On the double threshold energy detection-based spectrum sensing over κ-μ fading channel

Ribas, Annita de Oliveira Petrocchi; Dias, Ugo Silva

doi:10.1109/rws.2015.7129775

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The ED model based on double threshold owns high detection performance, but the psensing time is too long and the stability is low, while the system complexity is high [6][7][8]. In [6], a collaborative detection algorithm was proposed with double threshold and energy statistical weighting, where double threshold determined by the limit probability was considered to improve the performance of collaborative detection and weighted energy combinations were conducted to improve the detection performance of mixed regions.…”

Section: Edmentioning

confidence: 99%

“…In [6], a collaborative detection algorithm was proposed with double threshold and energy statistical weighting, where double threshold determined by the limit probability was considered to improve the performance of collaborative detection and weighted energy combinations were conducted to improve the detection performance of mixed regions. In [7], the spectrum sensing system was exploited based on double threshold energy detection 𝜅-𝜇. The performance on fading channels was analyzed, and the system represents small-scale varied in fading signals under light and multipath conditions.…”

Section: Edmentioning

confidence: 99%

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Joint optimization of sampling point and sensing threshold for spectrum sensing

Li,

Ouyang,

Miao

et al. 2024

IET Communications

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With the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

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Section: Edmentioning

confidence: 99%

Section: Edmentioning

confidence: 99%

Joint optimization of sampling point and sensing threshold for spectrum sensing

Li,

Ouyang,

Miao

et al. 2024

IET Communications

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“…The mentioned articles discuss the ED method with a single decision threshold. A double-threshold ED has been discussed in [9,10,11]. Although, in many papers, the noise power is assumed to be known, some works present the ways of noise estimation for ED purposes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for LTE Energy Detection Performance Improvement

Wasilewska

Bogucka

2019

Sensors

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The growing number of radio communication devices and limited spectrum resources are drivers for the development of new techniques of dynamic spectrum access and spectrum sharing. In order to make use of the spectrum opportunistically, the concept of cognitive radio was proposed, where intelligent decisions on transmission opportunities are based on spectrum sensing. In this paper, two Machine Learning (ML) algorithms, namely k-Nearest Neighbours and Random Forest, have been proposed to increase spectrum sensing performance. These algorithms have been applied to Energy Detection (ED) and Energy Vector-based data (EV) to detect the presence of a Fourth Generation (4G) Long-Term Evolution (LTE) signal for the purpose of utilizing the available resource blocks by a 5G new radio system. The algorithms capitalize on time, frequency and spatial dependencies in daily communication traffic. Research results show that the ML methods used can significantly improve the spectrum sensing performance if the input training data set is carefully chosen. The input data sets with ED decisions and energy values have been examined, and advantages and disadvantages of their real-life application have been analyzed.

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Weighted Fusion Scheme for Cooperative Spectrum Sensing

Verma

2020

2020 International Conference on Industry 4.0 Technology (I4Tech)

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On the double threshold energy detection-based spectrum sensing over κ-μ fading channel

Cited by 4 publications

References 12 publications

Joint optimization of sampling point and sensing threshold for spectrum sensing

Joint optimization of sampling point and sensing threshold for spectrum sensing

Machine Learning for LTE Energy Detection Performance Improvement

Weighted Fusion Scheme for Cooperative Spectrum Sensing

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