  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.
