scholarly journals Spectrum sensing of wideband signals based on cyclostationary and compressive sensing

2020 ◽  
Vol 20 (3) ◽  
pp. 1361
Author(s):  
Ali Mohammad A. AL-Hussain ◽  
Maher Khudair Mahmood Al Azawi
Keyword(s):  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Compression Ratio ◽  
High Speed ◽  
Processing Time ◽  
Sampling Rate ◽  
Probability Of Detection ◽  
Signal Spectrum ◽  
Sensing Matrix ◽  
Recovery Algorithm

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR with several compression ratio and processing time versus compression ratio are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection.

scholarly journals SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING

2020 ◽  
Vol 24 (06) ◽  
pp. 83-90
Author(s):  
Ali Mohammad A. AL-Hussain ◽  
◽  
Maher K. Mahmood ◽  
Keyword(s):  
False Alarm ◽  
Compressive Sensing ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Wide Band ◽  
Probability Of Detection ◽  
Signal Spectrum ◽  
Radio System ◽  
Probability Of False Alarm

Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix. The 𝓁1 norm algorithm is used to reconstruct the original signal. A closed form of probability of detection and probability of false alarm are derived. Computer simulation shows clearly that the compression ratio, recovery error and SNR level affect the probability of detection.

scholarly journals Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

2016 ◽  
Vol 6 (1) ◽  
pp. 1 ◽  
Author(s):  
Amr Hussein ◽  
Hossam Kasem ◽  
Mohamed Adel
Keyword(s):  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Compression Ratio ◽  
System Performance ◽  
High Speed ◽  
Sampling Rate ◽  
Energy Detector ◽  
Recent Theory ◽  
Detector Performance ◽  
Compression Sensing

Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

scholarly journals CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING MATRIX

2021 ◽  
Vol 1 (1) ◽  
pp. 134-145
Author(s):  
Hadeel S. Abed ◽  
Hikmat N. Abdullah
Keyword(s):  
Compressive Sensing ◽  
Probability Of Detection ◽  
Main Step ◽  
Mutual Coherence ◽  
Sensing Matrix ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Chebyshev Map ◽  
Recovery Error ◽  
Three Stages

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing  is the main step of CR.  Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening  in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconstruct a sparse signal. İn this paper a new chaotic matrix is proposed based on Chebyshev map and modified gram Schmidt (MGS). The CS based proposed matrix is applied for sensing  real TV signal as a PU. The proposed system is tested under two types of recovery algorithms. The performance of CS based proposed matrix is measured using recovery error (Re), mean square error (MSE), and probability of detection (Pd) and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the proposed system has low Re and high Pd under low SNR values and has low MSE with high compression.

scholarly journals Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jianfeng Liu ◽  
Xin-Lin Huang ◽  
Ping Wang
Keyword(s):  
Compressive Sensing ◽  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Temporal Correlation ◽  
False Alarm Probability ◽  
Spectrum Estimation ◽  
Sensing Data ◽  
Correlation Information

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

scholarly journals Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

2021 ◽  
Author(s):  
Xue Wang ◽  
Qian Chen ◽  
Min Jia ◽  
Xuemai Gu
Keyword(s):  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Detection Algorithm ◽  
Channel Structure ◽  
System Structure ◽  
Sensing Matrix ◽  
Sampling System ◽  
Wideband Spectrum Sensing

Abstract As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm, which is based on the advanced sub-Nyquist sampling framework, has the similar performance as MWC and even higher than MWC in some cases.

scholarly journals Chaotic Compressive Spectrum Sensing Based on Chebyshev Map for Cognitive Radio Networks

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 429
Author(s):  
Salma Benazzouza ◽  
Mohammed Ridouani ◽  
Fatima Salahdine ◽  
Aawatif Hayar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
High Performance ◽  
Greedy Algorithms ◽  
Recovery Process ◽  
Radio Networks ◽  
Acquisition Time ◽  
Sensing Matrix ◽  
Recovery Algorithm

