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.

我们提出了一种新颖的框架，将约束优化方法和深度学习模型相结合，从而实现了强大的泛化能力和优越的逼近能力，基于该框架，我们设计了一种新的神经辅助算法，以在切片之间的资源限制下最大化网络效用，该算法具有高可扩展性，轻松适应不同数量的切片和切片配置，通过与深度强化学习方法等最先进的解决方案进行比较，我们在系统级网络模拟器中实施了该解决方案，并通过大量评估结果表明，在不同网络切片场景下，我们的解决方案获得了近乎最优的服务质量满意度和有希望的泛化性能。