Sukhdeep Singh, Geon Kim, Dara Ron, Suyog Moogi, Pranshav Gajjar, N Someswara, Rao Koduri, Een Hong, Vijay Shah
We lifted 11 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| NextG-Wireless-Lab-NC-State/TelcoAgent | canonical | 7 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| audit_spec_citations | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/audit.py code served (permissive licence) · get_code("c816f7bf73ecf058") |
| ensure_output_dir | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/cli_utils.py code served (permissive licence) · get_code("119ec02f0a8e1501") |
| events_to_dicts | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/anomaly_detector.py code served (permissive licence) · get_code("73703e1742ce207c") |
| extract_spec_citations | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/audit.py code served (permissive licence) · get_code("0729de6e3cd1b2fe") |
| ground_truth_spec_numbers | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/audit.py code served (permissive licence) · get_code("317e21014a83437e") |
| record_from_result | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/evaluation/ragas_eval.py code served (permissive licence) · get_code("ac345acec2ecc0ff") |
| setup_logging | Ran | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/cli_utils.py code served (permissive licence) · get_code("49e279fff90c238c") |
| check_directional_consistency_hourly | Not yet run | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/ontology/core.py code served (permissive licence) · get_code("eee94c2ae11861eb") |
| get_default_ontology | Not yet run | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/ontology/core.py code served (permissive licence) · get_code("cdc4fc68669687d5") |
| parse_osm_context | Not yet run | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/cause_inference.py code served (permissive licence) · get_code("6ac8e6c85834ee5a") |
| plot_forecast_with_anomalies | Not yet run | NextG-Wireless-Lab-NC-State/TelcoAgent/telcoagent/explainer/anomaly_plot.py code served (permissive licence) · get_code("80072cfd6191caf4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.19821")
get_code_for_paper("2606.19821")
have("2606.19821")
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