are then measured with the presented instrument: Al(2)O(3), Nb(2)O(5), HfO(2), SiO(2), Ta(2)O(5), and ZrO(2). The obtained relation between the LIDT and gap at 1030 nm confirms the linear evolution of the threshold with the bandgap that exists at 800 nm, and our work expands the number of tested materials.