scholarly journals Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

2019 ◽  
Vol 8 (12) ◽  
pp. 4257-4261
Keyword(s):  
Cognitive Radio ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Research Work ◽  
Radio Networks ◽  
Decision Threshold ◽  
Operating Characteristics ◽  
Low Snr ◽  
Unique System ◽  
Proposed Model

This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.

scholarly journals Verification analysis for the reliable analytical multi-taper detector in next generation network

2020 ◽  
Vol 9 (4) ◽  
pp. 1486-1496
Author(s):  
Hebat Allah O. Selim ◽  
Ahmed Shaaban Dessouki ◽  
Heba Y. M. Soliman
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Analytical Study ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Radio Networks ◽  
Probability Of Detection ◽  
Computationally Efficient ◽  
Operating Characteristics ◽  
Simulation Techniques

Multi-taper method (MTM) acts as an effective detector of spectrum sensing in Cognitive radio networks. In this paper, an analytical study was proposed in which reliable, simple, and computationally efficient mathematical terms for the mean and variance of the probability density function (PDF) were derived using the MTM technique. The closed-form expressions for the probability of detection and false alarm for the MTM detector were obtained accordingly. The proposed analytical study was evaluated by intensive simulations using MATLAB. Different simulation techniques were proposed to verify the derived analysis. The existence of White Gaussian Noise was assumed. Important aspects of spectrum detection in cognitive radio networks were included such as, receiver operating characteristics, detection rate versus signal to noise ratio (SNR), and the minimum desired sample points for a specific performance. A comparison was completed with the energy detection technique and all of the results suggested that the proposed paradigm is both credible and powerful under all the parameters considered in the simulation.

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

scholarly journals A Robust Real-Time Automatic Recognition Prototype for Maritime Optical Morse-Based Communication Employing Modified Clustering Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1227 ◽  
Author(s):  
Xiaozheng Wang ◽  
Minglun Zhang ◽  
Hongyu Zhou ◽  
Xinglong Lin ◽  
Xiaomin Ren
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Experimental Tests ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Decision Threshold ◽  
Low Snr ◽  
Led Array

In maritime communications, the ubiquitous Morse lamp on ships plays a significant role as one of the most common backups to radio or satellites just in case. Despite the advantages of its simplicity and efficiency, the requirement of trained operators proficient in Morse code and maintaining stable sending speed pose a key challenge to this traditional manual signaling manner. To overcome these problems, an automatic system is needed to provide a partial substitute for human effort. However, few works have focused on studying an automatic recognition scheme of maritime manually sent-like optical Morse signals. To this end, this paper makes the first attempt to design and implement a robust real-time automatic recognition prototype for onboard Morse lamps. A modified k-means clustering algorithm of machine learning is proposed to optimize the decision threshold and identify elements in Morse light signals. A systematic framework and detailed recognition algorithm procedure are presented. The feasibility of the proposed system is verified via experimental tests using a light-emitting diode (LED) array, self-designed receiver module, and microcontroller unit (MCU). Experimental results indicate that over 99% of real-time recognition accuracy is realized with a signal-to-noise ratio (SNR) greater than 5 dB, and the system can achieve good robustness under conditions with low SNR.

Performance Analysis of Hybrid Fusion in Cognitive Radio Networks

2013 ◽  
Vol 479-480 ◽  
pp. 1027-1031
Author(s):  
Man Man Guo ◽  
Yun Xue Liu ◽  
Wen Qiang Fan
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Signal To Noise Ratio ◽  
Radio Networks ◽  
Base Station ◽  
Good Choice ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Impact ◽  
Hybrid Fusion

Spectrum sensing is a crucial issue in cognitive radio networks for primary user detection. Cooperative sensing based on energy detection in the cognitive radio network with multiple antennas base-station is considered in this letter. To improve the sensing performance, we investigate hybrid fusion of the observed energies from the base-station and decisions (1bit, hard information) from different cognitive radio (CR) users around the base-station. Further, we present an optimized scheme where the global detection probability can be maximized according to the Neyman-Pearson criterion. Finally the impact of the change of parameters (Signal to Noise Ratio and number of CR users) in the optimized scheme is analyzed. Numerical simulations and extensive analysis confirm that hybrid fusion base on the optimized scheme is a good choice, also, Signal to Noise Ratio (SNR) and number of CR users does not have influence on the optimized scheme

scholarly journals Spectrum sensing in cognitive radio wireless networks for orthogonal freqnency division multiplexing in additive white Gaussian noise channel

2017 ◽  
Vol 1 (T4) ◽  
pp. 180-186
Author(s):  
Tri Minh Nguyen ◽  
Tu Thanh Nguyen ◽  
Phuong Huu Nguyen
Keyword(s):  
Cognitive Radio ◽  
Gaussian Noise ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Sliding Window ◽  
White Gaussian Noise ◽  
Frequency Division ◽  
Low Snr ◽  
Primary Users ◽  
Ofdm Signals

Cognitive radio (CR) systems are one of the most interesting topics in recent years. They would enable more efficient use of the spectrum. The main problem of CR is how to dectect exactly the spectrum usage of primary users. There are many ways to do this, such as energy detector (ED), Axell’s detector, the sliding window detector, etc. Among them, cyclostationarity (CS) based dection methods attracted much attention because of their better results in low-SNR regimes. This paper will propose a method based on the autocorrelation property of orthogonal frequency division multiplexing (OFDM) signals in additive white Gaussian noise (AWGN).

scholarly journals Efficient and Accurate Frequency Estimator under Low SNR by Phase Unwrapping

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shen Zhou ◽  
Liu Rongfang
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Likelihood Estimation ◽  
Accurate Method ◽  
Phase Unwrapping ◽  
Single Frequency ◽  
Low Snr ◽  
Cramer Rao Bound ◽  
Frequency Estimator

In the case of low signal-to-noise ratio, for the frequency estimation of single-frequency sinusoidal signals with additive white Gaussian noise, the phase unwrapping estimator usually performs poorly. In this paper, an efficient and accurate method is proposed to address this problem. Different from other methods, based on fast Fourier transform, the sampled signals are estimated with the variances approaching the Cramer-Rao bound, followed with the maximum likelihood estimation of the frequency. Experimental results reveal that our estimator has a better performance than other phase unwrapping estimators. Compared with the state-of-the-art method, our estimator has the same accuracy and lower computational complexity. Besides, our estimator does not have the estimation bias.

scholarly journals Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 626 ◽  
Author(s):  
Ernesto Cadena Muñoz ◽  
Luis Fernando Pedraza Martínez ◽  
Cesar Augusto Hernandez
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Radio Networks ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Phone Call ◽  
Radio System

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

scholarly journals Spectrum sensing in cognitive radio networks: threshold optimization and analysis

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Kenan kockaya ◽  
Ibrahim Develi
Keyword(s):  
Online Learning ◽  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Learning Algorithm ◽  
Radio Networks ◽  
Decision Threshold ◽  
Online Learning Algorithm ◽  
Sensing Performance

AbstractCognitive 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, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold 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.

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