On the Energy Consumption of the Decision-Fusion Rules in Cognitive Radio Networks

Althunibat, Saud; Narayanan, S.; Renzo, Marco Di; Granelli, Fabrizio

doi:10.1109/camad.2012.6335312

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…if u k = +1, then the CU decides that the spectrum is used by a licensed user. Otherwise, the spectrum is identified as unused by the k th CU [9].…”

Section: System Modelmentioning

confidence: 99%

“…Energy efficiency is defined as the average successfully delivered data divided by the average energy consumed, as follows [9]:…”

Section: System Modelmentioning

confidence: 99%

“…Thus, its sensing performance can be expressed as P dk = 1 − P d h k and P f k = 1 − P the corresponding values if the CU is honest. Reflecting this to (7), (8),(9) and (10), we can get that the assigned values for a malicious attacker are approximately equal to α k = 1 − α h k and β k = 1 − β h k , where α h k and β h k…”

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confidence: 96%

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Robust Algorithm against Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks

Althunibat

Renzo

Granelli

2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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One of the main challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRN) is spectrum sensing falsification (SSDF) attack. A SSDF attack consists in a cognitive user providing false data about the spectrum status. SSDF attack can hugely degrade the achievable detection accuracy and energy efficiency of CRNs. In this paper, a robust CSS algorithm against SSDF attack is proposed. The proposed algorithm assigns a specific weight to each user, which is able to (i) completely eliminate the resulting effects on CSS caused by many types of SSDF attacks, (ii) convert some types of SSDF attacks to be honest users, and (iii) alleviate the influence of other honest users that suffer from poor sensing performance or/and very noisy reporting channels. Simulation results show that, compared to many previous works, a significant improvement in detection accuracy and energy efficiency can be attained by the proposed algorithm.

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“…if u k = +1, then the CU decides that the spectrum is used by a licensed user. Otherwise, the spectrum is identified as unused by the k th CU [9].…”

Section: System Modelmentioning

confidence: 99%

“…Energy efficiency is defined as the average successfully delivered data divided by the average energy consumed, as follows [9]:…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Robust Algorithm against Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks

Althunibat

Renzo

Granelli

2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

Self Cite

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show abstract

“…We attempt to optimize the parameters which affect the energy consumption and the throughput of the cooperative CRN such as sensing time, sensing threshold, and the number of cooperative SUs. Some recent works dealing with the issues of CRN energy efficiency have been presented in [11][12][13][14][15]. In [11], the authors studied a joint design of energy-efficient sensing and transmission duration for a CRN in which the PU was protected.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the authors identified the sensing-access strategies and the sensing order that achieve the maximum energy efficiency. In [15], a comparison among different decision-fusion rules was studied in terms of the consumed energy and the achievable detection probability at a given false alarm probability threshold.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy-efficient sensing in cooperative cognitive radio networks

Chen

et al. 2014

J Wireless Com Network

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In a cognitive radio network (CRN), when the secondary users (SUs) are battery-powered devices, the concept of energy-efficient design is very important. The sensing time and the number of cooperative SUs in the cooperative spectrum sensing could greatly affect energy consumption and throughput of the system. In this paper, we propose an energy utility function by formulating the achievable data rate of a cooperative CRN in terms of the energy consumption by the CRN. The maximization of the energy utility function is obtained by jointly designing the sensing time, sensing threshold, and number of cooperative SUs with the constraint of sufficient protection for primary user (PU). Numerical results show that the CRN can achieve almost the maximum achievable data rate with significant energy saving through the joint optimization.

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Robust spectrum sensing algorithm based on free probability theory

Wang

Chen

Jiang

et al. 2015

Wireless Communications

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In low signal-to-noise ratio (SNR) cases, the performance of spectrum sensing algorithms cannot meet the practical needs, which is a major problem faced by spectrum sensing technology in current cognitive radio field. Now, existing algorithms based on random matrix theory (RMT) have high sensing performance, but they require a large number of samples, which are very difficult to satisfy in practice. Free probability theory (FPT) is a main branch of RMT. It describes the asymptotic behavior of large random matrices and portrays a strong link between two matrices and their sum or product matrices. FPT can also be utilized to the digital communication system that can be modeled by random matrices and has been applied to spectrum sensing in simplified ideal channels, for example, additive white Gaussian noise channel. The most pivotal issue and difficulty of the FPT-based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In this paper, FPT-based spectrum sensing schemes are proposed for some typical wireless communication systems, such as multiple-input multiple-output system, Rayleigh multipath fading system, and orthogonal frequency division multiplexing system. It is shown that the asymptotic freeness behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for these typical systems with low SNR and very limited samples. Simulation results demonstrate that compared with the existing RMT-based spectrum detection methods, for example, the maximum and minimum eigenvalue detectors, the proposed FPT-based schemes offer superior detection performance and are more robust to low SNR cases, especially for a small sample of observations. Robust spectrum sensing algorithm based on free probability theory L. Wang et al. Free probability theory (FPT) [27-31] is a valuable toolfor describing the asymptotic behavior of random matrices. It has grown into a main branch of RMT through the pioneering work of Voiculescu in the 1980s [32,33]. FPT portrays a strong link between two matrices and their sum matrix or product matrix, which is completely different from other branches of RMT. For the purpose of spectrum sensing, it can separate the eigenvalues of real signal matrix from that of the received sample covariance matrix, which includes both signal and noise matrices. This is almost impossible in traditional mathematics. The most pivotal issue of the FPT-based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In some simplified communication environment, FPT has been managed to utilize in the field of spectrum sensing [34,35]. However, for some typical wireless communication scenarios, such as multiple-input multiple-output (MIMO) Rayleigh channels, MIMO multipath channels, and MIMO-orthogonal frequency division multiplexing (OFDM) channels, it needs to be further studied.

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On the Energy Consumption of the Decision-Fusion Rules in Cognitive Radio Networks

Cited by 14 publications

References 13 publications

Robust Algorithm against Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks

Robust Algorithm against Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks

Optimal energy-efficient sensing in cooperative cognitive radio networks

Robust spectrum sensing algorithm based on free probability theory

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