A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)line...A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)linearly frequency-modulated(LFM)signal ranging from 2.5 to 9.5 GHz,with an instantaneous bandwidth of 1 GHz.The IF LFM signal is converted to the optical domain via an intensity modulator and filtered by a fiber Bragg grating to generate two second-order sidebands.The two sidebands beat each other to generate a frequency-and-bandwidth-quadrupled LFM signal.By changing the center frequency of the IF LFM signal,the radar function can be operated within 8 to 40 GHz.One second-order sideband works in conjunction with the stimulated Brillouin scattering gain spectrum for microwave frequency measurement,providing an instantaneous measurement bandwidth of 2 GHz and a frequency measurement range from 0 to 40 GHz.The prototype is demonstrated to be capable of achieving a range resolution of 3.75 cm,a range error of less than ±2 cm,a radial velocity error within ±1 cm∕s,delivering clear imaging of multiple small targets,and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz.展开更多
The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail...The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.展开更多
Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spec...Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.展开更多
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 second...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.展开更多
With the development of wireless technologies,multifarious standards are currently used in the underground coal mine communication systems.In this paper,the coexistence of 802.15.4 based wireless senser networks （WSN...With the development of wireless technologies,multifarious standards are currently used in the underground coal mine communication systems.In this paper,the coexistence of 802.15.4 based wireless senser networks （WSNs） with other wireless networks using cognitive radio technique are discussed.Multiple sensor nodes are involved in the spectrum sensing to avoid the interference from other wireless users.The more the sensor nodes cooperate in the sensing,the better the detection performance can be obtained; however,more energy is consumed.How to get the tradeoff between energy efficiency and detection performance is a key problem.According to the requirements for detection,we first give the least required detection time of a single sensor node.Then,the voting fusion rule is adopted for the final decision making.Finally,the relationship between final detection performance and energy consumption is analyzed.展开更多
Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a r...Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.展开更多
In order to achieve higher spectrum efficiency in cognitive radio （CR） systems, a closed-form expression of the optimal decision threshold for soft decision cooperative spectrum sensing based on the minimum total er...In order to achieve higher spectrum efficiency in cognitive radio （CR） systems, a closed-form expression of the optimal decision threshold for soft decision cooperative spectrum sensing based on the minimum total error probability criterion is derived. With the analytical expression of the optimal decision threshold, the impact of different sensing parameters on the threshold value is studied. Theoretical analyses show that the optimal threshold achieves an efficient trade-off between the missed detection probability and the false alarm probability. Simulation results illustrate that the average signal-to-noise ratio （SNR） and the soft combination schemes have a great influence on the optimal threshold value, whereas the number of samples has a weak impact on the optimal threshold value. Furthermore, for the maximal ratio combing （MRC） and the modified deflection coefficient （MDC） schemes, the optimal decision threshold value increases and approaches a corresponding individual limit value while the number of CR users increases. But the number of CR users has a weak influence on the optimal decision threshold for the equal gain combining （EGC） scheme.展开更多
Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal pow...Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.展开更多
In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertain...In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.展开更多
With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of...With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).展开更多
The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the rob...The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.展开更多
Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately ...Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior.Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene.Thus,communication behavior recognition using raw sensing data under smallsample condition has become a new challenge.In this paper,a data enhanced communication behavior recognition(DECBR)scheme is proposed to meet this challenge.Firstly,a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme.Then,an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition.Moreover,DCGAN is applied to support data enhancement,which realize communication behavior recognition under small-sample condition.Finally,the scheme is verified by experiments under different data size.The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under smallsample condition.展开更多
Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can pro...Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.展开更多
In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorith...In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method.展开更多
Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overh...Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.展开更多
In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described ...In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described as a tradeoff between the throughput and the entirely consumed power. A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing secondary users(SU), transmission bandwidth and power. A combined optimization algorithm of bi-level optimization, Polyblock optimization and Dinkelbach's optimization is proposed to solve the proposed non-convex optimization problem effectively. The simulation results show that, compared with throughput maximization model(TMM), the energy efficiency maximization model(EEMM) improves EE of the CR system and limits the excessive power consumption effectively.展开更多
Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions. If the energy value falls into ...Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions. If the energy value falls into the corresponding region,it will be judged as "1",no information or "0". When the probability of false alarm is constrained to be constant,the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination(EGC) scheme. Moreover,compared with the traditional one bit hard combination,fewer average sensing bits are required to transmit to the data fusion center with the proposed method.展开更多
Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） ...Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.展开更多
In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditiona...In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.展开更多
The majority of existing papers about spectrum sensing have the assumption that secondary users(SUs) are stationary. However,mobility is an essential feature of mobile communications networks. In this paper,the detect...The majority of existing papers about spectrum sensing have the assumption that secondary users(SUs) are stationary. However,mobility is an essential feature of mobile communications networks. In this paper,the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics,i.e.,detection probability,miss detection probability and false alarm probability,were thoroughly investigated. In our analysis,a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover,the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability,but not on the false alarm probability.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62371191 and 62401207)the Space Optoelectronic Measurement and Perception Laboratory,Beijing Institute of Control Engineering(Grant No.LabSOMP-2023-05)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M764276)the Science and Technology Commission of Shanghai Municipality(Grant No.22DZ2229004).
