Automation Workflow for Automated Drive Test Analysis and Cell Reconfiguration

Manual drive testing is a costly, slow bottleneck in network optimization, tying up engineering teams for weeks to diagnose coverage holes or interference. This custom workflow automates that entire cycle by ingesting data from automated drive-test systems or UE measurements, using analysis agents to diagnose root causes, and orchestrating configuration changes. The operational upside comes from reducing capital tied up in test vehicles and redeploying RF engineers from repetitive diagnostics to strategic planning, directly improving asset utilization and service quality.

Implementation integrates with OSS/BSS for change approval and Element Management Systems for safe, audited configuration pushes. The architecture requires robust exception routing for high-risk changes and continuous KPI monitoring to validate improvements and trigger rollbacks. This creates a zero-touch optimization loop that continuously adapts the RAN to real-world conditions, protecting service-level agreements and reducing mean-time-to-repair for coverage issues by over 80%.

This workflow automates the costly, manual bottleneck of drive test analysis and cell reconfiguration. By continuously ingesting data from automated drive test systems and user equipment (UE) measurements, it replaces weeks-long manual campaigns with near real-time diagnosis and remediation. The operational upside comes from dramatically reduced engineering labor, accelerated mean-time-to-repair for coverage holes, and improved network KPIs through proactive optimization, directly lowering OPEX and protecting service quality.

Implementation integrates with OSS/BSS, Element Management Systems (EMS), and a RAN Intelligent Controller (RIC) via APIs. The orchestrator, built on frameworks like LangGraph, manages specialized agents for data validation, geospatial analysis, and parameter simulation. Critical controls include approval gates for high-risk changes, rollback procedures, and comprehensive observability into decision rationale and KPI impact. This creates a production-grade, audit-ready system for zero-touch RAN optimization.

Phase 1 establishes the core analysis engine, ingesting geolocated UE measurement reports and drive test logs into a data lake. We deploy a diagnostic agent using pre-trained models to identify coverage holes, interference, and poor handover zones, generating a prioritized list of recommended parameter adjustments. This initial phase delivers value by automating the analysis bottleneck, reducing manual review time from days to hours, while all proposed changes remain in a sandbox for engineering validation before any network impact.

Phase 2 introduces a closed-loop testing environment, integrating with a network digital twin or lab RAN to simulate the impact of proposed changes. An orchestration agent is added to bundle approved adjustments into atomic change sets. Finally, Phase 3 executes a pilot in a low-risk geographic cluster, integrating the workflow with the EMS/NMS via secure APIs and implementing a four-eyes approval gate. This final stage enables full, zero-touch reconfiguration, slashing the traditional multi-week drive-test-to-fix cycle to under 48 hours while maintaining strict change control.

This workflow automates a high-cost, slow manual process: ingesting UE measurement or drive test data, diagnosing coverage holes or interference, and proposing antenna tilt, power, or neighbor list changes. The operational upside comes from slashing optimization cycle times from weeks to hours, reducing truck rolls, and improving network KPIs faster. Savings are direct from reduced field labor and capital efficiency gains from better asset utilization, while revenue is protected through sustained service quality.

Implementation requires integrating with drive test systems like Accuver, TEMS, or Rohde & Schwarz, and network management systems (Ericsson OSS, Nokia NSP). The orchestrator, built on frameworks like LangGraph, manages the agentic diagnosis, routes proposals through a mandatory approval gate in ServiceNow or Jira, and executes changes via secured APIs. Rollout is phased, starting with read-only analysis, then proposal-only mode, and finally supervised execution for non-critical cells, with full audit trails and rollback procedures embedded in each step.

This workflow automates the diagnosis of coverage holes, interference, and capacity bottlenecks by continuously analyzing data from automated drive-test systems, UE trace data, and crowd-sourced measurements. It replaces manual, campaign-based analysis that can take weeks, enabling near-real-time network optimization. The operational upside comes from dramatically reduced engineering labor, faster mean-time-to-resolution for quality degradations, and improved capital efficiency through data-driven reconfiguration instead of costly new site builds. Savings are direct in OPEX and indirect in churn prevention and customer experience protection.

Implementation integrates with OSS/BSS via APIs (e.g., ServiceNow, Netcracker) for change management and uses a LangGraph orchestrator to sequence specialized analysis agents. The architecture requires robust controls: a gating engine that routes high-risk changes (e.g., antenna tilt, PCI modifications) for human review in a system like Jira, while auto-approving low-risk parameter tweaks. Observability is built into the validation loop, feeding results back into a network digital twin. Practical rollout must account for data quality variance across sources, phased deployment by market, and fallback procedures to revert changes if KPIs degrade.