Recently, the chaotic compressive sensing paradigm has been widely used in many areas, due to its ability to reduce data acquisition time with high security. For cognitive radio networks (CRNs), this mechanism aims at detecting the spectrum holes based on few measurements taken from the original sparse signal. To ensure a high performance of the acquisition and recovery process, the choice of a suitable sensing matrix and the appropriate recovery algorithm should be done carefully. In this paper, a new chaotic compressive spectrum sensing (CSS) solution is proposed for cooperative CRNs based on the Chebyshev sensing matrix and the Bayesian recovery via Laplace prior. The chaotic sensing matrix is used first to acquire and compress the high-dimensional signal, which can be an interesting topic to be published in symmetry journal, especially in the data-compression subsection. Moreover, this type of matrix provides reliable and secure spectrum detection as opposed to random sensing matrix, since any small change in the initial parameters generates a different sensing matrix. For the recovery process, unlike the convex and greedy algorithms, Bayesian models are fast, require less measurement, and deal with uncertainty. Numerical simulations prove that the proposed combination is highly efficient, since the Bayesian algorithm with the Chebyshev sensing matrix provides superior performances, with compressive measurements. Technically, this number can be reduced to 20% of the length and still provides a substantial performance.

scholarly journals Adaptive Sub-Nyquist Spectrum Sensing for Ultra-Wideband Communication Systems

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 342
Author(s):  
Yong Lu ◽  
Shaohe Lv ◽  
Xiaodong Wang
Keyword(s):  
Spectrum Sensing ◽  
Frequency Band ◽  
Communication Systems ◽  
High Speed ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Ultra Wideband ◽  
Wideband Spectrum Sensing ◽  
Adaptive Policy ◽  
Ultra Wideband Communication

With the ever-increasing demand for high-speed wireless data transmission, ultra-wideband spectrum sensing is critical to support the cognitive communication over an ultra-wide frequency band for ultra-wideband communication systems. However, it is challenging for the analog-to-digital converter design to fulfill the Nyquist rate for an ultra-wideband frequency band. Therefore, we explore the spectrum sensing mechanism based on the sub-Nyquist sampling and conduct extensive experiments to investigate the influence of sampling rate, bandwidth resolution and the signal-to-noise ratio on the accuracy of sub-Nyquist spectrum sensing. Afterward, an adaptive policy is proposed to determine the optimal sampling rate, and bandwidth resolution when the spectrum occupation or the strength of the existing signals is changed. The performance of the policy is verified by simulations.

A High Speed and Ultra Long-Haul Radio-Over-Fiber System Employing Dual Photoelectric Arms Coherent Modulation and Optical Duo-Binary Coding

2014 ◽  
Vol 988 ◽  
pp. 544-547
Author(s):  
Guang Li
Keyword(s):  
High Speed ◽  
Continuous Wave ◽  
Single Mode ◽  
Fiber Amplifier ◽  
Radio Over Fiber ◽  
Laser Source ◽  
Signal Spectrum ◽  
Fiber System ◽  
Binary Coding ◽  
Coherent Modulation

A novel high speed and ultra long-haul radio-over-fiber (ROF) system based on Dual Photoelectric Arms Coherent Modulation (DPACM) and Optical Duo-Binary Coding (ODBC) is proposed, and demonstrated. The signal spectrum bandwidth, generated by ODBC based on the first order DPACM, is half of non-return-to-zero (NRZ ) signal spectrum bandwidth. The secondary order DPACM generates a 40-GHz Millimeter-wave (mm-wave) that is transmitted over fiber (ROF). The simulation results show that, the bit rate can be up to 40 Gbps and the transmission distance is over 1500 Km, based on the ROF system with a 0 dBm continuous-wave laser source, multiple stages Er-Doped Fiber Amplifier (EDFA), a standard single mode fiber (SSMF) with a dispersion of 17 ps/nm/Km and a attenuation of 0.2 dB/Km.

