Learning and Adaptation in Cognitive Radios Using Neural Networks

Baldo, Nicola; Zorzi, Michele

doi:10.1109/ccnc08.2007.229

Cited by 70 publications

(47 citation statements)

References 14 publications

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“…As we argued in the previous sections and as several other authors [36,68] noticed, "Many CR proposals, such as [61,69,70], rely on a priori characterization of these performance metrics which are often derived from analytical models. Unfortunately, [...], this approach is not always practical due to e.g., limiting modeling assumption, non-ideal behaviors in real-life scenarios, and poor scalability" [68].…”

Section: Learning Approaches: Exploration and Exploitationmentioning

confidence: 99%

“…Unfortunately, [...], this approach is not always practical due to e.g., limiting modeling assumption, non-ideal behaviors in real-life scenarios, and poor scalability" [68]. To avoid these limitations and in order to tackle more realistic scenarios, many methods based on learning techniques were suggested: artificial neuronal networks (ANN), evolving connectionist systems (ECS) [71,72], statistical learning [73], regression models and so on.…”

Section: Learning Approaches: Exploration and Exploitationmentioning

confidence: 99%

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Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Jouini

Moy

Palicot

2012

J Wireless Com Network

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This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.

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Section: Learning Approaches: Exploration and Exploitationmentioning

confidence: 99%

Section: Learning Approaches: Exploration and Exploitationmentioning

confidence: 99%

Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Jouini

Moy

Palicot

2012

J Wireless Com Network

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“…Multilayered Feedback Neural Network (MFNN) [13] is used as an effective technique for real-time characterization of the communication performance and therefore offers some interesting learning capabilities. A distributed cognitive network access scheme is presented in [14], with the objective to provide the best Quality of Service (QoS), with respect to both radio link and core network performance and user application requirements, by using Fuzzy Logic-based techniques.…”

Section: Related Workmentioning

confidence: 99%

Throughput Maximization in Cognitive Radio Networks using Levenberg-Marquardt Algorithm

Roy¹,

Muralidhar²

2015

International Journal of Advanced Research in Computer and Comm

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Abstract:Cognitive radio network is the promising technology of the next generation communication networks which enables the secondary users (SUs) to use the free spectrum bands which are licensed originally to the primary users (PUs) without causing interference and utilize the spectrum more efficiently. Spectrum sensing should be carried out frequently in order to transmit the data successfully through SU without causing significant interference with the PU and to achieve the maximum throughput. In this paper we propose an artificial neural network model which is known as Levenberg-Marquardt (L-M) algorithm for predicting the propagation. The simulation results show that there is a significant gain in the throughput when compared with both random and HMM methods and it explains the superiority of the algorithm. Also, it is found that the L-M algorithm is faster than the other algorithms.

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“…Such a network can be used for solving artificial intelligence problems, e.g., machine learning. A neural network can be used in spectrum sensing, radio parameter adaptive decision and adjustment, and prediction of wireless network performance metrics such as bit error rate, throughput, delay, and so on [17][18][19][20].…”

Section: Supporting Technologiesmentioning

confidence: 99%

Design and implementation of a cognitive engine functional architecture

2012

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Intelligence is the most important characteristic for cognitive wireless networks. A cognitive engine built on reconfigurable wireless networks is the key to implementing this characteristic. The design and implementation of a cognitive engine is important in research on the theory of initiative cognition for cognitive wireless networks. This paper first discusses research on cognitive loops, then investigates cognitive functions in the loop through the design of a universal cognitive engine functional architecture, and finally verifies the architecture on the platform of a cognitive engine prototype system. cognitive radio, cognitive wireless network, cognitive loop, cognitive engine, prototype system "Cognition" is the scientific term for the process of thinking. Mitola et al.[1] introduced "cognition" to wireless communication and coined the phrase "Cognitive Radio" in 1999.From then on, cognitive radio and cognitive networks have attracted increased attention from researchers and research institutions [2][3][4], and have become a hotspot in the communication domain [5]. A cognitive network is able to sense information from the outside world and its internal state, and can then adjust the wireless network (including working frequency, air access, data protocols, and so on) by parsing and orienting the sensing information to adapt to changes in the environment. Moreover, a cognitive network can still learn to form new knowledge. Thus, a cognitive network is an intelligent network that can sense, make decisions, and learn like humans. Implementing such a wireless network requires a functional architecture as the brain to control the whole cognitive process, that is, a cognitive engine. The cognitive engine integrates various cognitive functions (such as sensing, learning, and reasoning) so that it can intelligently control a wireless network to implement a cognitive cycle. Rieser [6], a researcher at Virginia Tech, first modeled the biologically inspired cognitive engine called BioCR. Later, his colleague Rondeau [7] further developed the theory of cognitive engines. He evolved cognitive engine modeling as a multi-objective optimization through genetic algorithms and built a cognitive engine architecture composed of a cognitive controller, sensors, a decision maker, optimizers, and interfaces. However, as this cognitive engine was purely designed for waveform optimization, it has limitations. In this paper, we set out to design a universal cognitive engine functional architecture by studying the cognitive loop.

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Learning and Adaptation in Cognitive Radios Using Neural Networks

Cited by 70 publications

References 14 publications

Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Throughput Maximization in Cognitive Radio Networks using Levenberg-Marquardt Algorithm

Design and implementation of a cognitive engine functional architecture

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