Improved Adaptive Cooperative Spectrum Sensing in Cognitive Radio Networks
04 May 2018-pp 1-5
TL;DR: A particular weight have been designated to each CR participating in the process to classify according to reliability so as to facilitate the eradication of issues faced by individual CRs in precise spectrum sensing.
Abstract: We know that there is an issue for spectrum resources being unavailable because of increasing wireless services. To solve this a newer intelligent technology have been developed called as Cognitive Radio (CR). This technology inculcates the use of spectrum holes that occur due to the underutilization of the licensed spectrum. The identification of the spectrum holes can be done by using a method known as spectrum sensing. There are a variety of issues faced by individual CRs in precise spectrum sensing such as hidden terminal problem, shadowing and multipath fading. In order to facilitate the eradication of these issues a newer design of cooperative spectrum sensing have been developed called as Cooperative Spectrum Sensing (CSS). In this a particular weight have been designated to each CR participating in the process to classify according to reliability. Instead of using the conventional threshold equation an adaptive threshold equation have been devised for the initial energy detection algorithm and utilizing the adaptive threshold equation we have designed our algorithm
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TL;DR: Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.
Abstract: Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.
20 citations
Cites methods from "Improved Adaptive Cooperative Spect..."
...On the basis of the original weighted coefficient of [11], the algorithm WDT-ED added the ratio of the current user SNR value to the average SNR value to increase the accuracy of the thresholds....
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...In this section, to evaluate the performance of the proposed history-based adaptive double-threshold energy detection algorithm (HBADT-ED), we present numerical results and compare with the traditional double-threshold energy detection algorithm (TDT-ED) in [10], the weighted doublethreshold energy detection method (WDT-ED) modified from [11], the adaptive double-threshold energy detection scheme (ADT-ED) in [17], and the three consecutive time double-threshold energy detection method (TCTDT-ED) improved based on [7], respectively....
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TL;DR: From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters for 5G wireless communication using NOMA.
Abstract: In the recent past, efficient cooperative spectrum sensing and usage are playing a vital role in wireless communication because of the significant progress of mobile devices. There is a recent surge and interest on non-orthogonal multiple access (NOMA) focused on communication powered by wireless mode. In modern research, more attention has been focused on efficient and accurate NOMA. NOMA wireless communication is highly adapted with cognitive radio network (CRN) for improving performance. In the existing CRN, the secondary users could be able to access the idle available spectrum while primary users are engaged. In the traditional CRN, the primary user’s frequency bands are sensed as free, the secondary users could be utilized those bands of frequency resources. In this research, the novel methodology is proposed for cooperative spectrum sensing in CRN for 5G wireless communication using NOMA. The higher cooperative spectrum efficiency can be detected in the presence of channel noise. Cooperative spectrum sensing is used to improve the efficient utilization of spectrum. The spectrum bands with license authority primary user are shared by secondary users by simultaneously transmitting information with primary users. The cooperative spectrum sensing provides well under the circumstances that the different channel interference to the primary user can be guaranteed to be negligible than an assured thresholding value. The Noisy channel state information like AWGN and Rayleigh fading channels are considered as wireless transmission mediums for transmitting a signal using multiple-input-multiple-output NOMA to increase the number of users. The proposed NOMA is fascinated with significant benefits in CRN is an essential wireless communication method for upcoming 5G technology. From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters.
6 citations
01 Oct 2019
TL;DR: An optimization scheme to defend against spectrum sensing data falsification attacks based on the double threshold energy detection and block chain technology and a reward and penalty mechanism similar to the bitcoin system for CU's trust value statistics are proposed.
Abstract: The realization of spectrum sensing is becoming more and more possible with the development of technologies such as artificial intelligence and software radio. While we are paying attention to its performance, we cannot ignore the security problems it faces. This paper proposes an optimization scheme to defend against spectrum sensing data falsification (SSDF) attacks based on the double threshold energy detection and block chain technology. First, to reduce the impact of noise uncertainty on spectrum sensing results, we assume that the judgment threshold value will be dynamically determined according to the actual SNR of the receiver; Secondly, the block chain is introduced to consider as a spectrum sharing platform, and build the reputation classification mechanism for the cognitive users (CUs). We use a reward and penalty mechanism similar to the bitcoin system for CU's trust value statistics. Only users in the reliable zone can participate in sensing fusion; the rest will be in the waiting or discarding zone. We use MATLAB to carry out simulation experiments on the proposed algorithm. The experimental results show that the proposed algorithm can achieve the probability of false alarm to 0.1, the probability of detection to 0.93, and effectively avoid the interference of malicious users.
