Titlebook: Cognitive Radio Mobile Ad Hoc Networks; F. Richard Yu Book 2011 Springer Science+Business Media LLC 2011 Cognitive Radio Mobile Ad Hoc Net

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Cognitive Radio Mobile Ad Hoc Networks in Healthcareems demand a great amount of resources such as health care personnel and medical equipments. This increases the cost of health care making it unaffordable to the majority of our society. This chapter introduces an architecture and design of a health care automation network. The health care automatio

myriad

Interoperability Between IEEE 802.11e and HSDPA: Challenges from Cognitive Radio-user traveling in a public transportation system and requesting multimedia services to the operator. The interoperability between HSDPA and Wi-Fi (IEEE 802.11e standard) radio access technologies (RATs) is first addressed, a topology in which the user has access to both RATs was considered, togethe

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Allodynia

https://doi.org/10.1007/978-94-017-2975-8riety of non-uniform sub-channel selection policies are studied. We then present results on the scaling law of the opportunistic spectrum sharing in DS-CDMA/OFDM systems with multiple users. We provide numerical results to compare different sub-channel selection policies.

Restenosis

Wolfgang Krohn,Günter Küppers,Helga Nowotnycognitive radio networks. Under the proposed CREAM-MAC protocol, each secondary user is equipped with a cognitive radio-enabled transceiver and multiple channel sensors. Our cooperative sequential spectrum sensing scheme improves the accuracy of spectrum sensing and further protects the primary user

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https://doi.org/10.1007/978-3-642-71004-9a smart network topology, which provisions cognition capability to routing. Particularly, we present a distributed prediction-based cognitive topology control (PCTC) scheme to demonstrate the framework and verify its feasibility.

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https://doi.org/10.1007/978-3-319-27635-9mework, which enables the vehicular nodes with cognitive capability, is modeled and analyzed using game theory. Also, a Q-learning algorithm is used in vehicular nodes to provide the cognitive capability to learn and adapt their decision. Dynamics of Q-learning algorithm can be modeled as an evoluti