Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

2020 ◽  
Vol 12 (4) ◽  
pp. 18-36
Author(s):  
Faten Mashta ◽  
Wissam Altabban ◽  
Mohieddin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Primary User ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Energy Detector ◽  
Detection Methods ◽  
Two Stage ◽  
Low Snr ◽  
Good Detection

Spectrum sensing in cognitive radio has difficult and complex requirements, requiring speed and good detection performance at low SNR ratios. As suggested in IEEE 802.22, the primary user signal needs to be detected at SNR = -21dB with a probability of detection exceeds 0.9. Conventional spectrum sensing methods such as the energy detector, which is characterized by simplicity with good detection performance at high SNR values, are ineffective at low SNR values, whereas eigenvalues detection methods have good detection performance at low SNR ratios, but they have high complexity. In this paper, the authors investigate the process of spectrum sensing in two stages: in the first stage (coarse sensing), the energy detector is adopted, while in the second stage (fine sensing), eigenvalues detection methods are used. This method improves performance in terms of probability of detection and computational complexity, as the authors compared the performance of two-stage sensing scheme with ones where only energy detection or eigenvalues detection is performed.

An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

2021 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Maximum Eigenvalue ◽  
Two Stage ◽  
Smoothing Factor ◽  
Low Snr ◽  
Simulation Results ◽  
Good Detection

Spectrum sensing for cognitive radio requires speed and good detection performance at very low SNR ratios. There is no single-stage spectrum sensing technique that is perfect enough to be implemented in practical cognitive radio. In this paper, the authors propose a new parallel fully blind multistage detector. They assume the appropriate stage based on the estimated SNR values that are achieved from the SNR estimator. Energy detection is used in first stage for its simplicity and sensing accuracy at high SNR. For low SNRs, they adopt the maximum eigenvalues detector with different smoothing factor in higher stages. The sensing accuracy for the maximum eigenvalue detector technique improves with higher value of the smoothing factor. However, the computational complexity will increase significantly. They analyze the performance of two cases of the proposed detector: two-stage and three-stage schemes. The simulation results show that the proposed detector improves spectrum sensing in terms of accuracy and speed.

scholarly journals Comparison of Single User with Multi User Cooperative Spectrum Sensing for Energy Detector at Low SNR Wall

2021 ◽  
Vol 2070 (1) ◽  
pp. 012083
Author(s):  
Kavita Bani ◽  
Vaishali Kulkarni
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Roc Curve ◽  
Cooperative Spectrum Sensing ◽  
Probability Of Detection ◽  
Energy Detector ◽  
Low Snr ◽  
Single User ◽  
Increasing Demand ◽  
Region Of Convergence

Abstract With rapidly increasing demand in wireless communication, available licensed spectrum resources should be utilized efficiently and actively. Cognitive radio is a device which learns from surrounding environment and transmit its signal when license spectrum is unutilized. Spectrum sensing is the need for Cognitive radio. In this paper, Energy detector is implemented though MATLAB software for single and multiusers. Region of Convergence (ROC) curve is plotted for both normal ED and Cooperative spectrum sensing ED. Results show while increasing number of samples from 1k to 100k, probability of detection is also achieved 0.9 maximum. Increasing SNR from -20dB, -15dB to -10 dB, probability of detection is improved in ROC curve. Also cooperative spectrum sensing with OR rule gives good probability of detection 0.9 to 1.

Multistage Spectrum Sensing for Cognitive Radio Using Energy and Maximum Eigenvalues Detection

2020 ◽  
Vol 11 (4) ◽  
pp. 41-59
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Smoothing Factor ◽  
Third Stage ◽  
Simulation Results ◽  
Good Detection ◽  
High Computational Complexity

