Constrained Network Slice Assignment via Large Language Models

TL;DR

This paper investigates using Large Language Models to assist in network slice assignment, demonstrating that LLMs can provide effective initial groupings and semantic insights to improve resource allocation in 5G networks.

Contribution

It introduces a novel approach combining LLMs with optimization for network slicing, showing LLMs can generate reasonable initial allocations and reduce search space.

Findings

LLMs can produce a reasonable first draft of slice assignments in zero-shot mode.

Combining LLM insights with optimization improves resource utilization.

LLM-guided grouping achieves performance comparable to traditional methods.

Abstract

Modern networks support network slicing, which partitions physical infrastructure into virtual slices tailored to different service requirements (for example, high bandwidth or low latency). Optimally allocating users to slices is a constrained optimization problem that traditionally requires complex algorithms. In this paper, we explore the use of Large Language Models (LLMs) to tackle radio resource allocation for network slicing. We focus on two approaches: (1) using an LLM in a zero-shot setting to directly assign user service requests to slices, and (2) formulating an integer programming model where the LLM provides semantic insight by estimating similarity between requests. Our experiments show that an LLM, even with zero-shot prompting, can produce a reasonable first draft of slice assignments, although it may violate some capacity or latency constraints. We then incorporate the LLM's understanding of service requirements into an optimization solver to generate an improved allocation. The results demonstrate that LLM-guided grouping of requests, based on minimal textual input, achieves performance comparable to traditional methods that use detailed numerical data, in terms of resource utilization and slice isolation. While the LLM alone does not perfectly satisfy all constraints, it significantly reduces the search space and, when combined with exact solvers, provides a promising approach for efficient 5G network slicing resource allocation.