| Title | A Deep Learning-Based Discrete-Time Markov Chain Analysis of Cognitive Radio Network for Sustainable Internet of Things in 5G-Enabled Smart City |
|---|---|
| ID_Doc | 39978 |
| Authors | Sethi, SK; Mahapatro, A |
| Title | A Deep Learning-Based Discrete-Time Markov Chain Analysis of Cognitive Radio Network for Sustainable Internet of Things in 5G-Enabled Smart City |
| Year | 2024 |
| Published | Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, 48, 1 |
| Abstract | The integration of cognitive radio-based Internet of Things devices in 5G network environments for smart city applications necessitates effective spectrum management. The critical aspect of spectrum management lies in making appropriate spectrum decisions for selecting idle channels that meet the quality of service requirements of secondary users while avoiding harmful interference with primary users (PUs). This article addresses the challenges by proposing an 8-state-based discrete-time Markov chain model to analyze the busy and idle times of PUs in CRNs. By leveraging this model, expressions for the traffic state and channel state belief vector are derived under imperfect sensing conditions. Additionally, a deep neural network (DNN)-based spectrum decision algorithm is introduced to optimize spectral resource utilization, considering spatial and temporal availability and energy-saving aspects in cognitive packet transmission. Our analytical and numerical evaluations demonstrate the superiority of the DNN-based algorithm over traditional methods, showcasing improved spectral resource utilization. |
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