Effective Energy Detection for IoT Systems Against Noise Uncertainty at Low SNR

Yao, Junteng; Jin, Ming; Guo, Qinghua; Li, Yonghui; Xi, Jiangtao

doi:10.1109/jiot.2018.2877698

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…The higher order statistics, including third order statistics and fourth order statistics, are utilized for sensing OFDM transmitted signals, where the noise uncertainty cases are not considered for OFDM sensing [21]. Although some recent works considered spectrum sensing in noise uncertainty environments, the sensing performance is not very good in severe noise environments [22], [23]. However, the sensing of OFDM signals in noise uncertainty environments for low SNR cases is very important [23].…”

Section: -Introductionmentioning

confidence: 99%

“…Although some recent works considered spectrum sensing in noise uncertainty environments, the sensing performance is not very good in severe noise environments [22], [23]. However, the sensing of OFDM signals in noise uncertainty environments for low SNR cases is very important [23]. For these reasons, the major limitations of the existing spectrum-sensing methods result in their poor spectrum sensing performance for low SNR cases in noise uncertainty environments.…”

Section: -Introductionmentioning

confidence: 99%

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Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

HAQUE,

Shimamura

2023

jist

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Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments.

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Section: -Introductionmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

HAQUE,

Shimamura

2023

jist

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“…With the emergence and development of the concept of industry 4.0, Internet of things (IoT) [1]- [3] becomes the fundamental of 'Smart Cites' [2], as all the terrestrial objects are connected and information is shared [4]- [11]. Since 70 percent of the earth is covered with water, Internet of Underwater Things (IoUT) has a prosperous future and the 'Smart Oceans' [4], [12]- [19] is the direction. Challenges of IoUT have been shown in data acquisition, data transmission and data processing [20], wherein the first two terms are different from those of terrestrial IoT and have attracted more attention in the design of IoUT.…”

Section: Introductionmentioning

confidence: 99%

Acoustical Observation With Multiple Wave Gliders for Internet of Underwater Things

Lan

Jin

et al. 2021

IEEE Internet Things J.

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One of the challenges of Internet of Underwater Things (IoUT) is the design of nodes for collecting information from underwater, with features of low cost, long-term, long range, voyage routing, and real-time communications. Wave glider, has shown great potential acting as IoUT nodes through its persistent, long range travelling, and flexibility underwater. In this paper, we propose an architecture of Internet of Underwater Things, involving multiple wave gliders as nodes for acoustical observation. We present target localisation method via acoustical observation of nodes with multiple wave gliders deployed underwater, by which precision of bearing estimation of each node is required to achieve high precision of localisation. With the data collected, we apply a compensation method of bearing estimate when the hydrophone array is rotating during the observation. The feasibility of acoustical observation of wave gliders has been validated through both simulation and sea trial data, which is of great potential to be nodes for constructing IoUT.

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“…It does not require any prior information of the PU signal, and has low computational complexity. To improve the poor spectrum detection performance, some modified energy detection algorithms have been proposed [4], [5]. However, the spectrum sensing performance of the energy detection algorithm is poor under a low SNR and noise fluctuation.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Peaks-Based Spectrum Sensing Algorithm for Detecting Modulated Signals

Zhang

et al. 2020

IEEE Access

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Cognitive radio is considered an effective solution to spectrum shortages, which has been a significant issue in the next generation of wireless communications. In this paper, we focus on spectrum sensing under a low signal-to-noise-ratio (SNR) and noise fluctuation and propose a symmetric peaksbased spectrum sensing algorithm for modulated signals. First, we analyze the characteristics of the cyclic autocorrelation function of modulated signals, and construct a detection domain for detecting primary users based on the characteristics of the cyclic autocorrelation function of primary signals. Then, we introduce the significance level factor into the spectrum sensing, and develop a symmetric peaks criterion. Following this criterion, we propose a symmetric peaks-based spectrum sensing algorithm. Finally, we give the probabilities of detection and false alarm of the spectrum sensing algorithm, discuss the effect of the significance level factor on the spectrum sensing performance, and compare the complexity of the algorithm with that of other algorithms. The spectrum sensing algorithm proposed does not require any prior knowledge of primary user signals or noise in the systems, and can sense modulated signals under very low SNR. Simulation results are provided to verify the performance of the algorithm proposed under a low SNR and noise fluctuation. Compared with the maximum and minimum eigenvalue (MME) algorithm, frequency domain autocorrelation-based (FD-AC) algorithm and statistical knowledge autocorrelation-based (SKAB) algorithm, it improves about 4 dB margin in SNR.

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Effective Energy Detection for IoT Systems Against Noise Uncertainty at Low SNR

Cited by 13 publications

References 28 publications

Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

Acoustical Observation With Multiple Wave Gliders for Internet of Underwater Things

Symmetric Peaks-Based Spectrum Sensing Algorithm for Detecting Modulated Signals

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