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Inference Results: Agentic AI 5G Resource Allocation

This file provides the direct output of the Agentic AI Orchestration layer for a representative network scenario.

📡 Input Telemetry (Edge Node)

  • Throughput: 715.0 Mbps
  • Latency: 11.0 ms
  • Reliability: 99.967%
  • Density: 2533.0 devices/km²
  • Mobility: 58.0 km/h

🧠 ML Diagnostics (Diagnostics Tools)

  • Tool 1: Predict_Degradation (Random Forest): Predicted Network State: Scale lag
  • Tool 2: Assess_Link_Stability (K-Means Clustering): Link Stability Cluster: 2 - Unstable (Degraded)

🤖 Agentic Reasoning Layer (Orchestrator)

The Agentic Advisor (powered by LangChain and LLM) synthesized the following reasoning based on 3GPP TS 23.501 specifications:

3GPP Agentic AI RAN Framework - FINAL RESOURCE ALLOCATION REPORT

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1. Observations & Scenario Analysis The network is experiencing high device density (2533 devices/km²) with moderate mobility. ML tools indicate a "Scale lag" state, suggesting that the current resource allocation is not keeping pace with the dynamic load. The link stability has degraded into an "Unstable" regime (Cluster 2).

2. 3GPP-Compliant Recommendations

  • Vertical/Horizontal Scaling: Trigger immediate horizontal scaling for the eMBB slice to mitigate the "Scale lag" error. This is critical to maintain the 715Mbps throughput in a high-density scenario.
  • Mobility Management (TS 23.501): Adjust the Time-To-Trigger and Hysteresis parameters for handover. The 58km/h mobility requires faster handover decisions to prevent RLF (Radio Link Failure) in the unstable regime.
  • URLLC Prioritization: If URLLC flows are active, pre-emptively shift them to a dedicated resource pool with prioritized 5QI identifiers to guarantee reliability (>99.999%) while the eMBB slice recovers.

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