Double constraints adaptive energy detection for spectrum sensing in cognitive radio networks

Srisomboon, Kanabadee; Prayote, Akara; Lee, Wilaiporn

doi:10.1109/icmu.2015.7061037

Cited by 14 publications

(11 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that the estimated SNR is normally calculated as a ratio of estimated signal power (or variance) to estimated noise power (or variance). In addition, since a noise variance and signal variance cannot be perfectly estimated, we assume that an estimated noise variance and an estimated signal variance are with a tolerance in the range of 3% [5]. To set a system threshold of DCAED [5], is first compared to a critical SNR ( ) for mini-slot in order to select a condition to calculate an adaptive factor ( DCAED ).…”

Section: Detectionmentioning

confidence: 99%

“…Although AED outperforms the detection performance of ED, the false alarm detection rate of AED does not achieve the target performance stated by IEEE 802.22 standard which is less than 0.1. In our previous paper, we proposed an adaptive energy detection scheme, double constraints adaptive energy detection (DCAED) [5], which the system threshold is calculated by fixing P and P at the same time. By fixing these two parameters at the same time, DCAED gives high rate of P while gives low rate of P for all SNR levels.…”

Section: Introductionmentioning

confidence: 99%

“…MDCAED determines the existence of the PU by splitting a sensing slot into a multiple mini-slot. Each mini-slot is performed spectrum sensing using A DCAED [5]. Finally, each decision from mini-slot is sent to decision fusing process [6] to determine the existence of the PU.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-slot double constraints adaptive energy detection for spectrum sensing in Cognitive radio networks

Srisomboon

Prayote

Lee

2015

2015 15th International Symposium on Communications and Information Technologies (ISCIT)

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a new scheme of an adaptive energy detection, multi-slot double constraints adaptive energy detection (MDCAED), with an objective to improve the detection performance of our previous work, double constraints adaptive energy detection (DCAED). MDCAED exploits multiple mini-slot technique, which achieves the diversity reception concept, to increase the ability to distinguish noise from the PU since the effect of diversity reception increases the received SNR. MDCAED performs spectrum sensing by spitting a sensing slot into a multiple mini-slot. Each mini-slot is performed spectrum sensing using DCAED and the final decision is made by using Kof-N rule. The decision threshold is adapted on the SNR of each mini-slot using DCAED. Therefore, by exploiting the multiple mini-slot concept together with DCAED, is reduced while is improved. Although, the detection performance is improved, the average sensing time of MDCAED slightly increases compared to DCAED since the system threshold needs to adapt more than once. Nevertheless, the average sensing time of MDCAED still achieves the spectrum sensing requirement.

show abstract

Section: Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-slot double constraints adaptive energy detection for spectrum sensing in Cognitive radio networks

Srisomboon

Prayote

Lee

2015

2015 15th International Symposium on Communications and Information Technologies (ISCIT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the spectrum sensing, the secondary users identify the state of each frequency channel-free or occupied-using spectrum sensing techniques, such as energy detection [4][5][6][7][8][9], cyclostationary features detection [10][11][12][13][14][15], and matched filter detection [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Energy detection [4][5][6][7][8][9] computes the energy of the received sample as the squared magnitude of the Fast Fourier Transform (FFT) averaged over the number of samples. Then, this energy is compared to a threshold to decide on the presence or the absence of the primary user.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

2018

View full text Add to dashboard Cite

Abstract:Cognitive radio is an intelligent wireless solution that aims to enhance the access to the radio spectrum. In this technology, secondary users sense the radio spectrum, select the best channel among a pool of free channels, and determine the optimal transmission parameters to meet their quality-of-service requirements while maximizing the spectral efficiency. Over the past decade, several channel-ranking mechanisms have been proposed. However, these mechanisms consider only the remaining idle time of the channel and exclude some crucial parameters. This convincingly demonstrates a strong need for a new channel quality-ranking model that jointly considers several parameters to select the best communication channel for transmission. This paper proposes a utility model that integrates several important parameters for ranking channels. First, we underline the importance of the process of the channel quality ranking. Then, we describe a multi-attributes, utility-based, channel quality-ranking model. Finally, we describe a series of experiments and their results, which show that our model effectively ranks the best communication channels first.

show abstract

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Fawzi

El‐Shafai

Abd‐Elnaby

et al. 2020

Int J Communication

View full text Add to dashboard Cite

This paper presents an efficient adaptive two-stage spectrum sensing model for the cognitive radio (CR) systems. The proposed model combines two wellknown techniques: energy detection (ED) and wavelet denoising (WD). The ED technique is employed to identify the existence of the primary user (PU) signal in the case of high signal-to-noise ratio (SNR) by comparing the received signal with a threshold. In the case of low SNR, the ED technique alone cannot be adopted for decision making about the status of the PU due to the noise impact on the received signals. Hence, the stage of WD is exploited prior to the ED for reducing the noise effect and detecting the PU signal in the presence of noise. The detection performance of the proposed model is compared with those of the ED alone and the previous related two-stage spectrum sensing methods. Simulation results show that the proposed model improves the false alarm rate compared to the ED alone by 36% and 6%, respectively, at low SNRs of −15 and −20 dB. Also, the proposed model yields the smallest detection time compared to the previous two-stage spectrum sensing methods. The proposed model can be used to minimize the probability of false alarm, achieve a better probability of detection, and improve the sensing process in CR systems.

show abstract

Double constraints adaptive energy detection for spectrum sensing in cognitive radio networks

Cited by 14 publications

References 1 publication

Multi-slot double constraints adaptive energy detection for spectrum sensing in Cognitive radio networks

Multi-slot double constraints adaptive energy detection for spectrum sensing in Cognitive radio networks

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

Adaptive two‐stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems

Contact Info

Product

Resources

About