Hierarchical Decision Mamba Meets Agentic AI: A Novel Approach for RAN Slicing in 6G

TL;DR

This paper introduces an innovative Agentic AI framework for 6G RAN slicing that combines hierarchical decision-making with natural language understanding, leading to better resource management and performance improvements.

Contribution

It presents a novel integration of Hierarchical Decision Mamba controllers with a Large Language Model for interpretable and coordinated RAN slicing in 6G networks.

Findings

Higher throughput compared to baselines

Improved cell-edge performance

Reduced latency across slices

Abstract

Radio Access Network (RAN) slicing enables multiple logical networks to exist on top of the same physical infrastructure by allocating resources to distinct service groups, where radio resource scheduling plays a key role in ensuring compliance with slice-specific Service-Level Agreements (SLAs). Existing configuration-based or intent-driven Reinforcement Learning (RL) approaches usually rely on static mappings and SLA conversions. The current literature does not integrate natural language understanding with coordinated decision-making. To address these limitations, we propose an Agentic AI framework for 6G RAN slicing, driven by a super agent built using Hierarchical Decision Mamba (HDM) controllers and a Large Language Model (LLM). The super agent interprets operator intents and translates them into actionable goals using the LLM, which are used by HDM to coordinate inter-slice, intra-slice, and self-healing agents. Compared to transformer-based and reward-driven baselines, the proposed Agentic AI framework demonstrates consistent improvements across key performance indicators, including higher throughput, improved cell-edge performance, and reduced latency across different slices.