4 citations
Cites background from "Improved Adaptive Cooperative Spect..."
...Someone has proposed a cooperative energy detection technology, which can solve the problem of poor robustness of single node energy detection [3-5]....
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TL;DR: In this article , a detection method based on normalized short-time Fourier transform-Radon transform for Low frequency Sonar pulse signal (DNLS) is proposed, which is a constant false alarm detection method in normalized short time Fourier transformation-based domain.
Abstract: Under low-frequency background noise environments, due to the characteristics of poor stability and many interference targets of noise, the detection of unknown low-frequency sonar signals faces huge challenge. And sonar pulse signal detection methods based on time domain or frequency domain have the limitation of insufficient detection Signal-to-Noise Ratio (SNR). In order to improve the detection capability of weak sonar pulse signal in low frequency background noise environments, a Detection method based on normalized short-time Fourier transform-Radon transform for Low frequency Sonar pulse signal (DNLS) is proposed, which is a constant false alarm detection method in normalized short-time Fourier transform-Radon transform domain. In DNLS method, after the normalized short-time Fourier transform-Radon transformation, low-frequency noise energy to be dispersed into the entire transformation domain, and the sonar pulse signal energy containing the Linear Frequency Modulation (LFM) component is concentrated at a specific target point in the normalized short-time Fourier transform-Radon transform transformation domain, which can obtain a higher local SNR than the time-domain SNR. Moreover, the specific target point is distinguishable from the background noise, and the impulse signal detection decision is completed by constructing hypothesis test statistics on the target point data. The DNLS method solves the detection problems of low-frequency background such as poor stability, large fluctuations, and more interference. And the method of obtaining the test statistics of the constant false alarm detection, estimating the background noise and calculating the detection threshold is given. Extensive simulation results and actual data processing show that, under the simulation condition, in the minimum detection SNR of LFM, Continuous Wave(CW)-LFM and pulse trains of frequency modulated pulse signals with the same pulse width, compared with the dual-threshold constant false alarm rate energy detection method, the DNLS method is improved by 15dB, 13dB and 4dB, respectively. Under actual data conditions, in the detection of CW-LFM pulse signals with the same pulse width, compared with the double-threshold constant false alarm rate energy detection method, the detection performance of the DNLS method in the case of no ship radiated noise interference and strong radiated noise interference improves by 5dB and 5.5dB, respectively. The data analysis results show that the DNLS method has very good detection performance for LFM, pulse trains of frequency modulated, CW-LFM and other sonar pulse signals at low SNR, and can effectively detect the sonar pulse signals under the background of strong ship radiated noise.
3 citations
TL;DR: In this article , a detection method based on normalized short-time Fourier transform-Radon transform for Low frequency Sonar pulse signal (DNLS) is proposed, which is a constant false alarm detection method in normalized short time Fourier transforms-radon transform domain.
Abstract: Under low-frequency background noise environments, due to the characteristics of poor stability and many interference targets of noise, the detection of unknown low-frequency sonar signals faces huge challenge. And sonar pulse signal detection methods based on time domain or frequency domain have the limitation of insufficient detection Signal-to-Noise Ratio (SNR). In order to improve the detection capability of weak sonar pulse signal in low frequency background noise environments, a Detection method based on normalized short-time Fourier transform-Radon transform for Low frequency Sonar pulse signal (DNLS) is proposed, which is a constant false alarm detection method in normalized short-time Fourier transform-Radon transform domain. In DNLS method, after the normalized short-time Fourier transform- Radon transformation, low-frequency noise energy to be dispersed into the entire transformation domain, and the sonar pulse signal energy containing the Linear Frequency Modulation (LFM) component is concentrated at a specific target point in the normalized short-time Fourier transform-Radon transform transformation domain, which can obtain a higher local SNR than the time-domain SNR. Moreover, the specific target point is distinguishable from the background noise, and the impulse signal detection decision is completed by constructing hypothesis test statistics on the target point data. The DNLS method solves the detection problems of low-frequency background such as poor stability, large fluctuations, and more interference. And the method of obtaining the test statistics of the constant false alarm detection, estimating the background noise and calculating the detection threshold is given. Extensive simulation results and actual data processing show that, under the simulation condition, in the minimum detection SNR of LFM, Continuous Wave(CW)-LFM and pulse trains of frequency modulated pulse signals with the same pulse width, compared with the dual-threshold constant false alarm rate energy detection method, the DNLS method is improved by 15dB, 13dB and 4dB, respectively. Under actual data conditions, in the detection of CW-LFM pulse signals with the same pulse width, compared with the double-threshold constant false alarm rate energy detection method, the detection performance of the DNLS method in the case of no ship radiated noise interference and strong radiated noise interference improves by 5dB and 5.5dB, respectively. The data analysis results show that the DNLS method has very good detection performance for LFM, pulse trains of frequency modulated, CW-LFM and other sonar pulse signals at low SNR, and can effectively detect the sonar pulse signals under the background of strong ship radiated noise.
