A Cognitive Radio Spectrum Sensing Algorithm to Improve Energy Detection at Low SNR

As the demand of wireless communication increases exponentially, with the same ratio scarcity of spectrum also originates. To overcome this spectrum scarcity a novel approach, Cognitive Radio (CR) shows development of an opportunistic and promising technology. This paper explores implementation and analysis of the CR spectrum sensing techniques such as Matched filtering, Energy detection and Cyclostationary feature detection on MATLAB platform by simulation. We analyze performance of these techniques over, Nakagami-m fading channel with AWGN channel for both the BPSK and QPSK modulation.

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Analysis of Spectrum Sensing in Cognitive Radio Network with OFDM

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.725 ◽

2018 ◽

Vol 8 (6) ◽

pp. 84

Author(s):

Shadab Ahmed Khan ◽

Pawandeep Kaur

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Network ◽

Energy Detection ◽

Radio Spectrum ◽

Probability Of Detection ◽

Spectrum Utilization ◽

Wireless Applications ◽

Novel Technology ◽

Cognitive Environment

With the development of a new and ever expanding wireless applications and services, spectrum resources are facing in demands. In present scenario, the spectrum allotment has been done by providing each new service with its own fixed frequency Slot. Most of the user’s spectrum is already assigned, so it becomes very difficult to find spectrum for other users or existing users. This leads to the scarcity of available spectrum and inefficient channel utilization. Cognitive radio is a novel technology which improves the spectrum utilization by allowing secondary user to borrow unused radio spectrum from primary licensed users or to share the spectrum with the primary users. Present paper deals with the spectrum sensing in which multiple users detect the spectrum gap through energy detection and investigate the detection performance in an efficient and implementable way. Simulation results showed that the probability of detection is achieved at small value SNR in case of OFDM modulation as compare the other and simple cognitive environment.

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An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

2014 10th International Computer Engineering Conference (ICENCO) ◽

10.1109/icenco.2014.7050446 ◽

2014 ◽

Cited By ~ 12

Author(s):

Hossam M. Farag ◽

M. Ehab

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Energy Detection ◽

Radio Spectrum ◽

Detection Technique

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An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/ijertcs.2021040101 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1-20

Author(s):

Faten Mashta ◽

Mohieddin Wainakh ◽

Wissam Altabban

Keyword(s):

Cognitive Radio ◽

Computational Complexity ◽

Spectrum Sensing ◽

Energy Detection ◽

Maximum Eigenvalue ◽

Two Stage ◽

Smoothing Factor ◽

Low Snr ◽

Simulation Results ◽

Good Detection

Spectrum sensing for cognitive radio requires speed and good detection performance at very low SNR ratios. There is no single-stage spectrum sensing technique that is perfect enough to be implemented in practical cognitive radio. In this paper, the authors propose a new parallel fully blind multistage detector. They assume the appropriate stage based on the estimated SNR values that are achieved from the SNR estimator. Energy detection is used in first stage for its simplicity and sensing accuracy at high SNR. For low SNRs, they adopt the maximum eigenvalues detector with different smoothing factor in higher stages. The sensing accuracy for the maximum eigenvalue detector technique improves with higher value of the smoothing factor. However, the computational complexity will increase significantly. They analyze the performance of two cases of the proposed detector: two-stage and three-stage schemes. The simulation results show that the proposed detector improves spectrum sensing in terms of accuracy and speed.

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IMPROVEMENT OF ENERGY CONSUMPTION IN SPECTRUM SENSING COGNITIVE RADIO NETWORKS USING AN EFFICIENT TWO STAGE SENSING METHOD

Acta Polytechnica ◽

10.14311/ap.2017.57.0235 ◽

2017 ◽

Vol 57 (4) ◽

pp. 235 ◽

Cited By ~ 2

Author(s):

Hikmat Najem Abdullah ◽

Hadeel Sami Abed

Keyword(s):

Energy Consumption ◽

Cognitive Radio ◽

Fading Channels ◽

Spectrum Sensing ◽

Minimum Energy ◽

Energy Detection ◽

Primary User ◽

Low Snr ◽

Local Decision ◽

Two Stages

Cognitive radio (CR) is a wireless technology developed to improve the usage in the spectrum frequency. Energy consumption is considered as a big problem in this technology, especially during a spectrum sensing. In this paper, we propose an algorithm to improve the energy consumption during the spectrum sensing. The theoretical analysis to calculate the amount of energy consumption, using the proposed method during sensing stage as well as the transmission stage during transmitting a local decision to the fusion center FC, are derived. The proposed algorithm is using energy detection technique to detect the presence or absence of the primary user (PU). The proposed algorithm consists of two stages: the coarse sensing stage and fine sensing stage. In the coarse sensing stage, all the channels in the band are sensed shortly and the channel that have maximum (or minimum) energy is identified to make a dense fine sensing for confirming the presence of the PU signal (or hole). The performance of the proposed algorithm is evaluated in two scenarios: non-cooperative, and cooperative in both the AWGN and Rayleigh fading channels. The simulation results show that the proposed method improves the energy consumption by about 40% at a low SNR values, when compared with the traditional methods based on a single sensing stage and more advanced method based on censoring and sequential censoring algorithms.