文摘A microwave photonic prototype for concurrent radar detection and spectrum sensing is proposed.A direct digital synthesizer and an analog electronic circuit are integrated to generate an intermediate frequency(IF)linearly frequency-modulated(LFM)signal ranging from 2.5 to 9.5 GHz,with an instantaneous bandwidth of 1 GHz.The IF LFM signal is converted to the optical domain via an intensity modulator and filtered by a fiber Bragg grating to generate two second-order sidebands.The two sidebands beat each other to generate a frequency-and-bandwidth-quadrupled LFM signal.By changing the center frequency of the IF LFM signal,the radar function can be operated within 8 to 40 GHz.One second-order sideband works in conjunction with the stimulated Brillouin scattering gain spectrum for microwave frequency measurement,providing an instantaneous measurement bandwidth of 2 GHz and a frequency measurement range from 0 to 40 GHz.The prototype is demonstrated to be capable of achieving a range resolution of 3.75 cm,a range error of less than ±2 cm,a radial velocity error within ±1 cm∕s,delivering clear imaging of multiple small targets,and maintaining a frequency measurement error of less than ±7 MHz and a frequency resolution of better than 20 MHz.
文摘The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.
基金supported by the Key Projects of the 2022 National Defense Science and Technology Foundation Strengthening Plan 173 (Grant No.2022-173ZD-010)the Equipment PreResearch Foundation of The State Key Laboratory (Grant No.6142101200204)。
文摘Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.
文摘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.
基金Special Funds for Postdoctoral Innovative Projects of Shandong Province(No.201103099)
文摘With the development of wireless technologies,multifarious standards are currently used in the underground coal mine communication systems.In this paper,the coexistence of 802.15.4 based wireless senser networks （WSNs） with other wireless networks using cognitive radio technique are discussed.Multiple sensor nodes are involved in the spectrum sensing to avoid the interference from other wireless users.The more the sensor nodes cooperate in the sensing,the better the detection performance can be obtained; however,more energy is consumed.How to get the tradeoff between energy efficiency and detection performance is a key problem.According to the requirements for detection,we first give the least required detection time of a single sensor node.Then,the voting fusion rule is adopted for the final decision making.Finally,the relationship between final detection performance and energy consumption is analyzed.
文摘Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.
基金The National Natural Science Foundation of China(No.61271207,61372104)the National Science and Technology Major Project(No.2010ZX0300600201)the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province(No.BA2010023)
文摘In order to achieve higher spectrum efficiency in cognitive radio （CR） systems, a closed-form expression of the optimal decision threshold for soft decision cooperative spectrum sensing based on the minimum total error probability criterion is derived. With the analytical expression of the optimal decision threshold, the impact of different sensing parameters on the threshold value is studied. Theoretical analyses show that the optimal threshold achieves an efficient trade-off between the missed detection probability and the false alarm probability. Simulation results illustrate that the average signal-to-noise ratio （SNR） and the soft combination schemes have a great influence on the optimal threshold value, whereas the number of samples has a weak impact on the optimal threshold value. Furthermore, for the maximal ratio combing （MRC） and the modified deflection coefficient （MDC） schemes, the optimal decision threshold value increases and approaches a corresponding individual limit value while the number of CR users increases. But the number of CR users has a weak influence on the optimal decision threshold for the equal gain combining （EGC） scheme.
基金supported in part by National Natural Science Foundation of China under Grant No. 61871398in part by China Postdoctoral Science Foundation under Grant No. 2018M631122
文摘Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.
基金the National Science Foundation of China (No.91738201, 61971440)the Jiangsu Province Basic Research Project (No.BK20192002)+1 种基金the China Postdoctoral Science Foundation (No.2018M632347)the Natural Science Research of Higher Education Institutions of Jiangsu Province (No.18KJB510030)。
文摘In this paper,we investigate a spectrumsensing system in the presence of a satellite,where the satellite works as a sensing node.Considering the conventional energy detection method is sensitive to the noise uncertainty,thus,a temporal convolutional network(TCN)based spectrum-sensing method is designed to eliminate the effect of the noise uncertainty and improve the performance of spectrum sensing,relying on the offline training and the online detection stages.Specifically,in the offline training stage,spectrum data captured by the satellite is sent to the TCN deployed on the gateway for training purpose.Moreover,in the online detection stage,the well trained TCN is utilized to perform real-time spectrum sensing,which can upgrade spectrum-sensing performance by exploiting the temporal features.Additionally,simulation results demonstrate that the proposed method achieves a higher probability of detection than that of the conventional energy detection(ED),the convolutional neural network(CNN),and deep neural network(DNN).Furthermore,the proposed method outperforms the CNN and the DNN in terms of a lower computational complexity.
