An optimized SNR estimation technique using particle swarm optimization algorithm

Manesh, Mohsen Riahi; Quadri, Adnan; Subramaniam, Sriram; Kaabouch, Naima

doi:10.1109/ccwc.2017.7868387

Cited by 19 publications

(16 citation statements)

References 17 publications

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“…Another challenge was how to make sure that the SNR we are targeting is the one that we have created. To overcome this challenge, we used a noise measurement method based on the eigenvalues of covariance matrix of the received samples [ 40 , 41 ]. This technique allows one to estimate the noise power and the signal power in a signal at the receiver side.…”

Section: Resultsmentioning

confidence: 99%

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Arjoune

Kaabouch

2018

Sensors

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Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm.

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Section: Resultsmentioning

confidence: 99%

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Arjoune

Kaabouch

2018

Sensors

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“…The approach measures the noise using a technique based on the eigenvalues of the sample covariance matrix of the received signal. This technique calculates the eigenvalues, then, uses the Minimum Description Length criterion to split the eigenvalues corresponding to the signal and the ones corresponding to the noise [24][25][26][27]. This technique is considered as blind estimation technique because the power of the signal and the power of the noise are unknown and these parameters are estimated from the received signal.…”

Section: Introductionmentioning

confidence: 99%

Spectrum sensing: Enhanced energy detection technique based on noise measurement

Arjoune

Mrabet

Ghazi

et al. 2018

2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)

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Spectrum sensing enables cognitive radio systems to detect unused portions of the radio spectrum and then use them while avoiding interferences to the primary users. Energy detection is one of the most used techniques for spectrum sensing because it does not require any prior information about the characteristics of the primary user signal. However, this technique does not distinguish between the signal and the noise. It has a low performance at low SNR, and the selection of the threshold becomes an issue because the noise is uncertain. The detection performance of this technique can be further improved using a dynamic selection of the sensing threshold. In this work, we investigate a dynamic selection of this threshold by measuring the power of noise present in the received signal using a blind technique. The proposed model was implemented and tested using GNU Radio software and USRP units. Our results show that the dynamic selection of the threshold based on measuring the noise level present in the received signal during the detection process increases the probability of detection and decreases the probability of false alarm compared to the ones of energy detection with a static threshold. -spectrum sensing, energy detection, autocorrelation, matched filter, dynamic threshold, software defined radio, probability of detection, probability of false alarm. Keywords

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“…Various algorithms have been proposed so far for the implementation of efficient cooperative spectrum sensing in CRNs, just to mention [6]- [19] and references therein. Among them, particle swarm optimization (PSO) has been recently proved to be a very handy technique for spectrum sensing and allocation in CRNs [12]- [19]. In a nutshell, the current research trend in the field is toward the determination of the optimum power allocation and the simplification of the relay selection process [5].…”

Section: Introductionmentioning

confidence: 99%

“…It is a simple, fast and efficient stochastic swarm intelligence algorithm used in many discrete optimization problems [17]. PSO neither requires a differentiable objective function nor relies on a specific single variable initialization, while it is less complex than other evolutionary optimization methods, e.g., genetic algorithms [12], [14]. These merits render the PSO-based techniques attractive candidates for dealing with dynamic spectrum sensing in CRNs, which may involve non-convex and joint optimization of several parameters at the same time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic spectrum sensing through accelerated particle swarm optimization

Paschos

Kapinas

Ntouni

et al. 2017

2017 25th Telecommunication Forum (TELFOR)

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In this paper, a novel optimization algorithm, called the acceleration-aided particle swarm optimization (A-APSO), is proposed for reliable dynamic spectrum sensing in cognitive radio networks. In A-APSO, the acceleration variable of the particles in the swarm is also considered in the search space of the optimization problem. We show that the proposed A-APSO based spectrum sensing technique is more efficient in terms of performance than the corresponding one based on the standard particle swarm optimization algorithm.

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An optimized SNR estimation technique using particle swarm optimization algorithm

Cited by 19 publications

References 17 publications

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Spectrum sensing: Enhanced energy detection technique based on noise measurement

Dynamic spectrum sensing through accelerated particle swarm optimization

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