scholarly journals An Active and Passive Reputation Method for Secure Wideband Spectrum Sensing Based on Blockchain

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1346
Author(s):  
Xinyu Xie ◽  
Zhuhua Hu ◽  
Min Chen ◽  
Yaochi Zhao ◽  
Yong Bai
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Sampling Rate ◽  
Radio Networks ◽  
Single Node ◽  
Decision Algorithm ◽  
Collusion Attacks ◽  
Wideband Spectrum Sensing

Spectrum is a kind of non-reproducible scarce strategic resource. A secure wideband spectrum sensing technology provides the possibility for the next generation of ultra-dense, ultra-large-capacity communications to realize the shared utilization of spectrum resources. However, for the open collaborative sensing in cognitive radio networks, the collusion attacks of malicious users greatly affect the accuracy of the sensing results and the security of the entire network. To address this problem, this paper proposes a weighted fusion decision algorithm by using the blockchain technology. The proposed algorithm divides the single-node reputation into active reputation and passive reputation. Through the proposed token threshold concept, the active reputation is set to increase the malicious cost of the node; the passive reputation of the node is determined according to the historical data and recent performance of the blockchain. The final node weight is obtained by considering both kinds of reputation. The proposed scheme can build a trust-free platform for the cognitive radio collaborative networks. Compared with the traditional equal-gain combination algorithm and the centralized sensing algorithm based on the beta reputation system, the simulation results show that the proposed algorithm can obtain reliable sensing results with a lower number of assistants and sampling rate, and can effectively resist malicious users’ collusion attacks. Therefore, the security and the accuracy of cooperative spectrum sensing can be significantly improved in cognitive radio networks.

scholarly journals Acute Effects of Handheld Loading on Standing Broad Jump in Youth Athletes

2021 ◽  
Vol 18 (9) ◽  
pp. 5046
Author(s):  
Wei-Hsun Tai ◽  
Ray-Hsien Tang ◽  
Chen-Fu Huang ◽  
Shin-Liang Lo ◽  
Yu-Chi Sung ◽  
...  
Keyword(s):  
High Speed ◽  
Center Of Mass ◽  
Reaction Force ◽  
Sampling Rate ◽  
Random Order ◽  
Horizontal Distance ◽  
Acute Effects ◽  
Vertical Ground Reaction Force ◽  
Strength And Conditioning ◽  
Peak Knee

The study aimed to investigate the acute effects of handheld loading on standing broad jump (SBJ) performance and biomechanics. Fifteen youth male athletes (mean age: 14.7 ± 0.9 years; body mass: 59.3 ± 8.0 kg; height: 1.73 ± 0.07 m) volunteered to participate in the study. Participants were assigned to perform SBJ with and without 4 kg dumbbells in a random order. Kinematic and kinetic data were collected using 10 infrared high-speed motion-capture cameras at a 250 Hz sampling rate and two force platforms at a 1000 Hz sampling rate. A paired t-test was applied to all variables to determine the significance between loading and unloading SBJs. Horizontal distance (p < 0.001), take-off distance (p = 0.001), landing distance (p < 0.001), horizontal velocity of center of mass (CoM; p < 0.001), push time (p < 0.001), vertical impulse (p = 0.003), and peak horizontal and vertical ground reaction force (GRF; p < 0.001, p = 0.017) were significantly greater in loading SBJ than in unloading SBJ. The take-off vertical velocity of CoM (p = 0.001), take-off angle (p < 0.001), peak knee and hip velocity (p < 0.001, p = 0.007), peak ankle and hip moment (p = 0.006, p = 0.011), and peak hip power (p = 0.014) were significantly greater in unloading SBJ than in loading SBJ. Conclusions: Acute enhancement in SBJ performance was observed with handheld loading. The present findings contribute to the understanding of biomechanical differences in SBJ performance with handheld loading and are highly applicable to strength and conditioning training for athletes.

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