Enterprise AI Analysis
Evolving 5G-LENA Towards 6G: Integrating AI for Intelligent Scheduling of Multi-Flow Traffic
This paper introduces DRILL-Q, a novel Reinforcement Learning (RL)-based resource scheduler for 5G-LENA, designed to optimize resource allocation for latency-sensitive, multi-flow traffic. By integrating ns3-gym, it enables AI model training and evaluation within a realistic 5G NR simulation environment, paving the way for adaptable 6G networks.
Executive Impact Summary
Enhancing 5G/6G network intelligence for autonomous, QoS-aware resource scheduling.
1.00
Jain's Fairness Index (non-GBR)
Reduced
QoS Violations (DC-GBR)
Seamless
AI Integration (5G-LENA)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Problem Statement
Existing 5G-LENA simulators lack direct AI integration, making it challenging to evaluate AI-based network functions under realistic conditions. Current schedulers often fall short in handling complex, heterogeneous QoS demands of multi-flow traffic, especially for latency-sensitive applications.
Proposed Framework
We propose a modular Reinforcement Learning (RL)-based scheduling framework for 5G-LENA, leveraging the ns3-gym module. This integration allows for realistic, protocol-compliant Radio Access Network (RAN) simulation combined with AI model training, offering a foundation for reproducible and customizable experimentation.
DRILL-Q Scheduler
The Delay-Responsive Intelligent Learning for Latency-sensitive QoS (DRILL-Q) is an RL-based scheduler designed for latency-sensitive, multi-flow traffic. It performs flow-level scheduling considering 3GPP-defined QoS parameters like Packet Delay Budget (PDB), priority level, and Delay-critical GBR (DC-GBR), continuously adapting to real-time network dynamics.
Performance Results
Extensive simulations demonstrate that DRILL-Q significantly reduces QoS violations and improves fairness across flows compared to traditional 5G-LENA schedulers (RR, PF, QoS). It effectively adapts to flow-level priorities and UE-specific multi-flow characteristics under saturated conditions, even with PDCP discarding enabled.
Key Finding Spotlight
1.00 Jain's Fairness Index (non-GBR flows)DRILL-Q achieves near-perfect fairness among non-GBR flows in both PDCP discarding scenarios, significantly outperforming benchmark schedulers that show imbalance due to strict prioritization or priority-agnostic behavior.
DRILL-Q Operational Workflow
State Observation & Action Retrieval
→
Action Execution & Resource Scheduling
→
Reward Update & Environment Transition
| Ref. | Year | Simulator | Public Code | QoS Granularity |
|---|---|---|---|---|
| [3] | 2024 | N/A | No | User-level |
| [4] | 2024 | N/A | No | Slice-level |
| [5] | 2025 | ns-3 | No | User-level |
| [10] | 2024 | ns-3 | No | User-level |
| [15] | 2025 | N/A | No | User-level |
| [16] | 2024 | ns-3 | No | User-level |
| Ours | 2025 | ns-3 | Yes | Flow-level |
Our proposed framework offers the first open-source AI integration with 5G-LENA, providing flow-level QoS granularity and public code for reproducible research, which is a significant advancement over existing solutions.
Overcoming Latency & QoS Challenges with DRILL-Q
In a highly saturated 10 MHz bandwidth 5G network scenario, traditional schedulers like QoS, PF, and RR struggled to balance throughput, delay, and jitter across heterogeneous multi-flow traffic, especially for latency-sensitive DC-GBR flows. The QoS scheduler, for instance, led to near-zero throughput for non-GBR flows due to strict prioritization, while PF and RR were priority-agnostic. DRILL-Q, through its RL-based intelligence, demonstrated a balanced trade-off, consistently meeting DC-GBR requirements while significantly improving fairness and reducing QoS violations across all flows, even with PDCP discarding enabled. This highlights its capability to adapt to complex network dynamics and diverse QoS demands.
Outcome: DRILL-Q achieved superior QoS compliance and fairness, reducing QoS violations and ensuring more stable and efficient resource utilization for multi-flow traffic under congested 5G conditions. Its adaptive learning capability proved critical in handling dynamic network requirements.
Advanced ROI Calculator
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Implementation Roadmap
A typical phased approach to integrate AI-driven scheduling into your network infrastructure.
Phase 1: Assessment & Strategy (2-4 Weeks)
Comprehensive analysis of existing network architecture, traffic patterns, QoS requirements, and identification of AI integration points. Development of a tailored AI strategy and selection of optimal RL models.
Phase 2: Framework Integration & Training (6-12 Weeks)
Integration of the AI scheduling framework (e.g., ns3-gym with 5G-LENA) into a robust simulation environment. Data collection, model training, and iterative refinement of RL policies using realistic network traffic simulations.
Phase 3: Pilot Deployment & Validation (8-16 Weeks)
Deployment of the trained AI scheduler in a controlled pilot environment or specific network segments. Continuous monitoring, performance validation against key QoS metrics, and further optimization based on real-world feedback.
Phase 4: Full-Scale Rollout & Continuous Optimization (Ongoing)
Phased rollout across the entire network infrastructure. Establishment of a feedback loop for continuous learning and adaptation of the AI model to evolving network conditions, traffic demands, and new 6G use cases.
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