Energy Detection of Unknown Signals in Fading and Diversity Reception

Herath, Sanjeewa P.; Rajatheva, Nandana; Tellambura, Chintha

doi:10.1109/tcomm.2011.071111.090349

Cited by 190 publications

(159 citation statements)

References 18 publications

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“…This causes a cascading phenomenon that affects spectrum sensing, analysis and decision. Energy detection is the most widely used spectrum sensing technique due to its simplicity and low computational overhead [52], [85]. Energy detection is most susceptible to PUEAs due to its poor performance in environments with low SNR.…”

Section: A Attacks Against Cognitive Radio Network and Detection Tementioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

137

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Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

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Section: A Attacks Against Cognitive Radio Network and Detection Tementioning

confidence: 99%

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Chiwewe

Mbuya

Hancke

2015

IEEE Trans. Ind. Inf.

137

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“…The problem analysis is further extended to include various receive diversity schemes in [14]. In [15], comprehensive performance analysis of the energy detector over fading channels with single antenna reception and with antenna diversity has been developed. The problem of PU detection using ED technique in AWGN channel can be presented in the form of following binary hypothesis testing problem.…”

Section: Energy Detection For Single Antenna Casementioning

confidence: 99%

Exploiting Polarization for Spectrum Awareness in Cognitive Satellite Communications

Sharma

Chatzinotas

Ottersten

2015

Advances in Wireless Technologies and Telecommunication

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Abstract-Exploring new techniques for spectrum sensing (SS), which can detect the weak primary signals instantly, has been an important research challenge. In this paper, the problem of enhancing SS efficiency in cognitive SatComs has been considered. The analysis of different combining techniques has been carried out for SS using dual polarized antenna. Furthermore, polarization states of received signals are exploited and based on obtained polarization states, Optimum Polarization Based Combining (OPBC) technique has been used for carrying out SS in the satellite terminal. The sensing performance of OPBC technique has been compared to selection combining (SC), equal gain combining (EGC) and maximum ratio combining (MRC) techniques. The simulation results show that OPBC technique achieves a great improvement in sensing efficiency over other considered techniques at the expense of complexity in a dual polarized AWGN channel.

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“…This is because secondary users generally have very limited knowledge about the whole spectrum, which may leave the spectrum sensing results far from accurate. Some existing spectrum sensing methods in the literature are by way of matched filtering, waveform-based sensing [2], cyclostationary-based sensing [3,4], eigenvalue-based method [5,6], energy detection [7][8][9][10][11][12][13], etc. Obviously, energy detection is the most popular and simple way for spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

“…The energy detection method [7][8][9][10][11][12][13] for spectrum sensing measures the average energy of the total received signal during a period of time and compares it with a properly assigned threshold to decide the presence or absence of users. Typically, the energy detection method is formulated in a binary hypothesis test with a null Hypothesis H 0 for absence of users and an alternative Hypothesis H 1 for presence.…”

Section: Introductionmentioning

confidence: 99%

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Fundamental Limitations in Energy Detection for Spectrum Sensing

Peng

2018

JSAN

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Abstract:A key enabler for Cognitive Radio (CR) is spectrum sensing, which is physically implemented by sensor and actuator networks typically using the popular energy detection method. The threshold of the binary hypothesis for energy detection is generally determined by using the principles of constant false alarm rate (CFAR) or constant detection rate (CDR). The CDR principle guarantees the CR primary users at a designated low level of interferences, which is nonetheless subject to low spectrum usability of secondary users in a given sensing latency. On the other hand, the CFAR principle ensures secondary users' spectrum utilization at a designated high level, while may nonetheless lead to a high level of interference to the primary users. The paper introduces a novel framework of energy detection for CR spectrum sensing, aiming to initiate a graceful compromise between the two reported principles. The proposed framework takes advantage of the summation of the false alarm probability P f a from CFAR and the missed detection probability (1 − P d ) from CDR, which is further compared with a predetermined confidence level. Optimization presentations for the proposed framework to determine some key parameters are developed and analyzed. We identify two fundamental limitations that appear in spectrum sensing, which further define the relationship among the sample data size for detection, detection time, and signal-to-noise ratio (SNR). We claim that the proposed framework of energy detection yields merits in practical policymaking for detection time and design sample rate on specific channels to achieve better efficiency and less interferences.

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Energy Detection of Unknown Signals in Fading and Diversity Reception

Cited by 190 publications

References 18 publications

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview

Exploiting Polarization for Spectrum Awareness in Cognitive Satellite Communications

Fundamental Limitations in Energy Detection for Spectrum Sensing

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