Cognitive Radio and Networking Research at Virginia Tech

MacKenzie, Allen B.; Reed, Jeffrey H.; Athanas, Peter; Bostian, C.W.; Buehrer, R. Michael; DaSilva, Luiz A.; Ellingson, S.W.; Hou, Y. Thomas; Hsiao, Michael S.; Park, Jung‐Min; Patterson, Cameron; Raman, Sanjay; Silva, C. da

doi:10.1109/jproc.2009.2013022

Cited by 83 publications

(37 citation statements)

References 72 publications

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“…This cognitive loop includes environment observation, scenario synthesis, case-based decision making, link configuration optimization, performance estimation, and reasoning functions. Each function interacts with the radio hardware to form the cognitive cycle [4]. Both the above cognitive loops were built for certain cognitive radio application scenarios.…”

Section: Cognitive Loopmentioning

confidence: 99%

“…Thus, case-based reasoning can also be used in cognitive engine inference [4,12]. The difference between case-based reasoning and expert systems is that the former is based on experiences, whereas the latter is based on a priori knowledge.…”

Section: Supporting Technologiesmentioning

confidence: 99%

“…Virginia Tech's cognitive engine [4] was designed purely for adapting physical and link layers. Such a cognitive engine implements the cognitive loop from environmental awareness, through decision making, to waveform parameter optimization.…”

Section: Functional Architecturementioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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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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Section: Cognitive Loopmentioning

confidence: 99%

Section: Supporting Technologiesmentioning

confidence: 99%

Section: Functional Architecturementioning

confidence: 99%

mentioning

confidence: 99%

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Design and implementation of a cognitive engine functional architecture

2012

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“…It makes global adaptive changes based on application requirements and network conditions, which improves performance of the whole system [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Cross-layer Scheduling Algorithm Based on Cognitive Radio Network

Ji¹,

Nie²,

Yuan³

2013

Appl. Math. Inf. Sci.

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Cognitive radio network users have different QoS requirements. In order to provide different QoS requirements to users as well as increase the capacities of the cognitive radio networks, this paper presents a first in-depth comparative study on different factors effect QoS which include system performance, power interference, wireless channel conditions, services priorities and fairness constraints. A cross-layer scheduling algorithm which could optimize system performance and control power interference is proposed. Latency default probability of real-time users and throughput performance of non-real-time users are respectively used to measure the fairness of real-time operational system and non-real-time operational system. Simulation results are computed and the results are shown. Delay performance of real-time traffic and throughput performance of non-real-time traffic are guaranteed by setting reasonable weight to index. And power interference to authorized users caused by cognitive radio network users reduces obviously. And system obtains a very good fair performance.

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Visual Cognitive Radio

Liang

Shao

et al. 2011

Concurrency and Computation

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SUMMARY Cognitive radio is always based on spectrum sensing to cognize the physical characteristics of the wireless channel and carry out wireless communication resource scheduling. This kind of method makes it hard for cognitive radio to respond to a change in the radio environment in advance. Therefore, both the predictive ability and cognitive contents are limited. This paper proposes a new system called visual cognitive radio, which uses visual information to cognize the radio environment. Visual observation has a good predictive ability and abundant cognitive information, which enables the visual cognitive radio to react to the frequent change in the radio environment and make optimal configurations to the process of wireless communication. This paper presents a typical communication scene as an example to explain the advantages of visual cognitive radio, and also makes a preliminary analysis of its applications and challenges. Copyright © 2011 John Wiley & Sons, Ltd.

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Cognitive Radio and Networking Research at Virginia Tech

Cited by 83 publications

References 72 publications

Design and implementation of a cognitive engine functional architecture

Design and implementation of a cognitive engine functional architecture

A Cross-layer Scheduling Algorithm Based on Cognitive Radio Network

Visual Cognitive Radio

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