Platform Architecture
Autonomous multi-agent system optimizing RAN parameters—antenna tilt, azimuth, EIRP, PCI assignments, handover thresholds—across LTE/5G NR networks using real-time KPI analysis and digital twin validation
Multidimensional Data Architecture
Ingests RAN performance counters (RSRP, RSRQ, SINR, PRB utilization, CQI distributions, handover success rate) and configuration data (PCI plans, neighbor lists, power settings) through geohash-indexed time-series storage, enabling sub-100ms queries across cell-level and sector-level granularity.
Cell-Level Spatial Indexing
eNodeB/gNodeB sites and their sectors are indexed using geohash quadtrees, enabling parallel optimization of antenna parameters (electrical tilt, azimuth, beamwidth) while respecting cell overlap zones and co-channel interference boundaries.
Multi-Resolution KPI Analysis
Separates long-term degradation patterns (7-30 day trends in dropped call rate, average RSRP) from real-time incidents (sudden SINR drops, PRB saturation spikes) using sliding window aggregations at 1-minute, 15-minute, and daily resolutions.
Multi-Vendor Data Normalization
Unifies performance counters and configuration data across Ericsson, Nokia, Huawei, and Samsung equipment, mapping vendor-specific parameter names (e.g., "qRxLevMin" vs "minRxLevel") to standardized 3GPP TS 32.425 metric definitions.
Real-Time Configuration Sync
Network configuration changes (PCI reassignments, neighbor list updates, mobility parameter adjustments) propagate through event streams to both digital twin and live network management systems, maintaining consistency between planned and deployed states.
Multi-Layered Agentic Hierarchy
Four specialized AWS Bedrock agents work in concert: Strategic and Tactical agents diagnose RAN issues from KPI data, Manager agent prioritizes and sequences optimization actions, and Fixer agent validates changes via digital twin simulation before deployment to live network.
Analysis Layer: Strategic & Tactical Agents
Strategic Agent analyzes historical trends (7-30 days) to identify systemic issues like poor PCI planning, suboptimal antenna configurations, or persistent coverage holes. Tactical Agent monitors live KPIs (1-minute intervals) to detect acute problems: sudden handover failures, PRB congestion, or SINR degradation requiring immediate intervention.
Strategic Agent (Long-Horizon)
Analyzes 7-30 day KPI trends to identify recurring problems: cells with consistently high dropped call rates, sectors with poor RSRP coverage, PCI collision patterns, or imbalanced load distribution across frequency layers.
Tactical Agent (Short-Horizon)
Monitors 1-minute resolution KPIs for anomalies: sudden spikes in handover failure rate (>5%), PRB utilization exceeding 80%, SINR drops below -3dB, or unexpected traffic surges requiring immediate load balancing actions.
Sub-Agent Specialization Network
Each analysis agent spawns six specialized sub-agents running in parallel: Coverage sub-agent analyzes RSRP/RSRQ patterns, Interference analyzes PCI mod-3 and mod-30 collisions, Capacity monitors PRB utilization, Throughput evaluates CQI distributions and throughput KPIs, PCI Planning detects conflicts, and Layer Balance optimizes inter-frequency handovers.
Coverage
RSRP/RSRQ analysis
Interference
PCI mod-3/mod-30 conflicts
Capacity
PRB utilization tracking
Throughput
CQI & data rate optimization
PCI Planning
Neighbor collision-free plan
Layer Balance
MLB/MRO parameter tuning
Orchestration Layer: Manager Agent
Manager agent reconciles conflicting recommendations from analysis agents using operator-defined priorities (e.g., "maximize coverage" vs "minimize interference"). It scores each proposed action by projected KPI impact, geographic priority, and confidence level, then generates an ordered sequence of changes to deploy.
Recommendation Scoring
score = Σ(w_KPI × Δ_KPI) × priority_geo × urgency × confidence
Where w_KPI are operator-defined weights for each KPI (e.g., 2.0× weight for dropped calls vs 1.0× for throughput), Δ_KPI is the projected improvement, priority_geo elevates high-value areas, and confidence reflects agent certainty.
