Experimental study of spectrum sensing based on energy detection and network cooperation

Čabrić, Danijela; Tkachenko, A.; Brodersen, R.W.

doi:10.1145/1234388.1234400

Cited by 364 publications

(178 citation statements)

References 10 publications

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“…Such an assumption still allows us to gain valuable insight into the design of censoring and sleeping parameters. This has also been considered in [25] which presents an experimental study of cooperative spectrum sensing where the received SNR at each cognitive radio is assumed to be the same and it is shown that cooperative sensing still improves the detection performance of the cognitive network. Following this assumption, the probabilities of false alarm and detection for each radio are the same, denoted, respectively, by and .…”

Section: System Modelmentioning

confidence: 99%

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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Abstract-Reliability and energy consumption in detection are key objectives for distributed spectrum sensing in cognitive sensor networks. In conventional distributed sensing approaches, although the detection performance improves with the number of radios, so does the network energy consumption. We consider a combined sleeping and censoring scheme as an energy efficient spectrum sensing technique for cognitive sensor networks. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on the detection performance, by optimally choosing the sleeping and censoring design parameters. The constraint on the detection performance is given by a minimum target probability of detection and a maximum permissible probability of false alarm. Depending on the availability of prior knowledge about the probability of primary user presence, two cases are considered. The case where a priori knowledge is not available defines the blind setup; otherwise the setup is called knowledge-aided. By considering a sensor network based on IEEE 802.15.4/ZigBee radios, we show that significant energy savings can be achieved by the proposed scheme.Index Terms-Cognitive sensor networks, detection and fusion performance, distributed spectrum sensing.

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Section: System Modelmentioning

confidence: 99%

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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“…For a large number of samples, we can use the central limit theorem to approximate the test statistic as Gaussian [2]:…”

Section: System Modelmentioning

confidence: 99%

Optimal hard fusion strategies for cognitive radio networks

Maleki

Chepuri

Leus

2011

2011 IEEE Wireless Communications and Networking Conference

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Abstract-Optimization of hard fusion spectrum sensing using the k-out-of-N rule is considered. Two different setups are used to derive the optimal k. A throughput optimization setup is defined by minimizing the probability of false alarm subject to a probability of detection constraint representing the interference of a cognitive radio with the primary user, and an interference management setup is considered by maximizing the probability of detection subject to a false alarm rate constraint. It is shown that the underlying problems can be simplified to equality constrained optimization problems and an algorithm to solve them is presented. We show the throughput optimization and interference management setups are dual. The simulation results show the majority rule is optimal or near optimal for the desirable range of false alarm and detection rates for a cognitive radio network. Furthermore, an energy efficient setup is considered where the number of cognitive radios is to be minimized for the AND and the OR rule and a certain probability of detection and false alarm constraint. The simulation results show that the OR rule outperforms the AND rule in terms of energy efficiency.

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“…In false alarm, the SS declares PU is present when the spectrum is empty. In detection, the SU correctly declares a PU is using the spectrum [8]. The performance of sensing detection is thus measured through the probability of these two events.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Sensing and Determination of Optimum Threshold in Cognitive Radio System

Abdullahi¹,

Sani²,

Muazu³

2015

IJCA

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The challenges associated with the determination of detection threshold due to uncertainty was addressed in this paper. The technique of Cooperative Sensing Spectrum (CSS) was used to model the functional subsystems that made up the Cognitive Radio System. The model developed utilizes channel centre frequencies. Also, implemented and threshold actualized for the presence/absence of PU based on false alarm and detection decisions. The innovative approach employed the use of FLA for the improvement of the detection threshold. This was used to fine tune and determine the optimum threshold using the hypothesis H 0 and H 1 .

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Experimental study of spectrum sensing based on energy detection and network cooperation

Cited by 364 publications

References 10 publications

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Optimal hard fusion strategies for cognitive radio networks

Cooperative Spectrum Sensing and Determination of Optimum Threshold in Cognitive Radio System

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