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Secondary User Experience-oriented Resource Allocation in AI-empowered Cognitive Radio Networks Using Deep Neuroevolution
تخصیص منابع کاربر گرا ثانویه در شبکه های رادیویی شناختی دارای هوش مصنوعی با استفاده از تکامل عصبی عمیق-2020 Secondary user (SU)-experience-oriented resource
allocation (RA) will become increasingly important in cognitive
radio networks (CRNs) in future wireless networks. For efficient
real-time processes, cognitive radios (CRs) are usually combined
with artificial intelligence (AI) to improve better adaptation and
intelligent RA. However, deep learning (DL), which is a key
AI strategy with remarkable capabilities towards advancing this
vision, has several built-in limitations. Firstly, the most successful
DL applications require training with large amounts of data;
secondly, they assume that the data samples to be independent,
while in CRNs one typically encounters sequences of highly
correlated states. To circumvent this issue, this paper introduces
a deep neuroevolution (DNE) technique for dynamic RA. Using
this technique, a stable learning framework was achieved by
introducing the phenotypic plasticity of transmission rates and
delay constraints inside a multi-layer perceptron (MLP). The
stability of SU satisfaction as they increased in number was
achieved at 36 SUs, which is a 13.3% decrease from when they
were only 6 SUs in the CRN for all learning mechanisms. Keywords: Secondary user | Artificial intelligence| Cognitive radio networks | Ant colony optimization | Logistic regression | Multilayer perceptron | Deep Q-network | Deep neuroevolution |
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