A Modified Energy Detection Algorithm Based on Primary User Activity for Cognitive Radio Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1351-1354
Author(s):  
Gang Tao Han ◽  
Ying Qiang Ding ◽  
Xiao Min Mu ◽  
Jian Kang Zhang
Keyword(s):  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Spectrum Access ◽  
Theory Analysis ◽  
Channel State ◽  
User Activity

Spectrum sensing is used to identify the unused frequency bands and as such plays a key role in dynamic spectrum access. In most of the existing spectrum sensing models, the channel state of primary user is assumed unchanged within the spectrum sensing duration, which is not suitable for the channels with high activity, in which the primary user accesses or vacates from the channel frequently. In this paper, a Modified Energy Detection (MED) algorithm is proposed for this scenarios by considering the primary user activity within the spectrum sensing duration. Theory analysis and computer simulation results show that both the probability of detection and false alarm in this scenarios have been improved with our MED.

scholarly journals 5G cognitive radio system design with new algorithm asynchronous spectrum sensing

2021 ◽  
Vol 10 (4) ◽  
pp. 2046-2054
Author(s):  
Mohammed Mehdi Saleh ◽  
Ahmed A. Abbas ◽  
Ahmed Hammoodi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Spectrum Access ◽  
Secondary Users ◽  
Radio System ◽  
Wireless Applications ◽  
Fifth Generation ◽  
Time Required

Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the fifth generation (5G) system. Cognitive radio (CR) has emerged as the primary technology to address these challenges, allowing opportunist spectrum access as well as the ability to analyze, observe, and learn how to respond to environmental 5G conditions. The CR has the ability to sense the spectrum and detect empty bands in order to use underutilized frequency bands without causing unwanted interference with legacy networks. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary user SU to transmit asynchronously with primary user PU without causing harmful interference. This algorithm reduced the sensing time required to scan the whole frequency band by dividing it into n sub-bands that are all scanned at the same time. Also, this algorithm allows cognitive radio networks (CRN) nodes to select their operating band without requiring cooperation with licensed users. According to the BER, secondary users have better performance compared with primary users.

scholarly journals A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4136
Author(s):  
Jakub Nikonowicz ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Low Complexity ◽  
Primary User ◽  
Time Frame ◽  
Detection Algorithm ◽  
Experimental Simulation ◽  
Spectrum Access ◽  
Blind Signal ◽  
Occupancy Estimation

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

A Dynamic Threshold Optimization Algorithm for Spectrum Sensing with Energy Detection in Cognitive Radio

2012 ◽  
Vol 236-237 ◽  
pp. 917-922
Author(s):  
Wei Ran Wang ◽  
Shu Bin Wang ◽  
Xin Yan Zhao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Dynamic Threshold ◽  
Threshold Optimization ◽  
Sensing Performance

In order to improve an efficiency of energy detection for a spectrum sensing in cognitive radio (CR), this paper proposes a dynamic threshold optimization algorithm. The traditional energy detection algorithm uses a fixed threshold, and can't guarantee always the optimal sensing performance in any environment. The improvement for sensing performance need to minimize the undetected probability and the probability of false alarm, and it is dissimilar for different CR users to accept these two errors. We improve the traditional energy detection algorithm, and firstly introduce a preference factor to characterize CR users’ different requirements for these two errors, then, propose a dynamic threshold optimization algorithm by minimizing integrated detection error for different signal-to-noise ratio (SNR). The simulation results show that the proposed algorithm effectively reduces the integrated spectrum sensing error, and increases the probability of detection, especially in low SNR.

Noise Uncertainty Effect on a Modified Two-Stage Spectrum Sensing Technique

2016 ◽  
Vol 1 (2) ◽  
pp. 341 ◽  
Author(s):  
Heba A.Tag El-Dien ◽  
Rokaia M. Zaki ◽  
Mohsen M. Tantawy ◽  
Hala M. Abdel-Kader
Keyword(s):  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Probability Of Detection ◽  
Minimum Eigenvalue ◽  
Two Stage ◽  
Noise Uncertainty ◽  
Second Stage ◽  
Single Detector ◽  
Maximum Minimum

Detecting the presence or absence of primary user is the key task of cognitive radio networks. However, relying on single detector reduces the probability of detection and increases the probability of missed detection. Combining two conventional spectrum sensing techniques by integrating their individual features improves the probability of detection especially under noise uncertainty. This paper introduces a modified two-stage detection technique that depends on the energy detection as a first stage due to its ease and speed of detection, and the proposed Modified Combinational Maximum-Minimum Eigenvalue based detection as a second stage under noise uncertainty and comperes it with the case of using Maximum-Minimum Eigenvalue and  Combinational Maximum-Minimum Eigenvalue as a second stage.

scholarly journals Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

2021 ◽  
Vol 21 (1) ◽  
pp. 472
Author(s):  
G. A. Pethunachiyar ◽  
B. Sankaragomathi
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

scholarly journals Relay Assisted Spectrum Sensing in Presence of IQ Imbalance in Multi-Channel System

2021 ◽  
Author(s):  
Amaresh Kumar Sahu ◽  
Arunanshu Mahapatro ◽  
Radheshyam Patra
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Low Cost ◽  
Energy Detection ◽  
Primary User ◽  
Detection Scheme ◽  
Robust Detection ◽  
Spectrum Holes

Abstract The increasing demand of wireless technology leads to a need for dynamic spectrum access that is positively accomplished by cognitive radio (CR) technology. For its acceptable efficiency it needs a robust detection scheme which detect the spectrum holes. This paper investigates a cooperative spectrum sensing method for a relay-based cognitive radio network. In general relay is a low cost device and is more prone to hardware impairment such as In-phase and Quadrature-phase Imbalance (IQI), which can considerably limit the capabilities of sensing spectrum holes. This work studies the energy detection (ED) based spectrum sensing in multi-channel receiver scenarios that are affected by IQI. The detection and false alarm probabilities considering Gaussian primary user signal models are derived. Our results are simulation based and verified with theoretical findings.

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