Nhan Nguyen-Thanh

Evidence-Theory-Based Cooperative Spectrum Sensing With Efficient Quantization Method in Cognitive Radio

Sensing spectra in a reliable and efficient manner is fundamental to cognitive radio (CR). Ensuring cooperation among spectrum sensing devices is an appropriate method when a CR system is under deep shadowing and in a fading environment. In this paper, an enhanced scheme for cooperative spectrum sensing (CSS) based on efficient quantization and the Dempster-Shafer (D-S) theory of evidence is proposed. The proposed scheme includes an effective quantizer for the sensing data by utilizing special properties of the hypothesis distribution under different signal-to-noise ratios (SNRs) of the primary signal. As a result, the required bandwidth for the reporting channel is reduced while the advantage for combinations of the D-S theory is maintained. Simulation results revealed that significant improvements in the CSS gain, as well as a reduction in the system overhead, were achieved.

Log-likelihood Ratio Optimal Quantizer for Cooperative Spectrum Sensing in Cognitive Radio

In order to reduce the bandwidth requirement for reporting channel, the quantization of sensing datas log-likelihood ratio (LLR) in cooperative spectrum sensing is considered. A well-preserved quantizer which minimizes mean square error of the sensing datas LLR at quantizer output is achieved by applying the well-known Lloyd-Max quantization algorithm. The probability density function of the LLR of the sensing information is also formulated for designing the quantizer. Simulation results reveal that the proposed quantizer provides the same sensing performance of the original LLR test with only few quantization bits.

A cluster-based selective cooperative spectrum sensing scheme in cognitive radio

Developing an effective cooperative spectrum sensing (CSS) scheme in cognitive radio (CR), which is considered as promising system for enhancing spectrum utilization, is necessary. In this paper, a cluster-based optimal selective CSS scheme is proposed for reducing reporting time and bandwidth while maintaining a certain level of sensing performance. Clusters are organized based on the identification of primary signal signal-to-noise ratio value, and the cluster head in each cluster is dynamically chosen according to the sensing data qualities of CR users. The cluster sensing decision is made based on an optimal threshold for selective CSS which minimizes the probability of sensing error. A parallel reporting mechanism based on frequency division is proposed to considerably reduce the time for reporting decision to fusion center of clusters. In the fusion center, the optimal Chair-Vashney rule is utilized to obtain a high sensing performance based on the available cluster’s information.

Optimal Truncated Ordered Sequential Cooperative Spectrum Sensing in Cognitive Radio

The cost for increasing the accuracy of cooperative spectrum sensing in cognitive radio requires a long time for collecting sensing data. Sequential fusion, in which the sensing data reports are sequentially collected, combined, and tested with two thresholds for deciding to wait for the next data report or concluding the presence or absence of the primary signal, is considered to reduce the collecting time. A further reduction in the average sequential report number (ASN) can be achieved by arranging the reports in descending order of data quality where the better sensing data is reported earlier. In this paper, the ASN and the error probability of the ordered sequential fusion are analytically calculated based on the direct method. The results are then adopted to design an optimal method for obtaining the optimal sequential thresholds and truncated point. The optimal truncated point enables one to minimize the ASN by discarding the sensing data with low quality, while the optimal sequential thresholds ensure the maintaining of the sensing performance of sequential fusion be the same level to that of the conventional parallel fusion.

How Many Bits Should Be Reported In Quantized Cooperative Spectrum Sensing

We introduce an algorithm for optimizing sensing parameters including the number of sensing samples and the number of reporting bits of a quantization-based cooperative spectrum sensing scheme in cognitive radio networks. This is obtained by maximizing the network throughput subject to a target detection probability. With Rayleigh fading and energy detector, the proposed algorithm simultaneously optimizes the number of sensing samples at a local node, the number of bits for quantizing local sensing data and the global threshold at a fusion center.

