Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

Adaptive double-threshold energy detection algorithm for cognitive radio

Due to the fact that the conventional spectrum sensing algorithm is susceptible to noise, an adaptive double-threshold energy detection algorithm for a cognitive radio is proposed. Based on double-threshold energy detection, the algorithm can adaptively switch between one-round sensing and two-round sensing by comparing the observations with the pre-fixed thresholds. Mathematical expressions for the probability of detection, the probability of false alarm, and the sensing time are derived. The relationships including signal to noise ratio （SNR） vs. the probability of detection and SNR vs. the sensing time are plotted using Monte Carlo simulation and the algorithm is verified in a real cognitive system based on GNU Radio and universal software radio peripheral （USRP）. Simulation and experimental results show that, compared with the existing spectrum sensing method, the proposed algorithm can achieve a higher probability of detection within a reasonable sensing time.

Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal

As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.

Determination of Threshold for Energy Detection in Cognitive Radio Sensor Networks

The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.

Cyclostationary Feature Detection Based Spectrum Sensing Algorithm under Complicated Electromagnetic Environment in Cognitive Radio Networks

This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

Receiver Detection Employing Semaphore in Cognitive Radio

Interference from secondary users to primary users should be avoided in cognitive radio. However, it is difficult to solve the interference problem if the secondary users cannot get the information of the primary receivers. Insufficient information of primary users would result in inaccurate spectrum detecting result. To deliver the information of primary users, Receiver Detection Employing Semaphore (ReDES) is proposed in this paper. Primary receiver informs secondary users of its licensed receive frequency according to semaphore architecture directly in ReDES. The semaphore is used to determine the spectrum holes by secondary users. Frequency mapping method is come up with as a realization of ReDES. The procedure and the detailed techniques are illustrated to make ReDES scheme reasonable and feasible. Simulation results show that the proposed scheme can effectively detect the frequencies of primary receivers, and improve the accuracy of spectrum detection.

Research on cooperative detection and multiple access for cognitive radio system

As cognitive radio (CR) needs to detect spectrum accurately and utilize the idle spectrum efficiently,a new model of CR is proposed.This model adopts energy detection with auto-adapted threshold and produces spectrum mark vector.The basis function is obtained through making IDFT to the inner product of spectrum and phase vectors,and it is variable with the spectrum states.Data are sent through modulation on the basis function,and it can be estimated by correlation in the receiving end.The cooperative detection and multiple access of this model are also researched in this paper,and the performance is finally analyzed.Simulations show the cooperative detection of this model can obtain higher detection probability,and the disaccord of the basis functions between sending and receiving ends can lower its performance.

Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks

A most promising solution to the expansion of spectrum efficiency is cognitive radio(CR)and this expansion is achieved by permitting the licensed frequency bands to be accessed by unlicensed secondary users(SUs)with a lack of interference with licensed primary users(PUs).This utilization of CR networks in the spectrum sensing causes vulnerable attacks like primary user emulation(PUE)attack and here PUs play the role of malicious user and do not permit other users to utilize PUs channel even in their unavailability.On the basis of the traditional single-threshold energy detection algorithm,a novel modified double-threshold energy detector is formulated in the CR network and the detection probability,miss detection probability,probability of false alarm,and their inter-relationship are analyzed.This paper develops a modified double threshold energy detection cooperative spectrum sensing technique to alleviate the PUE attack.Finally,performance-based evaluation is carried out between the proposed and the existing energy detection spectrum sensing method that had no consideration on PUE attack.The resultant of the simulation in MATLAB has revealed that the proposed model has significantly mitigated PUE attack by means of providing outstanding performance.

Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection

Usefulness of sensor network applications in human life is increasing day by day and the concept of wireless connection promises new application areas. Sensor network can be very beneficial in saving human life from terrorist attacks causing explosion in certain areas leading to casualties. But realization of the sensor network application in explosive detection requires high scalability of the sensor network and fast transmission of the information through real time monitoring and control. In this paper a novel mechanism for explosive trace detection in any populated area by the use of mobile telephony has been described. The aim is to create a system that will assure common men, local population and above all the nation a secured environment, without disturbing their freedom of movement. It would further help the police in detection of explosives more quickly, isolation of suicide bombers, remediation of explosives manufacturing sites, and forensic and criminal investigation. To achieve this, the paper has projected an idea that can combine the strength of the mobile phones, the polymer sensor and existing cellular network. The idea is to design and embed a tiny cog-nitive radio sensor node into the mobile phone that adapts to the changing environment by analyzing the RF surroundings and adjusting the spectrum use appropriately. The system would be capable of detecting explo-sives within a defined territory. It would communicate the location of the detected explosives to the respec-tive service provider, which in turn would inform the law and enforcement agency or Police.