基金supported in part by the National Key Research and Development Program of China(2018YFB1802300)the Science and Technology Commission Foundation of Shanghai(Nos.21511101400 and 22511100600)+2 种基金the Young Elite Scientists Sponsorship Program by CICthe Program of Shanghai Academic/Technology Research Leader(No.21XD1433700)the Shanghai Rising-Star Program(No.21QC1400800)。
文摘With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).
基金the National Nat-ural Science Foundation of China under Grant No.62072406,No.U19B2016,No.U20B2038 and No.61871398the Natural Science Foundation of Zhejiang Province under Grant No.LY19F020025the Major Special Funding for“Science and Tech-nology Innovation 2025”in Ningbo under Grant No.2018B10063.
文摘The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.
基金supported by the National Natural Science Foundation of China(No.61971439 and No.61702543)the Natural Science Foundation of the Jiangsu Province of China(No.BK20191329)+1 种基金the China Postdoctoral Science Foundation Project(No.2019T120987)the Startup Foundation for Introducing Talent of NUIST(No.2020r100).
文摘Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior.Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene.Thus,communication behavior recognition using raw sensing data under smallsample condition has become a new challenge.In this paper,a data enhanced communication behavior recognition(DECBR)scheme is proposed to meet this challenge.Firstly,a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme.Then,an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition.Moreover,DCGAN is applied to support data enhancement,which realize communication behavior recognition under small-sample condition.Finally,the scheme is verified by experiments under different data size.The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under smallsample condition.
基金supported by the Foundation of Graduate Innovation Center in NUAA under Grant No. kfjj20190414the open research fund of Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Industry and Information Technology, Nanjing, 211106, China (No. KF20181913)+2 种基金National Natural Science Foundation of China (No. 61631020, No. 61871398, No. 61931011 and No. 61801216)the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Natural Science Foundation of Jiangsu Province (No. BK20180420)
文摘Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.
基金Projects(61362018,61861019)supported by the National Natural Science Foundation of ChinaProject(1402041B)supported by the Jiangsu Province Postdoctoral Scientific Research Project,China+1 种基金Project(16A174)supported by the Scientific Research Fund of Hunan Provincial Education Department,ChinaProject([2016]283)supported by the Research Study and Innovative Experiment Project of College Students,China
文摘In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method.
基金ACKNOWLEDGEMENT This work was partially supported by the Na- tional Natural Science Foundation of China under Grant No. 61071127 and the Science and Technology Department of Zhejiang Pro- vince under Grants No. 2012C01036-1, No. 2011R10035.
文摘Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.
基金supported by the National Natural Science Foundations of China under Grant Nos. 61301105, 61401288 and 61601221the Natural Science Foundations of Jiangsu Province under Grant No. BK20140828+1 种基金the China Postdoctoral Science Foundations under Grant Nos. 2015M581791 and 2015M580425the Fundamental Research Funds for the Central Universities under Grant No. DUT16RC(3)045
文摘In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described as a tradeoff between the throughput and the entirely consumed power. A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing secondary users(SU), transmission bandwidth and power. A combined optimization algorithm of bi-level optimization, Polyblock optimization and Dinkelbach's optimization is proposed to solve the proposed non-convex optimization problem effectively. The simulation results show that, compared with throughput maximization model(TMM), the energy efficiency maximization model(EEMM) improves EE of the CR system and limits the excessive power consumption effectively.
基金supported in part by the Hi-tech research and development program of China (2009AA011805)National Natural Science Foundation of China (61032002)+1 种基金the Important National Science and Technology Specifi c Projects of China (2009ZX03003-007)the Joint State Key Program of the National Natural Science Foundation of China and the National Railway Ministry of China (60830001)
文摘Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions. If the energy value falls into the corresponding region,it will be judged as "1",no information or "0". When the probability of false alarm is constrained to be constant,the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination(EGC) scheme. Moreover,compared with the traditional one bit hard combination,fewer average sensing bits are required to transmit to the data fusion center with the proposed method.
文摘Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.
基金Project(61471194)supported by the National Natural Science Foundation of ChinaProject(BK20140828)supported by the Natural Science Foundation of Jiangsu Province,ChinaProjects(NS2015088,DUT16RC(3)045)supported by the Fundamental Research Funds for the Central Universities,China
文摘In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.
基金supported by National Natural Science Foundation of China under Grand No.61671183
文摘The majority of existing papers about spectrum sensing have the assumption that secondary users(SUs) are stationary. However,mobility is an essential feature of mobile communications networks. In this paper,the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics,i.e.,detection probability,miss detection probability and false alarm probability,were thoroughly investigated. In our analysis,a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover,the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability,but not on the false alarm probability.