Comments and Corrections Comments on "Spectrum Sensing in Cognitive Radio Using Goodness-of-Fit Testing"

In this paper, we verify goodness-of-fit testing through the use of the Anderson-Darling (AD) test [1] for spectrum sensing in cognitive radio. In [1], it was shown that spectrum sensing based on the AD test outperforms the energy detection method. However, this positive result can only be obtained when the primary signal is assumed to be static during sensing interval, which is a very rare case in cognitive radio. This assumption reduces the generality of the proposed test in [1]. The verification results of the AD test with some more general and practical primary signals in this paper show that the application of the AD sensing scheme for spectrum sensing in cognitive radio is still a challenge and requires further research.

Extra-sensing game for malicious primary user emulator attack in cognitive radio network

Primary User Emulation (PUE) attack is a serious security problem in cognitive radio (CR) network. A PUE attacker emulates a primary signal during sensing duration in order the CR users not to use the spectrum. The PUE attacker is either selfish if it would like to take benefit of the spectrum, or malicious if it would like to do a Deny of Service of the CR network. In this paper, we only consider malicious PUE. We propose to perform sometimes an additional sensing step, called extra-sensing, in order to have a new opportunity to sense the channel and so to use it. Obviously the malicious PUE may still perform an attack during this extra-sensing. Therefore, our problem can be formulated as a zero-sum game to modeling and analyzing the strategies for two players. The equilibrium is expressed in closed-form. The results show that the benefit ratio and the probability of channels availability strongly influence the equilibrium. Numerical results confirm our claims.

Empirical Distribution-Based Event Detection in Wireless Sensor Networks: An Approach Based on Evidence Theory

Event detection is a central issue in wireless sensor networks. In this paper, we propose a novel event-detection scheme which utilizes empirical distribution and the combination method of evidence theory. Unlike conventional methods where the sensor uses energy detection to determine the presence or absence of an event, our scheme uses a goodness-of-fit (GOF) test to measure the distance between the observed data and the empirical distributions of both the presence and absence hypotheses. Multiple types of such two-sided GOF tests are combined to create a well-adapted detector using evidence theory. The simulation results show that the proposed detector is more accurate than conventional detectors in different kinds of noisy environments.

Attack and surveillance strategies for selfish primary user emulator in cognitive radio network

Primary user emulation (PUE) attack is a serious security problem in cognitive radio (CR) networks. In PUE attack, attacker transmits an emulated primary signal during a spectrum sensing interval to fool the CR system causing a prohibition in the secondary access on the attacked channel. An attacker is called selfish attacker if it performs the PUE attack for its selfish own purpose. Since it is obligate to reveal the users identification in any communication link, a channel surveillance process can help to identify the selfish PUE attacker. In this paper, we formulate a non-zero-sum game with incomplete information for analyzing and modeling the selfish PUE attack and surveillance strategies simultaneously. Nash Equilibrium (NE) is figured out in closed form. The results show that the network demand and the penalty factor strongly influence the NE. Numerical simulations confirm our claims based on our analytic results.

Mitigating Primary Emulation Attacks in Multi-Channel Cognitive Radio Networks: A Surveillance Game

Primary User Emulation Attack (PUEA), in which attackers emulate primary user signals causing restriction of secondary access on the attacked channels, is a serious security problem in Cognitive Radio Networks (CRNs). A user performing a PUEA for selfishly occupying more channels is called a selfish PUEA attacker. Network managers could adopt a surveillance process on disallowed channels for identifying illegal channel occupation of selfish PUEA attackers and hence mitigating selfish PUEA. Determining surveillance strategies, particularly in multichannel context, is necessary for ensuring network operation fairness. In this paper, we formulate a game, called multi-channel surveillance game, between the selfish attack and the surveillance process in multi-channel CRNs. The sequence-form representation method is adopted to determine the Nash Equilibrium (NE) of the game. We show that performing the obtained NE surveillance strategy significantly mitigates selfish PUEA.