Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications

He, An; Gaeddert, Joseph; Bae, K.K.; Newman, T. J.; Reed, Jeffrey H.; Morales, Lizdabel; Park, Chang-Hyun

doi:10.1145/1621076.1621081

Cited by 51 publications

(23 citation statements)

References 5 publications

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“…If the anticipated performance of this solution does not satisfy a certain criterion, the solution will be further adapted using adaptation mechanisms until the performance criterion is met or an adaptation time limit is reached. The solution mapping and validity check process maps the solution to operational parameters for specific actions and validates it before applying to the radio [1]. CBR is a reasoning process based on experiences (cases), which resembles human reasoning process to some extent.…”

Section: Case Based Decision Making Theory and Optimization Algorithmmentioning

confidence: 99%

“…Although memory size is becoming less of a concern in modern system design due to advancements in memory size, processing time is always a big concern in many wireless applications. Therefore, an appropriate case granularity needs to be defined and redundant cases removed from the case database to improve execution time [1].…”

Section: Case Learning and Case Librarymentioning

confidence: 99%

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Design of Reasoning Agent for Cognitive Radio Networks

Singh¹

2017

IJCA

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The main task for a cognitive radio system is to adapt to the transmission parameters in a dynamically changing environment. The nature of wireless channels and the number of transmission parameters to be optimized in various layers add more complexity to the adaptation process. In these situations Case Based Reasoning (CBR) agent along with optimization algorithm provides a robust solution. CBR Agent develops cases as the changes occur in the environment and acquires an understanding of the system. As new situations occur, experience from the previously developed cases is taken into consideration and a solution to the system is provided. In real time with training, the Cognitive Engine (CE) learns the variations occurring in the environment and utilizes the previously existing cases to provide new solution. As well as if abruptly some unexpected situation is encountered then the optimization algorithm is initiated and a solution is designed as per the need of the environment and this case is also added to the case list for future reference. Hence variations in the environment are mapped extensively and the optimization process becomes more efficient. In the present work case based reasoning along with particle swarm optimization is implemented.

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Section: Case Based Decision Making Theory and Optimization Algorithmmentioning

confidence: 99%

Section: Case Learning and Case Librarymentioning

confidence: 99%

Design of Reasoning Agent for Cognitive Radio Networks

Singh¹

2017

IJCA

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“…Usually, CBR involves 4-stage cycle: retrieve, reuse, revise and retain. CBR was used in a practical realization of the cognitive radio architecture for IEEE 802.22 applications [56]. The CBR engine is the focal point allowing decision-making in situations when secondary users must vacate a spectrum for a primary incumbent user.…”

Section: Some Specific Reasoning Realizationsmentioning

confidence: 99%

Learning and Reasoning in Cognitive Radio Networks

Gavrilovska

Atanasovski

Macaluso

et al. 2013

IEEE Commun. Surv. Tutorials

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Abstract-Cognitive radio networks challenge the traditional wireless networking paradigm by introducing concepts firmly stemmed into the Artificial Intelligence (AI) field, i.e., learning and reasoning. This fosters optimal resource usage and management allowing a plethora of potential applications such as secondary spectrum access, cognitive wireless backbones, cognitive machine-to-machine etc. The majority of overview works in the field of cognitive radio networks deal with the notions of observation and adaptations, which are not a distinguished cognitive radio networking aspect. Therefore, this paper provides insight into the mechanisms for obtaining and inferring knowledge that clearly set apart the cognitive radio networks from other wireless solutions.

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“…Experiential databases provide a faster first attempt to match the current situation with a successful decision made in the past. If a sufficiently similar case is not found, the top retrieved cases can act as partial seeds into a GA with the goal of improving performance [11]. This architecture is utilized within this article and will be discussed in more detail in Section 3.…”

Section: Ga Use In Crmentioning

confidence: 99%

“…This conversion is not a direct normalization, but rather makes use of dedicated utility functions to place the value on this scale. The utility functions presented in Equations (4)- (6) are based on previous work [11]. …”

Section: Utility and Fitness Definitionsmentioning

confidence: 99%

Cognitive radio engine parametric optimization utilizing Taguchi analysis

Amanna

Ali

Gadhiok

et al. 2012

J Wireless Com Network

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Cognitive radio (CR) engines often contain multiple system parameters that require careful tuning to obtain favorable overall performance. This aspect is a crucial element in the design cycle yet is often addressed with ad hoc methods. Efficient methodologies are required in order to make the best use of limited manpower, resources, and time. Statistical methods for approaching parameter tuning exist that provide formalized processes to avoid inefficient ad hoc methods. These methods also apply toward overall system performance testing. This article explores the use of the Taguchi method and orthogonal testing arrays as a tool for identifying favorable genetic algorithm (GA) parameter settings utilized within a hybrid case base reasoning/genetic algorithm CR engine realized in simulation. This method utilizes a small number of test cases compared to traditional design of experiments that rely on full factorial combinations of system parameters. Background on the Taguchi method, its drawbacks and limitations, past efforts in GA parameter tuning, and the use of GA within CR are overviewed. Multiple CR metrics are aggregated into a single figure-of-merit for quantification of performance. Desirability functions are utilized as a tool for identifying ideal settings from multiple responses. Kiviat graphs visualize overall CR performance. The Taguchi method analysis yields a predicted best combination of GA parameters from nine test cases. A confirmation experiment utilizing the predicted best settings is compared against the predicted mean, and desirability. Results show that the predicted performance falls within 1.5% of the confirmation experiment based on 9 test cases as opposed to the 81 test cases required for a full factorial design of experiments analysis.

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Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications

Cited by 51 publications

References 5 publications

Design of Reasoning Agent for Cognitive Radio Networks

Design of Reasoning Agent for Cognitive Radio Networks

Learning and Reasoning in Cognitive Radio Networks

Cognitive radio engine parametric optimization utilizing Taguchi analysis

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