Simple counting rule for optimal data fusion

Mansouri, N.; Fathi, Madjid

doi:10.1109/cca.2003.1223179

Cited by 20 publications

(11 citation statements)

References 11 publications

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“…The majority logic rule is considered for equal-weight combining, as suggested in [4]. For the optimal combining, we consider two cases: the ideal case when the fusion center knows all the required probabilities and the case when the counting method in [6] is applied to estimate the probabilities. The duration of H 1 period and H 0 period are assumed to follow geometric distribution with mean of 150 and 200 samples, respectively [7].…”

Section: Numerical Resultsmentioning

confidence: 99%

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Cooperative spectrum sensing based on adaptive weighting for a cognitive radio system

Shin

2011

IEICE Electron. Express

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Abstract:We propose a combining scheme for hard decisions of secondary users to improve the performance of cooperative spectrum sensing in a cognitive radio system. In contrast to the conventional equal-weight combining, the proposed scheme assigns unequal weights to different users to form the global decision statistics. Specifically, the combining weights are updated adaptively such that a higher weight is given to the decision of a more reliable user. In order to update the weights, the fusion center estimates the reliability of each user based on the past recode of the user's local decisions. Numerical results show that the proposed scheme outperforms the equal-weight scheme and optimal scheme with counting method, especially when the channels from the primary transmitter to the secondary users are highly disparate.

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Section: Numerical Resultsmentioning

confidence: 99%

“…Since these are generally unknown to the fusion center, the optimal combining is hard to implement. In [6], a counting method was proposed to estimate the probabilities. However, the counting method may be inaccurate, when the number of data is insufficient and/or the channel is time-varying.…”

Section: Optimal Combiningmentioning

confidence: 99%

Cooperative spectrum sensing based on adaptive weighting for a cognitive radio system

Shin

2011

IEICE Electron. Express

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“…However, to implement this rule, the probabilities of detection and false alarm must be known. In [5] and [6], two iterative algorithms are proposed to estimate the probabilities of detection and false alarm, but both of them respond slowly when the system is changed.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Cooperative Spectrum Sensing Scheme Based on the Optimal Data Fusion Rule

Chen

Wang

2007

2007 4th International Symposium on Wireless Communication Systems

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Spectrum sensing is a key technology in Cognitive Radio (CR). It provides the CR system with available vacant channels list by sensing the spectrum environment over a wide frequency band. However, local sensing by single node will be compromised if the sensing node experiences shadowing or fading effect. Cooperative spectrum sensing is derived to solve this problem. In this paper, we propose an adaptive cooperative spectrum sensing scheme by using the optimal data fusion rule. To implement such a rule, the probabilities of detection and false alarm of each sensing node must be known, but these probabilities are not readily available in practice. So we develop an iterative algorithm to estimate the probabilities of detection and false alarm. Two different methods are also developed to enhance the adaptability of the proposed estimating algorithm in time-varying environment. Simulation results show that the proposed scheme can approach the ideal optimal performance but do not need any prior information of the Primary Users' (PU) signal.

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“…The P di and P f i of each SU are estimated by the spectrum coordinator using the counting rule by comparing the local sensing results with global or fused results [21].…”

Section: P DImentioning

confidence: 99%

Evolutionary Game Theory‐Based Collaborative Sensing Model in Emergency CRAHNs

Gvk

Bapat

2012

Journal of Electrical and Computer Engineering

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Game theory has been a tool of choice for modeling dynamic interactions between autonomous systems. Cognitive radio ad hoc networks (CRAHNs) constituted of autonomous wireless nodes are a natural fit for game theory-based modeling. The game theory-based model is particularly suitable for "collaborative spectrum sensing" where each cognitive radio senses the spectrum and shares the results with other nodes such that the targeted sensing accuracy is achieved. Spectrum sensing in CRAHNs, especially when used in emergency scenarios such as disaster management and military applications, needs to be not only accurate and resource efficient, but also adaptive to the changing number of users as well as signal-to-noise ratios. In addition, spectrum sensing mechanism must also be proactive, fair, and tolerant to security attacks. Existing work in collaborative spectrum sensing has mostly been confined to resource efficiency in static systems using request-based reactive sensing resulting in high latencies. In this paper, evolutionary game theory (EGT) is used to model the behavior of the emergency CRAHNS, providing an efficient model for collaborative spectrum sensing. The resulting implementation model is adaptive to the changes in its environment such as signal-to-noise ratio and number of users in the network. The analytical and simulation models presented validate the system design and the desired performance.

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Simple counting rule for optimal data fusion

Cited by 20 publications

References 11 publications

Cooperative spectrum sensing based on adaptive weighting for a cognitive radio system

Cooperative spectrum sensing based on adaptive weighting for a cognitive radio system

An Adaptive Cooperative Spectrum Sensing Scheme Based on the Optimal Data Fusion Rule

Evolutionary Game Theory‐Based Collaborative Sensing Model in Emergency CRAHNs

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