In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key elements for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for a better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.
翻译:在当今时代,下一代网络,如第5代(5G)和第6代(6G)网络,需要高度安全、低纬度、高可靠标准和能力。在这些网络中,可重新配置的无线网络切片被认为是5G和6G网络的关键要素之一。可重新配置的剪片使操作者能够使用单一基础设施运行网络的各种实例,以提高服务质量(QOS)。QOS可以通过利用人工智能和机器学习算法重新配置和优化这些网络来实现。为网络管理开发智能决策机制并限制网络切片故障,需要通过机器学习和可重新配置的无线网络解决方案。在本文中,我们提出了一个混合的深层次学习模式,其中包括一个卷发神经网络(CNN)和长期短期内存(LSTM)。CNN进行资源分配、网络重组和切片选择,而LSTM则用于网络切片的统计信息(工作量平衡、误差率等)。拟议的模型的可适用性通过使用多种未知的装置来验证其应用性。拟议的模型的可应用性通过使用95的精确性、切片超载性来反映其全面失灵率。