Spectrum sensing in cognitive radio has difficult and complex requirements such as requiring speed and sensing accuracy at very low SNRs. In this paper, the authors propose a novel fully blind sequential multistage spectrum sensing detector to overcome the limitations of single stage detector and make use of the advantages of each detector in each stage. In first stage, energy detection is used because of its simplicity. However, its performance decreases at low SNRs. In second and third stage, the maximum eigenvalues detector is adopted with different smoothing factor in each stage. Maximum eigenvalues detection technique provide good detection performance at low SNRs, but it requires a high computational complexity. In this technique, the probability of detection improves as the smoothing factor raises at the expense of increasing the computational complexity. The simulation results illustrate that the proposed detector has better sensing accuracy than the three individual detectors and a computational complexity lies in between the three individual complexities.

scholarly journals A Classification of the Spectrum Sensing Techniques for Cognitive Radio

2019 ◽  
Vol 8 (4) ◽  
pp. 11586-11595
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Prior Information ◽  
Primary User ◽  
Probability Of Detection ◽  
Coherent Detection ◽  
Main Function ◽  
Advantages And Disadvantages ◽  
Wideband Spectrum Sensing

Cognitive radio is a solution to the problem of radio spectrum scarcity. It gives the opportunity to a secondary user to exploit the spectrum allocated toa primary user. The main function of cognitive radio is spectrum sensing whichhas gained new aspects in the last decades to determine opportunistic spectrum holes. There are many spectrumsensing methods proposed in the literature. The Performance of thesetechniques may vary in different situations; it can be described by probability of detection, probability of false alarm, and sensing time. It is therefore important to compare and indicate the best scheme for a specified scenario. In this paper, we propose a classification of the main approaches of single user spectrum sensing based on its synchronization requirement into two main categories: coherent detection and non-coherent detection. The coherent detection needs some or full prior information about the primary user signal for detecting it, where the non-coherent detection does not need any prior information about the primary user signal for detecting it. In addition, we highlight the advantages and disadvantages of narrowband and wideband spectrum sensing procedures along with the challenges involved in their implementation.Furthermore, we introduce the concept and basics of cooperative sensing and interference based sensing.This paper helps the designer to be familiar with all the techniques used to achieve spectrum sensing.

scholarly journals DOUBLE THRESHOLD SPECTRUM SENSING WITH OPTIMIZATION OF SAMPLES IN COGNITIVE RADIO NETWORKS

2021 ◽  
Vol 22 (2) ◽  
pp. 161-167
Author(s):  
Chilakala Sudhamani
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Probability Of Detection ◽  
Secondary User ◽  
Correct Detection ◽  
Propagation Time

In cognitive radio networks spectrum sensing plays a vital role to identify the presence or absence of the primary user. In conventional spectrum sensing one secondary user will make a final decision regarding the availability of licensed spectrum. But Secondary user fail to make a correct detection about the presence of the primary user if he is in fading environment and it causes interference to the licensed users. Therefore to avoid interference to the licensed users and to make correct detection, number of samples is increased, Which increases the probability of detection. In this paper the optimization of samples is proposed to reduce the system overhead and also to increase the propagation time. Simulation results show the optimized value of samples for a given probability of false alarm and also the variation of probability of detection with optimized samples and false alarm is shown in the results. ABSTRAK: Dalam rangkaian radio kognitif, penginderaan spektrum memainkan peranan penting untuk mengenal pasti kehadiran atau ketiadaan pengguna utama. Dalam penginderaan spektrum konvensional, seorang pengguna sekunder akan membuat keputusan akhir mengenai ketersediaan spektrum berlesen. Tetapi pengguna Sekunder gagal membuat pengesanan yang betul mengenai kehadiran pengguna utama jika dia berada dalam persekitaran yang pudar dan menyebabkan gangguan kepada pengguna yang berlesen. Oleh itu untuk mengelakkan gangguan kepada pengguna berlesen dan membuat pengesanan yang betul, jumlah sampel meningkat, yang meningkatkan kemungkinan pengesanan. Dalam makalah ini pengoptimuman sampel dicadangkan untuk mengurangi overhead sistem dan juga untuk meningkatkan waktu penyebaran. Hasil simulasi menunjukkan nilai sampel yang dioptimumkan untuk kebarangkalian penggera palsu dan juga variasi kebarangkalian pengesanan dengan sampel yang dioptimumkan dan penggera palsu ditunjukkan dalam hasil.

scholarly journals A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2522 ◽  
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Yuxin Li ◽  
Zhiqiang Bao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Transmission Band ◽  
Primary User ◽  
Detection Performance ◽  
Final Decision ◽  
Soft Decision ◽  
Hidden Terminal ◽  
Sensing Performance

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.