Analysis and Design of an Obstacle Detection Radar Transceiver for ISM Band

Authors designed an obstacle radar transceiver for ISM band. This work is focused on rectangular microstrip transceiver integrated with innovative metamaterial structure at a height of 3.276 mm from the ground plane. Two rectangular microstrip transceiver is designed for transmitting and receiving purpose. This work is mainly focused on increasing the potential parameters of rectangular microstrip transceiver. RMT along with the proposed innovative metamaterial structure is designed to resonate at 2.259 GHz. Simulation results showed that the impedance bandwidth of the RMT is improved by 575%, return loss is reduced by 391% and efficiency is improved by 28% by incorporating the proposed innovative metamaterial structure. For verifying that the proposed innovative metamaterial structure possesses negative values of Permeability and Permittivity within the operating frequency range, Nicolson-Ross-Weir method (NRW) has been employed. An op-amp and comparator is used to compare the return loss of transmitting and receiving RMT. An indicator is used to indicate difference of return loss and power of transmitting and receiving rectangular microstrip transceiver. For all simulation purpose, computer simulation technology-microwave studio (CST-MWS) software has been used.

Cooperative detection algorithm of spectrum holes in cognitive radio

To improve the detection performance of sensing users for primary users in the cognitive radio, an optimal cooperative detection algorithm for many sensing users is proposed. In this paper, optimal decision thresholds of each sensing user are discussed. Theoretical analysis and simulation results indicate that the detection probability of optimal decision threshold rules is better than that of determined threshold rules when the false alarm of the fusion center is constant. The proposed optimal cooperative detection algorithm improves the detection performance of primary users as the attendees grow. The 2 dB gain of detection probability can be obtained when a new sensing user joins in, and there is a 17 dB improvement when the accumulation number increases from 1 to 50.

HI detection of J030417.78+002827.4 by the Five-hundred-meter Aperture Spherical Radio Telescope

We present the first HI 21 cm spectroscopy detection of J030417.78+002827.4,which is an active galactic nucleus(AGN)with an intermediate-mass black hole(IMBH)in the center.The observations were carried out with the Five-hundred-meter Aperture Spherical Radio Telescope(FAST)last year.We relied on the ON-OFF observing approach with the 19-beam receiver covering 1.05-1.45 GHz.Within a total integration time of about 20 min,the root mean square(RMS)of our data reaches 1.2 mJy beam-1,at a velocity resolution of 1.6 km s^(-1).Radio frequency interference(RFI)is checked and excluded during the data analysis.The detected HI spectroscopy shows a dual-horned profile with a line width of 223.5 km s^(-1),indicating gas rotation around this AGN.The redshift of this galaxy derived from our HI observation is0.0447.We calculate the atomic gas mass by the integrated flux of the HI emission line.The total gas mass in this galaxy is estimated to be 1.8×10^(10)M☉.We find the fraction of gas-to-stellar mass ratio in J030417.78+002827.4 is more than 50%.This ratio is much higher than the typical value found in other AGNs with supermassive black holes(SMBHs),and is comparable to some star-forming galaxies recently observed by FAST.

Detection of solar radio burst intensity based on a mod ified multifactor SVM algorithm

To realize the automatic detection of solar radio burst(SRB)intensity,detection based on a modified multifactor support vector machine(SVM)algorithm is proposed.First,the influence of SRB on global navigation satellite system(GNSS)signals is analyzed.Feature vectors,which can reflect the SRB intensity of stations,are also extracted.SRB intensity is classified according to the solar radio flux,and different class labels correspond to different SRB intensity types.The training samples are composed of feature vectors and their corresponding class labels.Second,training samples are input into SVM classifiers to one-against-one training to obtain the optimal classification models.Finally,the optimal classification model is synthesized into a modified multifactor SVM classifier,which is used to automatically detect the SRB intensity of new data.Experimental results indicate that for historical SRB events,the average accuracy of SRB intensity detection is greater than 90%when the solar incident angle is higher than 20°.Compared with other methods,the proposed method considers many factors with higher accuracy and does not rely on radio telescopes,thereby saving cost.