3 citations
References
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TL;DR: In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented and the cooperative sensing concept and its various forms are explained.
Abstract: The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.
4,812 citations
"Improved Adaptive Cooperative Spect..." refers methods in this paper
...Prior to the invention of the CSS there have been many non CSS schemes invented such as cyclostationary detection, matched filter detection and the very basic energy detection that are discussed in [8] and [9]....
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01 Apr 1967
TL;DR: By using Shannon's sampling formula, the problem of the detection of a deterministic signal in white Gaussian noise, by means of an energy-measuring device, reduces to the consideration of the sum of the squares of statistically independent Gaussian variates.
Abstract: By using Shannon's sampling formula, the problem of the detection of a deterministic signal in white Gaussian noise, by means of an energy-measuring device, reduces to the consideration of the sum of the squares of statistically independent Gaussian variates. When the signal is absent, the decision statistic has a central chi-square distribution with the number of degrees of freedom equal to twice the time-bandwidth product of the input. When the signal is present, the decision statistic has a noncentral chi-square distribution with the same number of degrees of freedom and a noncentrality parameter λ equal to the ratio of signal energy to two-sided noise spectral density. Since the noncentral chi-square distribution has not been tabulated extensively enough for our purpose, an approximate form was used. This form replaces the noncentral chi-square with a modified chi-square whose degrees of freedom and threshold are determined by the noncentrality parameter and the previous degrees of freedom. Sets of receiver operating characteristic (ROC) curves are drawn for several time-bandwidth products, as well as an extended nomogram of the chi-square cumulative probability which can be used for rapid calculation of false alarm and detection probabilities. Related work in energy detection by J. I. Marcum and E. L Kaplan is discussed.
3,071 citations
TL;DR: This paper designs the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected, and forms the sensing-throughput tradeoff problem mathematically, and uses energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput.
Abstract: In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance in cognitive radio networks. There are two parameters associated with spectrum sensing: probability of detection and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users' perspective, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable throughput for the secondary network. In this paper, we study the problem of designing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and use energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. Cooperative sensing using multiple mini-slots or multiple secondary users are also studied using the methodology proposed in this paper. Computer simulations have shown that for a 6 MHz channel, when the frame duration is 100 ms, and the signal-to-noise ratio of primary user at the secondary receiver is -20 dB, the optimal sensing time achieving the highest throughput while maintaining 90% detection probability is 14.2 ms. This optimal sensing time decreases when distributed spectrum sensing is applied.
2,889 citations
TL;DR: The optimal voting rule for any detector applied to cooperative spectrum sensing is derived and the detection threshold when energy detection is employed, and a fast spectrum sensing algorithm is proposed for a large network which requires fewer than the total number of cognitive radios while satisfying a given error bound.
Abstract: We consider cooperative spectrum sensing in which multiple cognitive radios collaboratively detect the spectrum holes through energy detection and investigate the optimality of cooperative spectrum sensing with an aim to optimize the detection performance in an efficient and implementable way. We derive the optimal voting rule for any detector applied to cooperative spectrum sensing. We also optimize the detection threshold when energy detection is employed. Finally, we propose a fast spectrum sensing algorithm for a large network which requires fewer than the total number of cognitive radios in cooperative spectrum sensing while satisfying a given error bound.
744 citations
"Improved Adaptive Cooperative Spect..." refers methods in this paper
...In [5] the optimum number of secondary users required for cooperative spectrum sensing have been discussed by using adaptive threshold strategy and [6] describes the optimal n value in order to reduce the total error rate....
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TL;DR: This paper considers the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user and proposes an iterative algorithm to obtain the optimal values for these two parameters.
Abstract: In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.
486 citations
Additional excerpts
...In [7] the false alarm probability is minimized keeping the miss detection probability constant and optimizing the threshold of detection....
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