Comparison of cyclostationary and energy detection in cognitive radio network

Khan, Risala T.; Islam, Md. Imdadul; Zaman, Shakila; Amin, M. R.

doi:10.1109/iwci.2016.7860359

Cited by 17 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…67 However, because it is easy to implement recent research works on detecting PU signals have implemented energy detection overmatched filter detection. 78 Still, because energy detection relies only on noise power for PU signal detection its performance is seriously affected by uncertainty in noise power. 79 To counter this challenge, a fixed threshold value called SNR wall is normally used to indicate an uncertainty level.…”

Section: Energy Detectionmentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

show abstract

Section: Energy Detectionmentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…To enable dynamic spectrum access, these systems perform spectrum sensing to decide on the presence or absence of the primary user [2,3]. Several sensing techniques have been proposed to sense the radio spectrum including energy detection [4][5][6][7][8], autocorrelation [9][10][11][12], and matched filter based sensing [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…If this energy is higher than this threshold, then the primary user is deemed to be present; otherwise, the primary user signal is considered absent. This technique is simple as it does not require any prior information about the primary user signal, which makes it much more straightforward than matched filter and autocorrelation sensing technique [5]. However, the performance of this technique is highly dependent on the noise which is random [6]; thus, using a static threshold degrades the performance of energy detection [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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)

View full text Add to dashboard Cite

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

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%

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

Comparison of cyclostationary and energy detection in cognitive radio network

Cited by 17 publications

References 15 publications

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Spectrum sensing: Enhanced energy detection technique based on noise measurement

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

Contact Info

Product

Resources

About