Model comparison used.

Communication networks of the future will rely heavily on network slicing (NS), a technology that enables the creation of distinct virtual networks within a shared physical infrastructure. This capability is critical for meeting the diverse quality of service (QoS) requirements of various applications, from ultra-reliable low-latency communications to massive IoT deployments. To achieve efficient network slicing, intelligent algorithms are essential for optimizing network resources and ensuring QoS. Artificial Intelligence (AI) models, particularly deep learning techniques, have emerged as powerful tools for automating and enhancing network slicing processes. These models are increasingly applied in next-generation mobile and wireless networks, including 5G, IoT infrastructure, and software-defined networking (SDN), to allocate resources and manage network slices dynamically. In this paper, we propose an Interpretable Network Bandwidth Slicing Identification (INBSI) system that leverages a modified Convolutional Neural Network (CNN) architecture with Nesterov-accelerated Adaptive Moment Estimation (NADAM) optimization. Additionally, we use a Variational Autoencoder (VAE) for preprocessing initial data, along with reconstructed data for data validity assessment. The model we propose outperforms other alternatives and reaches an accuracy peak of (84%) in the system environment. A range of accuracy was achieved by (k-nearest neighbors algorithm) KNN (76%), Random Forest (69%), BaggingClassifier (70%), and Gaussian Naive Bayes (GaussianNB) (55%). The accuracy of additional methods varies, including Decision Trees, AdaBoost, Deep Neural Forest (DNF), and Multilayer Perceptrons (MLPs). We utilize two eXplainable Artificial Intelligence (XAI) approaches, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to provide insight into the impact of certain input characteristics on the network slicing process. Our work highlights the potential of AI-driven solutions in network slicing, offering insights for operators to optimize resource allocation and enhance future network management.未来的通信网络将高度依赖网络切片（Network Slicing, NS）技术——该技术可在共享的物理基础设施之上创建彼此独立的虚拟网络。此能力对于满足各类应用多样化的服务质量（Quality of Service, QoS）需求至关重要，涵盖超可靠低延迟通信、大规模物联网（Internet of Things, IoT）部署等场景。为实现高效的网络切片，需借助智能算法优化网络资源并保障服务质量。人工智能（Artificial Intelligence, AI）模型，尤其是深度学习技术，已成为自动化与优化网络切片流程的强力工具。此类模型正日益应用于下一代移动与无线网络，包括5G、物联网基础设施以及软件定义网络（Software-Defined Networking, SDN），以动态分配资源并管理网络切片。本文提出一种可解释性网络带宽切片识别（Interpretable Network Bandwidth Slicing Identification, INBSI）系统，该系统采用结合了内斯特罗夫加速自适应矩估计（Nesterov-accelerated Adaptive Moment Estimation, NADAM）优化的改进型卷积神经网络（Convolutional Neural Network, CNN）架构。此外，本文还利用变分自编码器（Variational Autoencoder, VAE）对初始数据进行预处理，并结合重构数据开展数据有效性评估。所提模型的性能优于其他对比方案，在系统环境下的准确率峰值可达84%。对比基准方法中，k近邻算法（k-nearest neighbors algorithm, KNN）的准确率为76%、随机森林（Random Forest）为69%、BaggingClassifier为70%，高斯朴素贝叶斯（Gaussian Naive Bayes, GaussianNB）为55%；其余方法如决策树、AdaBoost、深度神经森林（Deep Neural Forest, DNF）以及多层感知器（Multilayer Perceptrons, MLPs）的准确率各有不同。本文采用两种可解释人工智能（eXplainable Artificial Intelligence, XAI）方法——沙普利加性解释（Shapley Additive Explanations, SHAP）与局部可解释模型无关解释（Local Interpretable Model-Agnostic Explanations, LIME），以解析特定输入特征对网络切片流程的影响。本研究凸显了人工智能驱动的解决方案在网络切片领域的应用潜力，可为运营商优化资源分配、改进未来网络管理提供参考依据。