scholarly journals Spectrum Sensing for OFDM Cognitive Radio using Matched Filter Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 1443-1448
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Orthogonal Frequency Division Multiplexing ◽  
Cross Correlation ◽  
Matched Filter ◽  
False Alarm Probability ◽  
Spectrum Allocation ◽  
Probability Of Detection ◽  
Operating Characteristics

The exponentially increasing high data rate applications demand more spectrum. Conventional spectrum allocation schemes failed in providing spectrum to meet the requirement. Cognitive radio introduces dynamic spectrum allocation. Spectrum sensing plays a very important role in cognitive radio. In this paper, OFDM with sensing algorithm based on matched filter detection is presented. An Orthogonal Frequency Division Multiplexing (OFDM) with Cyclic Prefix is used to reduce Inter Block Interference between successive symbols. The Receiver Operating Characteristics (RoC), detection probability with respect to Signal to Noise Ratio (SNR) for a given False Alarm Probability (PFA) obtained and analyzed. Simulation results shows that, this algorithm achieves probability of detection values 0.38 and 0.9 for SNR values of -20dB and -15 dB respectively at PFA=10-2 . Chen, Hou-Shin, considered Time Domain Symbol Cross-correlation between two OFDM symbols. This paper mainly focusses on improving the detection probability at low SNR values without considering cross-correlation, which requires more computations.

scholarly journals Spectrum Sensing Using Energy Detector in Rayleigh Fading Channel

2020 ◽  
Vol 32 ◽  
pp. 02005
Author(s):  
Hemlata Patil ◽  
Sai Vennela Nekkanti ◽  
Disha Negi ◽  
Nikita Masanagi ◽  
Sakshi More
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Rayleigh Fading ◽  
False Alarm Probability ◽  
Rayleigh Fading Channel ◽  
Probability Of Detection ◽  
Detection Methods ◽  
Operating Characteristics

Cognitive Radio, which is an adaptive and intelligent radio has an important feature called spectrum sensing. It monitors the presence or absence of the authorized users in a fixed or licensed spectrum. There is a rise in the use of wireless communications in recent times, which has led to the underutilization of the available bandwidth. This problem can be solved to some extent by using cognitive radio technology. Cognitive radio gives an opportunity for unauthorized users to use the licensed spectrum, resourcefully, when not in use. Using spectrum sensing energy detection methods, the existence of authorized users, in various fading channels can be done. The operating characteristics of the receiver can be analyzed using parameters of performance like signal to noise ratio, probability of false-alarm, probability of detection. In this paper we assess Rayleigh fading channel and compare with AWGN (non- fading) channel. Theoretical and simulated results are compared, and observations are being made.

scholarly journals Performance Prediction of OFDM-Based Cognitive Radio for Next-Generation Networking Capabilities

2020 ◽  
Vol 9 (1) ◽  
pp. 1911-1919
Keyword(s):  
Cognitive Radio ◽  
Mobile Networks ◽  
Orthogonal Frequency Division Multiplexing ◽  
New Technology ◽  
False Alarm Probability ◽  
Probability Of Detection ◽  
Next Generation ◽  
Spectral Correlation ◽  
Multicarrier Transmission ◽  
Daily Life Activities

As a pioneering technology, next generation of mobile networks would overcome many problems of daily life activities. This new technology has many challenges that are tried to be destroyed. In this regard, to optimize the utilization of the allowed spectrum, an essential tool such as cognitive radio (CR) must be employed. It provides opportunity to use spectrum in strategic manner to both licensed and unlicensed users in such a way that the available spectrum is exploited in an efficient strategy. Meanwhile, the orthogonal frequency division multiplexing (OFDM) multicarrier transmission mechanism represents one of the more familiar techniques that are widely applied in free space systems of communication. Since this algorithm has the capability of satisfying the prime aspect of CR, which is associated with locally exploiting the unused spectrum autonomously, OFDM procedure is investigated as a CR system’s candidate. In other words, it is of interest to study its behavior when it is combined with CR technology. Here, our goal is to treat the problem of spectrum sensing techniques in conjunction with OFDM signal. Simulation results show that OFDM spectral correlation can be enhanced via varying the number of samples. Also, OFDM pilot is mandatory and acts as a flag for OFDM frames. So, it is of importance to boost it for better detection. Additionally, the probability of detection (Pd ) is estimated for different values of signal strength when the false alarm probability (Pf ) is fixed. Moreover, receiver operating characteristic, which is the variation of Pd as a function of Pf for a fixed SNR, is drawn for two cases; theoretical and simulated. It is found that the two cases are of high degree of coincidence.

scholarly journals Robust Spectrum Sensing Demonstration Using a Low-Cost Front-End Receiver

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Daniele Borio ◽  
Emanuele Angiuli ◽  
Raimondo Giuliani ◽  
Gianmarco Baldini
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
High Frequency ◽  
Detection Probability ◽  
Low Cost ◽  
False Alarm Probability ◽  
Primary Users ◽  
Front End ◽  
Challenging Tasks

Spectrum Sensing (SS) is an important function in Cognitive Radio (CR) to detect primary users. The design of SS algorithms is one of the most challenging tasks in CR and requires innovative hardware and software solutions to enhance detection probability and minimize low false alarm probability. Although several SS algorithms have been developed in the specialized literature, limited work has been done to practically demonstrate the feasibility of this function on platforms with significant computational and hardware constraints. In this paper, SS is demonstrated using a low cost TV tuner as agile front-end for sensing a large portion of the Ultra-High Frequency (UHF) spectrum. The problems encountered and the limitations imposed by the front-end are analysed along with the solutions adopted. Finally, the spectrum sensor developed is implemented on an Android device and SS implementation is demonstrated using a smartphone.

Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

2019 ◽  
Vol 10 (1) ◽  
pp. 83-97
Author(s):  
Amoon Khalil ◽  
Mohiedin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Detection Performance ◽  
Shrinkage Estimator ◽  
Detection Window ◽  
Simulation Results

Spectrum Sensing is one of the major steps in Cognitive Radio. There are many methods to conduct Spectrum Sensing. Each method has different detection performances. In this article, the authors propose a modification of one of these methods based on MME algorithm and OAS estimator. In MME&OAS method, in each detection window, OAS estimates the covariance matrix of the signal then the MME algorithm detects the signal on the estimated matrix. In the proposed algorithm, authors assumed that there is correlation between two consecutive decisions, so authors suggest the OAS estimator depending on the last detection decision, and then detect the signal using MME algorithm. Simulation results showed enhancement in detection performance (about 2dB when detection probability is 0.9. compared to MME&OAS method).

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.

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 Novel Sensing Strategy Based on Energy Detector for Spectrum Sensing

2019 ◽  
Vol 9 (21) ◽  
pp. 4634 ◽  
Author(s):  
Hai Huang ◽  
Jia Zhu ◽  
Junsheng Mu
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Energy Detector ◽  
Accuracy Improvement ◽  
Classical Scheme ◽  
Sensing Performance

Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper.

scholarly journals Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hurmat Ali Shah ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Posterior Probability ◽  
Nearest Neighbor ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Training Phase ◽  
K Nearest Neighbor ◽  
Combination Scheme

Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

Sign in / Sign up

Export Citation Format

Share Document