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A novel double threshold-based spectrum sensing technique at low SNR under noise uncertainty for Cognitive Radio Systems

10.21203/rs.3.rs-397280/v1 ◽

2021 ◽

Author(s):

Garima Mahendru

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Energy Detection ◽

Communication Link ◽

Noise Power ◽

Radio System ◽

Low Snr ◽

Detection Probabilities ◽

Analytical Result ◽

Novel Concept

Abstract Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.

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Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8703.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2655-2661

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Kernel Function ◽

Feature Space ◽

Energy Detection ◽

Spectrum Efficiency ◽

Dynamic Threshold ◽

Noise Uncertainty ◽

Low Snr ◽

Space Data

Cognitive radio (CR) is a new technology that is proposed to improve spectrum efficiency by allowing unlicensed secondary users to access the licensed frequency bands without interfering with the licensed primary users. As there are several methods available for spectrum sensing, the energy detection (ED) is more popular due to its simple implementation. However, ED is more vulnerable to the noise uncertainty so for that reason, we present a robust detector using signal to noise ratio (SNR) with dynamic threshold energy detection technique is combined with the kernel principal component analysis (KPCA) in Cognitive Radio Networks (CRN). The primary purpose of kernel function is to ensure that its dependency relies on inner-product of data without the feature space data requirement. In this paper, with the aid of kernel function the spectrum sensing with the leading eigenvector approach is modified to a feature space of higher dimensionality.By introducing of efficient detection system with dynamic threshold facility helps the better detection levels even low SNR values with quite a lot of noise uncertainty levels. The simulation results of the proposed system reveal that KPCA outperforms with that of traditional PCA in terms of false alarm rate, detector performance when tested under various uncertainties for orthogonal frequency division multiplexing signal.

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A Metaheuristic based approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks

Recent Patents on Engineering ◽

10.2174/1872212113666191025130543 ◽

2019 ◽

Vol 13 ◽

Author(s):

Garima Mahendru ◽

Anil K Shukla ◽

L M Patnaik

Keyword(s):

Particle Swarm Optimization ◽

Cognitive Radio ◽

Spectrum Sensing ◽

Detection Probability ◽

Particle Swarm ◽

Pso Algorithm ◽

Energy Detection ◽

Swarm Optimization ◽

Noise Uncertainty ◽

Low Snr

Background: : The mounting growth of wireless technology is attracting high demand for frequency spectrum. The measurements of spectrum usage depicts that a significant portion of spectrum lays unoccupied or overcrowded. The main cause of the glitch is the existing inefficient and fixed scheme of spectral allocation. Cognitive radio is one such technology that permits wireless devices to detect the unused frequency band and reconfigure its operating parameters to attain required quality of service. Objective: To permit dynamic allocation of the frequency band, spectrum sensing is performed which is an essential function of Cognitive radio and involves detection of an unused spectrum space to setup a communication link. Method: : This paper presents a meta-heuristic approach for selection of a decision threshold for energy detection based spectrum sensing. At low SNR and in presence of noise uncertainty performance of energy detection method fails. A novel adaptive double threshold based spectrum-sensing method is proposed to avoid such a sensing failure. Further, the metaheuristic approach employs Particle Swarm Optimization (PSO) algorithm to compute an optimal value of the threshold to attain robustness against noise uncertainty at low SNR. Results: : The simulation results of the proposed metaheuristic double threshold based spectrum sensing method demonstrate enhanced performance in comparison to the existing methods in terms of reduced error rate and increased detection probability. Some of the existing methods have been analyzed and compared from a survey of recent patents on spectrum sensing methods to support the new findings The concept of adaptive thresholding improves the detection probability by 39 % and 27 % at noise uncertainty of 1.02 and 1.04 respectively at a signal to noise ratio of -10 dB. Furthermore, the error probability reduces to 58% at the optimal threshold using Particle Swarm Optimization (PSO) algorithm for signal to noise ratio of -9 dB. Conclusions: : The main outcome of this work is reduction in probability of sensing failure and improvement in the detection probability using adaptive double thresholds at low SNR. Further, particle swarm optimization helps in obtaining minimum probability of error under noise uncertainty with an optimal threshold.

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