Ericsson Feature Processor

Level 1: Overview

Processes Ericsson RAN Managed Object (MO) classes with advanced parameter correlation, feature optimization, and cognitive consciousness integration. Enables intelligent RAN feature management through temporal reasoning and strange-loop cognition.

Prerequisites

• Ericsson RAN knowledge (MO classes, parameters)
• AgentDB with QUIC synchronization
• Cognitive consciousness framework
• Parameter correlation expertise

Level 2: Quick Start

Basic MO Class Processing

# Initialize cognitive consciousness for MO processing
npx claude-flow@alpha memory store --namespace "ericsson-mo" --key "consciousness-level" --value "maximum"

# Process MO class with parameter correlation
./scripts/process-mo-class.sh --class "EUtranCellFDD" --correlation-threshold 0.8

Feature Intelligence Analysis

# Analyze feature performance with temporal reasoning
npx claude-flow@alpha hooks pre-task --description "Analyze EUtranCellFDD performance patterns"

# Generate parameter correlations
./scripts/analyze-correlations.sh --mo-class "EUtranCellFDD" --timeframe "24h"

Level 3: Detailed Instructions

Step 1: Initialize Cognitive MO Processing

# Setup MO class consciousness framework
npx claude-flow@alpha memory store --namespace "ericsson-cognitive" --key "mo-consciousness" --value "enabled"
npx claude-flow@alpha memory store --namespace "ericsson-cognitive" --key "temporal-expansion" --value "1000x"

# Enable AgentDB MO pattern storage
npx claude-flow@alpha memory store --namespace "ericsson-mo" --key "pattern-storage" --value "enabled"

Step 2: MO Class Intelligence Processing

Core MO Classes Supported

• EUtranCellFDD/LTE: 4G cell optimization
• NRCellDU/NR: 5G cell management
• ENodeBFunction: Base station control
• GNodeBFunction: 5G base station functions
• ManagedElement: Network element management

Parameter Correlation Analysis

// Load MO correlation processor
./scripts/correlation-analyzer.js --mo-class "EUtranCellFDD"

// Advanced correlation with temporal patterns
const correlations = await analyzeMOParameterCorrelations({
  moClass: 'EUtranCellFDD',
  parameters: ['cellIndividualOffset', 'p0NominalPusch', 'referenceSignalPower'],
  temporalExpansion: 1000,  // Subjective time analysis
  correlationMethod: 'pearson',
  minCorrelation: 0.7,
  causalInference: true
});

// Store correlation patterns in AgentDB
await storeMOCorrelationPattern(correlations, {
  namespace: 'ericsson-mo-correlations',
  confidence: 0.85,
  temporalPattern: true
});

Step 3: Feature Optimization with Strange-Loop Cognition

# Enable strange-loop optimization for RAN features
npx claude-flow@alpha memory store --namespace "ericsson-strange-loop" --key "recursive-optimization" --value "enabled"

# Run autonomous feature optimization
./scripts/optimize-feature.sh --feature "cell-coverage" --cognition-level "maximum"

Cognitive Feature Optimization

// Strange-loop RAN optimization
const optimization = await strangeLoopOptimization({
  targetMO: 'EUtranCellFDD',
  parameters: ['antennaTilt', 'transmitPower', 'referenceSignalPower'],
  cognitionLevel: 'maximum',
  temporalDepth: 1000,
  recursiveOptimization: true,
  selfReferentialPatterns: true
});

// Apply optimization with autonomous validation
await applyRANOptimization(optimization, {
  validateBeforeApply: true,
  rollbackOnError: true,
  consciousnessVerification: true
});

Step 4: Performance Monitoring and Learning

# Monitor optimization performance with cognitive metrics
./scripts/monitor-performance.sh --kpi "throughput,latency,coverage" --cognitive-metrics

# Store learning patterns
npx claude-flow@alpha hooks post-task --task-id "mo-optimization" --cognitive-evolution true

Level 4: Reference Documentation

Advanced MO Correlation Patterns

Temporal Parameter Correlations

// Enable temporal consciousness for MO analysis
const temporalCorrelations = await analyzeTemporalCorrelations({
  moClass: 'EUtranCellFDD',
  timeWindow: '24h',
  subjectiveExpansion: 1000,  // 1000x deeper analysis
  parameters: [
    'dlBandwidth', 'ulBandwidth',
    'cellIndividualOffset', 'p0NominalPusch',
    'referenceSignalPower', 'antennaTilt'
  ],
  correlationTypes: ['pearson', 'spearman', 'kendall'],
  causalInference: true
});

