scholarly journals Kinerja Spectrum Sensing dengan Metode Matched Filter Detector pada Radio Kognitif

2013 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
BAYU ANGGA ◽  
DWI ARYANTA ◽  
NASRULLAH ARMI
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Matched Filter ◽  
Primary User ◽  
Energy Detector ◽  
New Paradigm ◽  
Major Step ◽  
Probability Of False Alarm ◽  
Radio Frequency Spectrum

ABSTRAKEvolusi sistem nirkabel dan teknologi saat ini telah membuat dampak besar pada masyarakat. Namun, disaat yang sama pengelolaan dan pemanfaatan kelangkaan spektrum tidak efisien. Radio kognitif adalah paradigma baru dalam merancang sistem komunikasi nirkabel yang bertujuan untuk meningkatkan pemanfaatan spektrum frekuensi radio (RF) dan mengurangi seminimal mungkin kelangkaan spektrum. Spectrum sensing adalah langkah utama yang akan memungkinkan jaringan radio kognitif, yaitu untuk menentukan status spektrum dan aktivitas pengguna utama secara berkala, dengan menggunakan metode matched filter detector dan energy detector sebagai pembandingnya. Hasil dari kinerja spectrum sensing berdasarkan simulasi, menunjukan kinerja matched filter detector membutuhkan SNR = 15 dB untuk mencapai probability detection (Pd) sebesar 100%, dengan probability false alarm sebesar 0,01, sedangkan energy detector hanya membutuhkan SNR = 14,2 dB. Secara keseluruhan untuk deteksi sinyal yang optimal kinerja matched filter detector tidak lebih baik dibanding kinerja energy detector.Kata kunci: spectrum sensing, radio kognitif, probability detection, matched filter detector, energy detector.ABSTRACTThe evolution of wireless systems and current technology has made a huge impact on society. However, at the same time the management and utilization of spectrum scarcity is not efficient. Cognitive Radio is a new paradigm in designing wireless communication system that aims to improve the utilization of the radio frequency spectrum (RF) and reduce to a minimum the scarcity of spectrum. Spectrum sensing is a major step that will allow the cognitive radio networks, namely to determine the status of the spectrum and activity of the primary user at regular intervals, using the method of matched filter detector and energy detector as a comparison. The results of the performance spectrum sensing based on simulations, indicates the performance matched filter detector requires SNR = 15 dB to achieve detection probability (Pd) of 100%, with a probability of false alarm of 0.01, whereas energy detector only requires SNR = 14.2 dB. As a whole for optimum signal detection performance of matched filter detector is not better than the performance of energy detector.Keywords: spectrum sensing, cognitive radio, probability detection, matched filter detector, energy detector.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

An Algorithm Based on Double-Threshold Energy Detection in Cognitive Radio

2014 ◽  
Vol 643 ◽  
pp. 105-110
Author(s):  
Yuan Li ◽  
Jia Yin Chen ◽  
Xiao Feng Liu ◽  
Ming Chuan Yang
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Mathematical Proof ◽  
Threshold Energy ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Threshold Detection ◽  
Probability Of False Alarm ◽  
Double Threshold

Aiming at the situation where the double-threshold detection has been widely used without complete mathematical proof and condition of application, this paper proves its correctness under the circumstance of spectrum sensing, and circulates the condition where this method can work. The proof and simulation show that, comparing with traditional energy detection, this method can increase the probability of detection by 27% to 42% at most when the SNR is between-15dB and-2dB, while the probability of false alarm is increased by less than 2%.

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.

One-Bit Hard Combination Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio

2015 ◽  
Vol 4 (2) ◽  
pp. 66
Author(s):  
Md. Shamim Hossain ◽  
Md. Ibrahim Abdullah ◽  
Mohammad Alamgir Hossain
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Probability Of False Alarm ◽  
Decision Scheme ◽  
Comparable Performance ◽  
Signal Simulation ◽  
Detection Energy ◽  
Detector Model

<p>In this paper, the performance of 1-bit hard combination decision scheme for cooperative spectrum sensing in Cognitive Radio has been studied to maximize the probability of primary user detection. Energy detector model is used to observe the presence of primary user signal. Simulation result shows that the probability of missed detection is decreasing for both conventional and 1-bit hard combination OR rule with increasing the probability of false alarm correspondingly. It also has demonstrated that 1-bit hard combination decision scheme exhibits comparable performance with the conventional one-bit scheme and thus achieves a good tradeoff between performance and complexity.</p>

