Titlebook: Cognitive Radio Oriented Wireless Networks; 12th International C Paulo Marques,Ayman Radwan,Michael Gundlach Conference proceedings 2018 IC

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Semantic Issues in E-Commerce Systemsf mmWave BS is short due to high propagation and shadowing loss, thus large number of BSs are required to be deployed densely. A network consisting of many BSs consumes large energy. Sleep and activation control is a promising technique to reduce energy consumption. However, to select a set of BSs t

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Semantic Issues in E-Commerce Systemsers through their mobile or wearable devices. In this scenario, service providers or eavesdroppers can track users’ activities, location, and interactions with other users, which may discourage citizens from accessing smart city services. Pseudonym-based systems have been proposed as an efficient so

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Visualization of Uncertainty in Tag Clouds,z band has been verified. Three-tier model has been tested, where the highest priority has been assigned to the fixed WiMAX users, whose transmit parameters cannot be modified. Second tier of users was constituted by the peer-to-peer microwave line, whereas the third tier of lowest priority covered

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Laura Po,Federica Rollo,Raquel Trillo Ladoctors like communications. This paper seeks to identify the impact of the BC technology in novel spectrum sharing concepts using the Citizens Broadband Radio Service (CBRS) concept as an example. The results indicate that the BC core characteristics can be utilized in several use cases addressing cu

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Andrea Calì,Dorian Gorgan,Martín Ugarte and simulation work, realistic wireless environment representation is a necessity. Existing spectrum occupancy models range from simple additive white Gaussian noise to elaborate, based on large scale wireless spectrum measurements, but universal models are not available. Creating such a model for

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https://doi.org/10.1007/978-3-319-53640-8 in unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms to improve resource exploitation. We evaluate the performance of two classical MAB learning algorithms, . and Thomson Sampling, to handle the decentralized decision-making of Spectrum Access, applied to IoT networks; as well a