An Adaptive Spectrum-Sensing Algorithm for Cognitive Radio Networks based on the Sample Covariance Matrix

2017 ◽  
Vol 67 (3) ◽  
pp. 325 ◽  
Author(s):  
Chhagan Charan ◽  
Rajoo Pandey
Keyword(s):  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Primary User ◽  
Adaptive Threshold ◽  
Probability Of Detection ◽  
Superior Performance ◽  
Probability Of Error ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Sample Covariance

<p>A novel adaptive threshold spectrum sensing technique based on the covariance matrix of received signal samples is proposed. The adaptive threshold in terms of signal to noise ratio (SNR) and spectrum utilisation ratio of primary user is derived. It considers both the probability of detection and the probability false alarm to minimise the overall decision error probability. The energy- based spectrum sensing scheme shows high vulnerability under noise uncertainty and low SNR. The existing covariance-based spectrum sensing technique overcomes the noise uncertainty problem but its performance deteriorates under low SNR. The proposed covariance-based scheme effectively addresses the low SNR problem. The superior performance of this scheme over the existing covariance-based detection method is confirmed by the simulation results in terms of probability of detection, probability of error, and requirement of samples for reliable detection of spectrum.</p>

scholarly journals Spectrum Sensing in Cognitive Radio Networks: Threshold Value Optimization and Analysis

2020 ◽  
Author(s):  
Kenan KOÇKAYA ◽  
İbrahim DEVELİ
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Threshold Value ◽  
Radio Networks ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Optimal Threshold ◽  
Detection Scheme ◽  
Spectrum Detection

Abstract Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, we propose an online learning algorithm for the energy detection scheme, which aims to maximizing spectrum detection performance. Optimal threshold value, which is critical for the determination of the absence or the presence of a licensed user, was mathematically expressed in accordance with the balance between probability of detection and probability of false alarm. Performance of the proposed algorithm was tested on non-fading and different fading channels for low signal-to-noise ratio (SNR) regime with noise uncertainty. In conclusion of the simulation studies, improvement in spectrum detection performance according to optimal threshold value selection was observed.

scholarly journals Simulation and Analysis of the OFDM Transceiver Based Commutation System

2020 ◽  
Vol 26 (12) ◽  
pp. 131-140
Author(s):  
Areej Munadel ◽  
Ekhlas Kadhum Hamza
Keyword(s):  
Cognitive Radio ◽  
Signal To Noise Ratio ◽  
Radio Spectrum ◽  
Cyclic Prefix ◽  
Probability Of Detection ◽  
Energy Detector ◽  
Signal To Noise ◽  
Wireless Applications ◽  
Low Snr

As a result of the increase in wireless applications, this led to a spectrum problem, which was often a significant restriction. However, a wide bandwidth (more than two-thirds of the available) remains wasted due to inappropriate usage. As a consequence, the quality of the service of the system was impacted. This problem was resolved by using cognitive radio that provides opportunistic sharing or utilization of the spectrum. This paper analyzes the performance of the cognitive radio spectrum sensing algorithm for the energy detector, which implemented by using a MATLAB Mfile version (2018b). The signal to noise ratio SNR vs. Pd probability of detection for OFDM and SNR vs. BER with CP cyclic prefix with energy detector is calculated and analyzed. In this paper, the proposed work produces more accurate results compared to the existing techniques at low SNR values.

Improved Two-Stage Spectrum Sensing for Cognitive Radio Networks

2019 ◽  
Vol 23 (6) ◽  
pp. 1052-1062
Author(s):  
Fidel Wasonga ◽  
Thomas O. Olwal ◽  
Adnan Abu-Mahfouz ◽  
◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Two Stage ◽  
Probability Of Error ◽  
Average Spectrum ◽  
Sensing Time

Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time.

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