Maritime cognitive radio spectrum sensing based on multi-antenna cyclostationary feature detection

Zhang, Jingbo; Ran, Feng; Li, Da

doi:10.1080/00207217.2019.1692373

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…where: ( ) is the received signal by ULU ( ) is the LU transmitting signal ℎ is the channel gain ( ) is AWGN present From Equations (2) and 3, four different possible cases that can occur in signal detection are defined: firstly, Probability of Detection (PD) which is declaring H1 when LU signal is actually present and Secondly, Probability of Missing (PM) which describes declaring idleness of spectrum when LU signal is present. Probability of False Alarm (PFA) is the third one that describes the occupancy of the spectrum when LU is not actually present and the fourth one is declaring 0 when LU is not present.…”

Section: A Spectrum Sensing In Cognitive Radiomentioning

confidence: 99%

“…Previous researches have shown that spectrum scarcity is not only limited to insufficient spectrum but also to poor utilization of the allocated spectrum. In 1999, Mitola proposed a technique known as Cognitive Radio Networks (CRNs) that overcome spectrum scarcity [1,2]. Cognitive Radio (CR) is a WC technique in which a transceiver intelligently senses the spectral environment over a wide frequency band to detect unused spectrum and provide wireless links through the unused spectrum [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement of Equal Gain Combiner for Detection of Spectrum Hole in a Cognitive Radio System

Ojo¹,

Adeyemo²,

Ojo³

et al. 2020

ujeee

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Section: A Spectrum Sensing In Cognitive Radiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Equal Gain Combiner for Detection of Spectrum Hole in a Cognitive Radio System

Ojo¹,

Adeyemo²,

Ojo³

et al. 2020

ujeee

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“…The wideband sensing methods focus on analysing the number of frequencies at a time whereas narrowband methods focus on analysing one frequency channel at a time. several narrowband spectrum sensing methods have been introduced such as energy detection [5,6], matched filter detection [7], cyclostationary feature detection [8,9], covariance based detection [10,11], and machine learning based sensing approach [12], etc. The energy detection method relies on power of incoming signal and compares it to previously estimated threshold to estimate the presence of PUs.…”

Section: Introductionmentioning

confidence: 99%

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

M A,

C R

2023

JAIT

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Over the past two decades, communication technologies have advanced significantly, but the growing use of various communication methods has led to a shortage of available spectrum. Cognitive Radio (CR) emerges as a solution to this challenge. A crucial aspect of CR is spectrum sensing, which detects available spectrum gaps. However, current spectrum sensing methods have limitations, including insufficient signal representation, inefficiency, and sensitivity to noise. To address these issues, this study embraces a deep learning approach and introduces an innovative spectrum detection architecture for cognitive radio networks. The method combines deep learning and reinforcement learning, leveraging deep learning for energy and energy correlation feature extraction. Additionally, a Recurrent Neural Network (RNN) module is used to capture time-shifted signal correlation. To enhance feature extraction, Short-Time Fourier Transform (STFT) feature extraction is incorporated. The combined feature vector is processed through a reinforcement learning module. Finally, these features are used to train the deep learning classifier which uses residual blocks for better representation of feature while learning. The highest prediction score is considered as the decision threshold in this work. The outcome of this work is compared with other deep learning methods in terms of 𝑷 𝒅 , 𝑷 𝒇 and sensing error for varied sample size and modulation schemes. The comparative analysis shows the robustness of proposed approach by achieving the 𝑷 𝒇 as 0.32 and 0.06 for QAM16 and QPSK modulations.

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“…The effect of this process is a fluctuation of the PU signal, which could be so severe and resulting to a low signal below the sensitivity of SU. This effect causes poor signal reception, making PU difficult to sense [17]- [19]. If SU is unable to detect PU due to an obstruction, the likelihood of interference to the PU rises.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network

Okediran

Ojo

et al. 2022

IJEECS

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Spectrum Sensing (SS) is cognitive radio's (CR’s) fundamental and essential mechanism to locate idle spectrum. Multipath effects in wireless channels, on the other hand, reduce CR's sensing accuracy, resulting in a significant risk of missing data. Optimal Spectrum Sensing Technique with Energy Harvesting (OSSTEH) used to solve this problem suffers from poor performance due to only one Primary User (PU) branch used. Hence, in this paper, an enhanced SS with energy harvesting technique is proposed using Maximal Ratio Combiner (MRC). The multiple copies of the PU signal were received using 'N' number of secondary users (SU) antennas and divided into two equal fractions. The first fraction was used for spectrum sensing, while the latter was used for energy harvesting. The first set of SU antennas received a PU signal, which was combined using MRC, and the output of the combiner was used as an input to the energy detector to calculate the received signal's energy. To assess the existence or absence of the PU signal, the acquired energy was compared to the threshold limit. The enhanced technique was evaluated using the probability of detection and signal strength compared with conventional OSSTEH. The enhanced technique gave a high detection rate with an increase in charging rate than the conventional OSSTEH.

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Maritime cognitive radio spectrum sensing based on multi-antenna cyclostationary feature detection

Cited by 4 publications

References 18 publications

Enhancement of Equal Gain Combiner for Detection of Spectrum Hole in a Cognitive Radio System

Enhancement of Equal Gain Combiner for Detection of Spectrum Hole in a Cognitive Radio System

DRLNet: A Deep Reinforcement Learning Network for Hybrid Features Extraction and Spectrum Sensing in Cognitive Radio Networks

Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network

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