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Adaptive Intelligent Cooperative Spectrum Sensing In Cognitive Radio
Radio Spectrum is most precious and scarce resource and must be utilized...
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Collaboration and Coordination in Secondary Networks for Opportunistic Spectrum Access
In this paper, we address the general case of a coordinated secondary ne...
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Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access
An important challenge in underlay dynamic spectrum access (DSA) is how ...
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Cluster Size Optimization in Cooperative Spectrum Sensing
In this paper, we study and optimize the cooperation cluster size in coo...
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Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network
Future wireless networks will progressively displace service provisionin...
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Exoplanet Atmosphere Retrieval using Multifractal Analysis of Secondary Eclipse Spectra
We extend a data-based model-free multifractal method of exoplanet detec...
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Overlay Secondary Spectrum Sharing with Independent Re-attempts in Cognitive Radios
Opportunistic spectrum access (OSA) is a promising reform paradigm envis...
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Analytical and Learning-Based Spectrum Sensing Time Optimization in Cognitive Radio Systems
Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by optimizing its spectrum sensing time is formulated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. The energy consumed for finding a transmission opportunity is evaluated and a discussion on the impact of the number of the primary users on the secondary user throughput and consumed energy is presented. In order to avoid the challenges associated with the analytical method, as a second solution, a systematic neural network-based sensing time optimization approach is also proposed in this paper. The proposed adaptive scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance, and cooperation of the artificial neural networks used in the proposed method are disclosed in detail and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimization scheme.
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