Validation Layer: Fixer Agent
Fixer agent tests each proposed change in the digital twin before live deployment. For example, before adjusting antenna tilt on cell X, it simulates the impact on RSRP coverage, neighbor cell interference, and handover zones. If KPIs degrade beyond thresholds (e.g., >2% drop in coverage), the change is rejected. Otherwise, it's approved for production deployment.
Digital Twin Test
Simulate parameter changes
KPI Impact Check
Verify no degradation >2%
Auto-Rollback
Revert if live KPIs drop
Digital Twin Substrate
A software replica of the live RAN network that mirrors all cell configurations (antenna tilt, azimuth, power, PCI assignments, neighbor lists, handover parameters) and simulates RF propagation, interference, and load distribution. Changes are tested here first before deploying to production eNodeBs/gNodeBs.
RF Propagation Modeling
Simulates RSRP/RSRQ coverage using simplified propagation models (path loss, antenna gain patterns, electrical/mechanical tilt effects). Predicts how changes to antenna tilt, azimuth, or EIRP will affect signal strength at different geographic locations.
Interference & PCI Simulation
Calculates SINR based on serving cell signal vs. neighbor cell interference. Detects PCI mod-3 and mod-30 collisions that cause handover confusion. Evaluates how PCI reassignments or power changes affect co-channel and adjacent-channel interference.
Traffic & Capacity Simulation
Models PRB utilization and throughput based on traffic patterns. Simulates how mobility load balancing (MLB) parameter changes or cell range expansion (CRE) offsets affect load distribution across cells, predicting congestion hotspots before they occur.
Deployment & Rollback
Approved changes deploy to live eNodeBs/gNodeBs via vendor-specific APIs (Ericsson ENM, Nokia NetAct, etc.). System monitors post-deployment KPIs for 15 minutes. If dropped call rate increases >2% or RSRP degrades >3dB, changes auto-revert to previous configuration.
Continuous Learning Framework
Agents learn from deployment outcomes: if increasing antenna tilt by 2° improved RSRP by 5dB without degrading neighbors in similar scenarios, this pattern is stored. Future recommendations in similar network contexts leverage these learned outcomes, improving optimization accuracy over time.
Outcome Memory Storage
Each agent stores historical optimization results: what RAN parameters were changed (e.g., "tilt +2°, azimuth -5°"), in what network context (cell density, traffic load, interference levels), and the resulting KPI impact (dropped calls ↓3%, RSRP ↑4dB). This forms a learned knowledge base.
Pattern Recognition Across Cells
System identifies recurring patterns: "urban high-rise cells with RSRP <-95dBm benefit from tilt increase" or "highway sectors with high handover failure respond well to neighbor list expansion". These patterns generalize to similar cells elsewhere in the network.
Feedback Loop from Live Network
After every deployment, agents observe actual KPI changes over 24 hours. If results match predictions (e.g., predicted +3dB RSRP improvement, observed +2.8dB), confidence increases. If outcomes diverge (predicted improvement but degradation occurred), the action pattern is de-weighted for future recommendations.
Real-Time Processing Architecture
RAN performance counters stream in at 1-minute intervals from thousands of cells. System processes these KPI streams (RSRP, PRB utilization, handover success rate) in real-time, triggering agent analysis when thresholds breach. End-to-end latency from KPI ingestion to recommendation generation: <2 minutes.
KPI Data Ingestion
Collect RSRP, RSRQ, SINR, PRB utilization, handover metrics from eNodeBs/gNodeBs at 1-minute intervals via vendor OSS interfaces
Agent Analysis (Parallel)
Strategic and Tactical agents spawn 6 sub-agents each (Coverage, Interference, Capacity, Throughput, PCI, Layer Balance) to analyze KPIs concurrently
Recommendation Prioritization
Manager agent scores all proposed changes (PCI reassignment, tilt adjustment, power change) by KPI impact and operator priorities, producing ranked action list
Digital Twin Validation
Fixer agent simulates each change in digital twin, verifying RSRP/SINR/throughput don't degrade beyond thresholds (e.g., <2% KPI drop)
Live Network Deployment
Push approved parameter changes to production eNodeBs/gNodeBs via vendor APIs, monitor KPIs for 15 minutes, auto-rollback if degradation detected
Learning from Outcomes
Compare predicted vs. actual KPI improvements (e.g., predicted +3dB RSRP vs. observed +2.8dB), store results to improve future recommendations