Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dimensional resource allocation problems. However, deep learning models suffer limited generalization and adaptability to dynamic slicing configurations. In this paper, we propose a novel framework that integrates constrained optimization methods and deep learning models, resulting in strong generalization and superior approximation capability. Based on the proposed framework, we design a new neural-assisted algorithm to allocate radio resources to slices to maximize the network utility under inter-slice resource constraints. The algorithm exhibits high scalability, accommodating varying numbers of slices and slice configurations with ease. We implement the proposed solution in a system-level network simulator and evaluate its performance extensively by comparing it to state-of-the-art solutions including deep reinforcement learning approaches. The numerical results show that our solution obtains near-optimal quality-of-service satisfaction and promising generalization performance under different network slicing scenarios.

翻译：网络切片是5G及未来通信中高效支持多样化业务的关键技术。许多网络切片方案依赖深度学习来管理复杂高维的资源分配问题。然而，深度学习模型对动态切片配置的泛化能力和适应性有限。本文提出一种新型框架，将约束优化方法与深度学习模型相结合，实现了强泛化能力与卓越的近似性能。基于该框架，我们设计了一种新型神经辅助算法，用于在切片间资源约束条件下为各切片分配无线资源以最大化网络效用。该算法具有高度可扩展性，能轻松适应不同数量的切片及其配置。我们在系统级网络模拟器中实现了所提方案，并通过与深度强化学习等最新方案进行广泛对比评估了其性能。数值结果表明，在不同网络切片场景下，我们的方案能获得接近最优的服务质量满意度，并展现出优越的泛化性能。