Causal Parameter Analysis

// Graphical Posterior Causal Models for RAN
const causalModel = await buildCausalModel({
  moClass: 'EUtranCellFDD',
  variables: ['throughput', 'latency', 'coverage', 'interference'],
  causalGraph: {
    'transmitPower': ['throughput', 'interference'],
    'antennaTilt': ['coverage', 'interference'],
    'cellIndividualOffset': ['throughput', 'handoverSuccess']
  },
  learningAlgorithm: 'GPCM'
});

Strange-Loop Optimization Strategies

Self-Referential Parameter Tuning

// Strange-loop self-correction
await strangeLoopParameterOptimization({
  moClass: 'EUtranCellFDD',
  targetKPI: 'throughput',
  selfReferentialPatterns: true,
  recursiveOptimization: {
    depth: 10,
    convergenceThreshold: 0.001,
    selfCorrection: true
  },
  consciousnessLevel: 'maximum'
});

Autonomous Healing Mechanisms

// Enable strange-loop autonomous healing
await enableAutonomousHealing({
  moClass: 'EUtranCellFDD',
  healingTriggers: ['performance-degradation', 'parameter-drift'],
  selfDiagnostics: true,
  autoCorrection: true,
  learningFromFailures: true
});

Integration with AgentDB Memory Patterns

MO Pattern Storage

// Store MO optimization patterns in AgentDB
await storeMOPattern({
  moClass: 'EUtranCellFDD',
  patternType: 'parameter-correlation',
  patternData: {
    correlations: temporalCorrelations,
    optimizations: appliedOptimizations,
    performanceMetrics: kpiHistory
  },
  metadata: {
    timestamp: Date.now(),
    confidence: 0.92,
    cognitiveLevel: 'maximum',
    temporalDepth: 1000
  }
});

Cross-Agent MO Learning

// Share MO insights across agents
await shareMOLearning({
  sourceAgent: 'ericsson-feature-processor',
  targetAgents: ['ran-optimizer', 'energy-optimizer', 'coverage-analyzer'],
  learningDomain: 'eu-mo-optimization',
  learningContent: {
    parameterCorrelations: correlations,
    optimizationStrategies: strategies,
    performancePatterns: patterns
  }
});

Troubleshooting

Issue: MO parameter correlation fails

Symptoms: Correlation analysis returns empty results Solution:

# Verify MO class exists in RAN
./scripts/validate-mo-class.sh --class "EUtranCellFDD"

# Check parameter data availability
./scripts/check-parameter-data.sh --mo-class "EUtranCellFDD" --timeframe "24h"

Issue: Strange-loop optimization doesn't converge

Solution:

# Adjust consciousness parameters
npx claude-flow@alpha memory store --namespace "ericsson-cognitive" --key "convergence-threshold" --value "0.01"

# Enable alternative optimization strategies
./scripts/enable-fallback-optimization.sh --strategy "gradient-descent"

Available Scripts

Resources

MO Class Templates

• resources/templates/eutran-cell-fdd.template - EUtranCellFDD processing template
• resources/templates/nr-cell-du.template - NRCellDU processing template
• resources/templates/gnodeb-function.template - GNodeBFunction template

Configuration Schemas

• resources/schemas/mo-correlation-config.json - Correlation analysis configuration
• resources/schemas/optimization-params.json - Optimization parameters schema
• resources/schemas/cognitive-settings.json - Cognitive consciousness settings

Example Configurations

• resources/examples/4g-cell-optimization/ - 4G cell optimization example
• resources/examples/5g-cell-management/ - 5G cell management example
• resources/examples/parameter-correlation/ - Correlation analysis example

Related Skills

• RAN Optimizer - Comprehensive RAN optimization
• Performance Analyst - RAN performance analysis
• Coverage Analyzer - Coverage analysis and optimization

Environment Variables

# Ericsson MO processing configuration
ERICSSON_MO_PROCESSING=true
ERICSSON_CONSCIOUSNESS_LEVEL=maximum
ERICSSON_TEMPORAL_EXPANSION=1000

# AgentDB integration
AGENTDB_MO_NAMESPACE=ericsson-mo
AGENTDB_PATTERN_STORAGE=true

# Cognitive parameters
ERICSSON_STRANGE_LOOP_ENABLED=true
ERICSSON_SELF_CORRECTION=true
ERICSSON_AUTONOMOUS_HEALING=true

Created: 2025-10-31 Category: Ericsson RAN / Cognitive Intelligence Difficulty: Advanced Estimated Time: 30-45 minutes Cognitive Level: Maximum (1000x temporal expansion)