2018 4th International Conference for Convergence in Technology (I2CT) 2018
DOI: 10.1109/i2ct42659.2018.9057839
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Spectrum Sensing and Dynamic Spectrum Allocation for Cognitive Radio Network

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Cited by 3 publications
(2 citation statements)
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“…Since these works require lots of statistical knowledge, which is difficult to obtain in a dynamic network, the intelligent approaches have been adopted for DSA further. In [12], genetic algorithm (GA) is adopted by the central node to complete DSA for each secondary user (SU) in a CRN. Benefit from the fitting properties of deep neural networks and the interaction with the environment, several works focus on the deep reinforcement learning (DRL) structure [13].…”
Section: Introductionmentioning
confidence: 99%
“…Since these works require lots of statistical knowledge, which is difficult to obtain in a dynamic network, the intelligent approaches have been adopted for DSA further. In [12], genetic algorithm (GA) is adopted by the central node to complete DSA for each secondary user (SU) in a CRN. Benefit from the fitting properties of deep neural networks and the interaction with the environment, several works focus on the deep reinforcement learning (DRL) structure [13].…”
Section: Introductionmentioning
confidence: 99%
“…For example, different approaches emerged in the Cognitive Radio (CR) environment, where DSA is used to allocate the idle channels to Secondary Users (SU), which usually are unlicensed and have lower priority in the network [89][90][91][92][93], based on Fuzzy logic, Q-learning, randomized rounding algorithm, etc., to learn, estimate and allocate the required spectrum.…”
Section: Spectrum Allocation Techniquesmentioning
confidence: 99%