An Overview of the Different Principal Spectrum Sensing Techniques in Cognitive Radio Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 342-347
Author(s):  
Asmaa Maali ◽  
Hayat Semlali ◽  
Sara Laafar ◽  
Najib Boumaaz ◽  
Abdallah Soulmani
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Operational Characteristic ◽  
Main Function ◽  
Maximum Eigenvalue ◽  
Smoothing Factor

Cognitive radio is a technology proposed to increase the effective use of the spectrum. This can be done through the main function of cognitive radio technology, which is the spectrum sensing. In our work, we propose an analysis of the following spectrum sensing techniques: the matched filter detector, the cyclostationary feature detector, the energy detector and the maximum eigenvalue detector. More attention is given to blind sensing techniques that they do not need any knowledge of the primary user signal characteristics, namely the energy detection and maximum eigenvalue detection. These methods are evaluated in terms of Receiver Operational Characteristic curves and detection probability for various values of Signal to Noise Ratio based on Monte Carlo simulations, using MATLAB. As a result of this study, we found that the energy detection offers a good performance only for high SNR. Furthermore, with the maximum eigenvalue detector, the noise uncertainty problem encountered by the energy detection is solved when the value of the smoothing factor L ≥ 8 and. Finally, a summary of the comparative analysis is presented.

scholarly journals Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Nandita Lavanis ◽  
Devendra Jalihal
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Random Variable ◽  
Energy Detection ◽  
Primary User ◽  
Test Statistic ◽  
Secondary Users ◽  
Probability Of False Alarm ◽  
Standard Energy

A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a p-norm detector with p≥2, where p=2 corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of p and show that p=3 along with the Gaussian assumption is the best amongst all the cases considered.

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.

scholarly journals Performance Evaluation of Energy Detector Based Spectrum Sensing for Cognitive Radio using NI USRP-2930

2017 ◽  
Vol 7 (4) ◽  
pp. 1934 ◽  
Author(s):  
Fatima Zahra El Bahi ◽  
Hicham Ghennioui ◽  
Mohcine Zouak
Keyword(s):  
Performance Evaluation ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Software Defined Radio ◽  
Signal To Noise Ratio ◽  
Software Radio ◽  
Primary User ◽  
Energy Detector ◽  
Experimental Measurements ◽  
Signal To Noise

This paper presents the performance evaluation of the Energy Detector technique, which is one of the most popular Spectrum Sensing (SS) technique for Cognitive Radio (CR). SS is the ability to detect the presence of a Primary User (PU) (i.e. licensed user) in order to allow a Secondary User (SU) (i.e unlicensed user) to access PU's frequency band using CR, so that the available frequency bands can be used efficiently. We used for implementation an Universal Software Radio Peripheral (USRP), which is the most used Software Defined Radio (SDR) device for research in wireless communications. Experimental measurements show that the Energy Detector can obtain good performances in low Signal to Noise Ratio (SNR) values. Furthermore, computer simulations using MATLAB are closer to those of USRP measurements.

scholarly journals Spectrum Sensing in Cognitive Radio

2012 ◽  
pp. 29-42
Author(s):  
Wei-Ho Chung
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Probability Of Detection ◽  
Sensor Nodes ◽  
Fusion Rules ◽  
Probability Of False Alarm ◽  
Individual Decisions

The cognitive radio has been widely investigated to support modern wireless applications. To exploit the spectrum vacancies in cognitive radios, the chapter considers the collaborative spectrum sensing by multiple sensor nodes in the likelihood ratio test (LRT) frameworks. In this chapter, the functions of sensors can be served through the cooperative regular nodes in the cognitive radio, or the specifically deployed sensor nodes for spectrum sensing. In the LRT, the sensors make individual decisions. These individual decisions are then transmitted to the fusion center to make the final decision, which provides better detection accuracy than the individual sensor decisions. The author provides the lowered-bounded probability of detection (LBPD) criterion as an alternative criterion to the conventional Neyman-Pearson (NP) criterion. In the LBPD criterion, the detector pursues the minimization of the probability of false alarm while maintaining the probability of detection above the pre-defined value. In cognitive radios, the LBPD criterion limits the probabilities of channel conflicts to the primary users. Under the NP and LBPD criteria, the chapter provides explicit algorithms to solve the LRT fusion rules, the probability of false alarm, and the probability of detection for the fusion center. The fusion rules generated by the algorithms are optimal under the specified criteria. In the spectrum sensing, the fading channels influence the detection accuracies. The chapter investigates the single-sensor detection and collaborative detections of multiple sensors under various fading channels and derives testing statistics of the LRT with known fading statistics.

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