Relevant bibliographies by topics / Optimum decision fusion rule

Contents

  1. Journal articles

Academic literature on the topic 'Optimum decision fusion rule'

Author: Grafiati
Published: 14 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Optimum decision fusion rule.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Optimum decision fusion rule"

1

Arshad, Kamran, Muhammad Ali Imran, and Klaus Moessner. "Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/424036.

Full text
Abstract:
The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.
APA, Harvard, Vancouver, ISO, and other styles
2

Rauniyar, Ashish, Jae Min Jang, and Soo Young Shin. "Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks." Journal of Advances in Computer Networks 3, no. 3 (2015): 207–12. http://dx.doi.org/10.7763/jacn.2015.v3.168.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Saleh, Ibrahim Ahmed, Omar Ibrahim Alsaif, and Maan A. Yahya. "Optimal distributed decision in wireless sensor network using gray wolf optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 646. http://dx.doi.org/10.11591/ijai.v9.i4.pp646-654.

Full text
Abstract:
<p class="Default">The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.</p>
APA, Harvard, Vancouver, ISO, and other styles
4

Khan, Muhammad Sajjad, Noor Gul, Junsu Kim, Ijaz Mansoor Qureshi, and Su Min Kim. "A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users." Wireless Communications and Mobile Computing 2020 (January 31, 2020): 1–10. http://dx.doi.org/10.1155/2020/2509081.

Full text
Abstract:
Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.
APA, Harvard, Vancouver, ISO, and other styles
5

Liao, Yiwei, Xiaojing Shen, and Hang Rao. "Analytic Sensor Rules for Optimal Distributed Decision Given $K$-Out-of-$L$ Fusion Rule Under Monte Carlo Approximation." IEEE Transactions on Automatic Control 65, no. 12 (2020): 5488–95. http://dx.doi.org/10.1109/tac.2020.2977890.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Usman, Muhammad, and Insoo Koo. "Secure Cooperative Spectrum Sensing for the Cognitive Radio Network Using Nonuniform Reliability." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/101809.

Full text
Abstract:
Both reliable detection of the primary signal in a noisy and fading environment and nullifying the effect of unauthorized users are important tasks in cognitive radio networks. To address these issues, we consider a cooperative spectrum sensing approach where each user is assigned nonuniform reliability based on the sensing performance. Users with poor channel or faulty sensor are assigned low reliability. The nonuniform reliabilities serve as identification tags and are used to isolate users with malicious behavior. We consider a link layer attack similar to the Byzantine attack, which falsifies the spectrum sensing data. Three different strategies are presented in this paper to ignore unreliable and malicious users in the network. Considering only reliable users for global decision improves sensing time and decreases collisions in the control channel. The fusion center uses the degree of reliability as a weighting factor to determine the global decision in scheme I. Schemes II and III consider the unreliability of users, which makes the computations even simpler. The proposed schemes reduce the number of sensing reports and increase the inference accuracy. The advantages of our proposed schemes over conventional cooperative spectrum sensing and the Chair-Varshney optimum rule are demonstrated through simulations.
APA, Harvard, Vancouver, ISO, and other styles
7

Zhu, Yunmin, and Xiaorong Li. "Optimal decision fusion given sensor rules." Journal of Control Theory and Applications 3, no. 1 (2005): 47–54. http://dx.doi.org/10.1007/s11768-005-0060-z.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yuan, Xiao Guang, Dong Zhu Feng, Jian Deng, and Yuan Jie Bai. "Resource-Constrained Wireless Sensor Network Information Decision Fusion in Ocean Environment." Applied Mechanics and Materials 433-435 (October 2013): 229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.229.

Full text
Abstract:
In order to solve the decision information fusion issues of resource-constrained wireless sensor network, several decision information fusion rules under exponential distribution fading channel are investigated in this paper. At first, optimal likelihood ratio rule is given. The detection performance of this fusion rule is best, however, this rule acquires channel information which is too costly for resource constrained sensor networks. To solve this problem, suboptimal likelihood ratio fusion rule is proposed which requires only the knowledge of channel statistics. In addition, the reduced forms of the suboptimal are also derived, in the case of extreme channel signal-to-noise ratio (SNR). Theoretical analysis and simulations show that suboptimal fusion rule needs much less computation and information, yet exhibits only slight performance degradation. Suboptimal fusion rules are practicable for resource constrained wireless sensor networks decision information fusion system working in ocean environment.
APA, Harvard, Vancouver, ISO, and other styles
9

Zhu, Yunmin. "Optimum Fusion in Distributed Multisensor Network Decision Systems." IFAC Proceedings Volumes 32, no. 2 (1999): 8321–26. http://dx.doi.org/10.1016/s1474-6670(17)57419-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Liu, Shoujun, Kehao Wang, Kezhong Liu, and Wei Chen. "Noncoherent Decision Fusion over Fading Hybrid MACs in Wireless Sensor Networks." Sensors 19, no. 1 (2019): 120. http://dx.doi.org/10.3390/s19010120.

Full text
Abstract:
In this paper, we consider the problem of decision fusion for noncoherent detection in a wireless sensor network. Novel to the current work is the integration of the hybrid multi-access channel (MAC) in the fusion rule design. We assume that sensors transmit their local binary decisions over a hybrid MAC which is a composite of conventional orthogonal and nonorthogonal MACs. Under Rayleigh fading scenario, we present a likelihood ratio (LR)-based fusion rule, which has been shown to be optimal through theoretical analysis and simulation. However, it requires a large amount of computation, which is not easily implemented in resource-constrained sensor networks. Therefore, three sub-optimal alternatives with low-complexity are proposed, namely the weighed energy detector (WED), the deflection-coefficient-maximization (DCM), and the two-step (TS) rules. We show that when the channel signal-to-noise ratio (SNR) is low, the LR-based fusion rule reduces to the WED rule; at high-channel SNR, it is equivalent to the TS rule; and at moderate-channel SNR, it can be approached closely by the DCM rule. Compared with the conventional orthogonal and nonorthogonal MACs, numerical results show that the hybrid MAC with the proposed fusion rules can improve the detection performance when the channel SNR is medium.
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Optimum decision fusion rule' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography