Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service
IF 1.1
3区 计算机科学
Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
引用次数: 0
Abstract
The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptabili...使用混合 RL-CNN 模型在 5G 网络中动态分配资源，优化延迟和服务质量
5G 网络的快速部署需要创新的解决方案来实现高效、动态的资源分配。当前的策略虽然在一定程度上有效，但缺乏实时适应性。
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来源期刊
Network-Computation in Neural Systems
工程技术-工程：电子与电气
CiteScore
3.70
自引率
1.30%
发文量
22
审稿时长
>12 weeks
期刊介绍：
Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas:
Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function.
Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications.
Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis.
Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals.
Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET.
Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.
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