scholarly journals Optimization of spectrum sensing in cognitive radio using genetic algorithm

2012 ◽  
Vol 25 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Rashmi Deka ◽  
Soma Chakraborty ◽  
Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Rapid Development ◽  
Probability Of Detection ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Space Allocation ◽  
Wireless Environment ◽  
Better Than

Spectrum availability is becoming scarce due to the rise of number of users and rapid development in wireless environment. Cognitive radio (CR) is an intelligent radio system which uses its in-built technology to use the vacant spectrum holes for the use of another service provider. In this paper, genetic algorithm (GA) is used for the best possible space allocation to cognitive radio in the spectrum available. For spectrum reuse, two criteria have to be fulfilled - 1) probability of detection has to be maximized, and 2) probability of false alarm should be minimized. It is found that with the help of genetic algorithm the optimized result is better than without using genetic algorithm. It is necessary that the secondary user should vacate the spectrum in use when licensed users are demanding and detecting the primary users accurately by the cognitive radio. Here, bit error rate (BER) is minimized for better spectrum sensing purpose using GA.

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 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.

Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems

2013 ◽  
Vol 411-414 ◽  
pp. 1521-1528 ◽  
Author(s):  
Yu Yang ◽  
Yan Li Ji ◽  
Han Hui Li ◽  
Du Lei ◽  
Meng Rui
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Two Stage ◽  
Probability Of False Alarm ◽  
Detection Model ◽  
Radio Systems ◽  
Cyclostationary Feature Detection

In this paper, we investigate the features of energy detection and cyclostationary feature detection for spectrum sensing. In order to combine their advantages, we propose an adaptive two-stage sensing scheme which performs spectrum sensing using an energy detector first in cognitive radio networks. Then in the second stage, this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, the goal of our proposed scheme is to optimize the probability of detection and sensing speed at the same time. In order to obtain the optimal detection thresholds, we can formulate the detection model as a nonlinear optimization problem. Furthermore, the simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.

A hybrid model for energy efficient spectrum sensing in cognitive radio

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahua Bhowmik ◽  
P. Malathi P. Malathi
Keyword(s):  
Cognitive Radio ◽  
Prediction Model ◽  
Spectrum Sensing ◽  
Hybrid Model ◽  
Energy Efficient ◽  
Probability Of Detection ◽  
Content Type ◽  
Hybrid Prediction ◽  
Probability Of False Alarm ◽  
Sensing Time

Purpose Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR. Design/methodology/approach This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs. Findings The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s. Research implications The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on. Originality/value A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

A Robust Spectrum Sensing Method Using Filter Bank

2011 ◽  
Vol 58-60 ◽  
pp. 54-59
Author(s):  
Dong Feng Zhao ◽  
Xiao Ma ◽  
Xian Wei Zhou
Keyword(s):  
Fourier Transform ◽  
Cognitive Radio ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Spectrum Sensing ◽  
Filter Bank ◽  
Gaussian Function ◽  
Time Spectrum ◽  
Radio System ◽  
Wireless Environment

Robust spectrum sensing is one of the essential issues for building and commercializing the cognitive radio system. With the challenging wireless environment, a reliable and computation feasible real-time spectrum sensing method using filter bank has been proposed in this letter. Extended Gaussian Function and Fast Fourier Transform are effectively combined together to implement a non-parameter and thus robust spectrum sensing. Numerical results are provided to compare the performance of the proposed method with that of the existing spectrum sensing.

A Novel Weighted Cluster Based Co-operative Spectrum Sensing Scheme for Cognitive Radio Network

2020 ◽  
Vol 10 ◽  
Author(s):  
Md. Anamul Kabir ◽  
Anjon Sarker ◽  
Abdullah Al-Mamun Bulbul ◽  
Himadri Shekhar Mondal
Keyword(s):  
Cognitive Radio ◽  
Fading Channel ◽  
Cognitive Radio Network ◽  
Base Station ◽  
Probability Of Detection ◽  
Radio Network ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Channel Bandwidth ◽  
Sensing Performance

Cognitive radio can be described as a radio system where it familiarizes to the situations of the environs by some procedure like analyzing, observing and learning. Cognitive radio can basically recycle the unused white space in the license spectrum through sensing the network thus the method can achieve the maximum implementation of the radio bandwidth. When we forward the sensing observation to the cognitive radio base station via any fading channel, sensing performance will relentlessly be degraded. With the intention of lessening the fading and shadowing, co-operative detection is favored over independent detection. For sensing AND rule, OR rule and K-out-of-N rule has much better sensing result compared to the independent detection. Clustered based cooperation has been employed to expand the sensing enactment moderately. Sensing of co-operative spectrum which is weighted has been used to boost the cluster cooperation scheme. But weighted cluster also has some downsides which can be overcome by planning a new algorithm where the usage of channel bandwidth is reduced. Regarding to the probability of detection and the probability of false alarm, the simulation outcomes show the improvement for our proposed method.

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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