Symbiotic Radio Systems:Detection and Performance Analysis

Symbiotic radio(SR)is an emerging green technology for the Internet of Things(IoT).One key challenge of the SR systems is to design efficient and low-complexity detectors,which is the focus of this paper.We first drive the mathematical expression of the optimal maximum-likelihood(ML)detector,and then propose a suboptimal iterative detector with low complexity.Finally,we show through numerical results that our proposed detector can obtain near-optimal bit error rate(BER)performance at a low computational cost.

Intrusion Detection System with Remote Signalling for Vehicles Using an Arduino Controller and Radio-Frequency Technology

Malicious activities or policy violations have been a concern for the past years. For example, many people have been victims of robbery on vehicles. A conceptual diagram of an Intrusion Detection System (IDS) [1] [2] for vehicles with remote signaling using an Arduino controller and radio-frequency technology is proposed in this paper. To address malicious activities on vehicles, two aspects are considered here, namely: notifier and detector. Firstly, an object-oriented C module that puts on and off a controller (installed inside the vehicle) and an anti-theft electronic editing that powered using an alternator and supported by a back-up battery are implemented. Secondly, a magnetic intrusion sensor, controlled by a proximity detector using radio-frequency technology, has been installed on each vehicle door. To enable IDS, a user needs to activate the monitoring system when leaving their vehicle. This is done using a remote system. In case the user does not activate the monitoring system while leaving the vehicle, a 5-meter-proximity detector will automatically lock the system and set off the monitoring system whenever the user is outside the detection zone. The detection zone is a 5-meter radius area centered at the controller. Here, monitoring consists of geolocating any intruders within the detection zone. This means, if any of the vehicle doors is opened while the system is still locked, the controller will activate the vehicle alarm for a few seconds, thereafter send an SMS notification to the owner. The system automatically unlocks as soon as the proximity detector is within the detection zone. The contribution of this paper, as compared to other similar work, is to reinforce the electronic implementation of IDS.

Simultaneous detection of CH_(4)and CO_(2)through dual modulation off-axis integrated cavity output spectroscopy

This study established a novel method for the simultaneous detection of two-component gases.Radio frequency(RF)white noise disturbance laser current and wavelength modulation were simultaneously used to improve the off-axis integrated cavity output spectroscopy technique,and a high-precision dual modulation OA-ICOS(RF-WM-OA-ICOS)system was established.The two laser beams were coupled into one laser beam that was applied incident to the cavity of RF-WM-OA-ICOS system.The second harmonic signals of CH_(4)and CO_(2)gas simultaneously appeared in the rising or falling edge of a triangular wave.This method was used to measure CH_(4)and CO_(2)with different concentrations.The results indicated that the proposed system has high stability and can accurately and simultaneously measure the concentrations of CH_(4)and CO_(2),with an optimal integration time of 220 s.The minimum detection limit was 10 ppb for CH_(4)and 1.5 ppm for CO_(2).The corresponding noise equivalent absorption sensitivity values were calculated as 2.67×10^(-13)cm^(-1)·Hz^(-1/2)and 5.18×10^(-11)cm^(-1)·Hz^(-1/2),respectively.The proposed dual-component gas simultaneous detection method can also be used for high-precision simultaneous detection of other gases.Therefore,this study may serve as a reference for developing portable multicomponent gas analyzers.

RFI Detection for Multichannel HRWS SAR System Based on Spatial Cross Correlation

Multichannel high-resolution and wide-swath(HRWS)imaging is an advanced digital beamforming technique for future synthetic aperture radar(SAR)systems.However,radio frequency interference(RFI)is a critical concern for HRWS SAR missions,which distorts measure-ments and produces image artifacts.In this paper,the spatial cross-correlation coefficients of multichannel HRWS SAR signals are investigated for RFI detection.It is found when the two channels are correlated,RFI-polluted areas present lower coherence values than non-polluted areas in the same scenarios,which makes previous methods fail.Further,this paper studies the case of two fully decorrelated channels to maximize the coherence difference among RFI and target echoes,and RFI detection is realized by exploiting the anomaly value of coherence.Experimental results of real air-borne multichannel SAR data demonstrate that the RFI can be detected successfully.