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

2025-12-29

AI Summary

To address the challenges of multi-SLA differentiated assurance, static resource scheduling, and semantic understanding deficiency in 6G RAN slicing, this paper proposes a compute-semantic-control co-designed agent framework. Methodologically, it introduces a novel integration of large language model (LLM)-based intent parsing with a hierarchical decision-making Mamba (HDM) controller, enabling three-tier collaborative agents—cross-slice, intra-slice, and self-healing—that transcend conventional static mapping or reinforcement learning paradigms. The framework supports natural-language-driven SLA semantic modeling and dynamic closed-loop scheduling. Evaluation demonstrates significant improvements over Transformer- and RL-based baselines in key metrics—including throughput, edge user rate, and end-to-end latency—while enabling real-time, multi-dimensional, heterogeneous SLA-constrained resource orchestration.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Integrates natural language understanding with coordinated decision-making for RAN slicing
• Translates operator intents into actionable goals using LLM and HDM controllers
• Improves throughput, cell-edge performance, and reduces latency in 6G networks

Innovation

• Hierarchical Decision Mamba coordinates multi-agent slicing
• Large Language Model interprets intents into actionable goals
• Agentic AI framework improves throughput and reduces latency