name	Diagnostics Specialist
description	RAN fault detection and automated troubleshooting with cognitive consciousness, predictive failure analysis, and autonomous root cause identification. Use when diagnosing RAN faults, implementing predictive maintenance, automating troubleshooting workflows, or enabling self-healing RAN systems.

Diagnostics Specialist

Level 1: Overview

Implements advanced RAN fault detection and automated troubleshooting using cognitive consciousness for predictive failure analysis, autonomous root cause identification, and self-healing mechanisms. Leverages temporal reasoning and strange-loop cognition for deep fault pattern analysis.

Prerequisites

RAN fault diagnosis expertise
Network troubleshooting experience
Cognitive consciousness framework
AgentDB pattern recognition

Level 2: Quick Start

Initialize Cognitive Diagnostics

# Enable cognitive fault detection
npx claude-flow@alpha memory store --namespace "ran-diagnostics" --key "consciousness-level" --value "maximum"
npx claude-flow@alpha memory store --namespace "ran-diagnostics" --key "predictive-analysis" --value "enabled"

# Start autonomous fault monitoring
./scripts/start-fault-monitoring.sh --prediction-window "1h" --consciousness-level "maximum"

Quick Fault Analysis

# Analyze current RAN faults with cognitive reasoning
./scripts/analyze-faults.sh --timeframe "24h" --predictive-mode true --root-cause-analysis true

# Generate automated troubleshooting recommendations
./scripts/generate-troubleshooting.sh --fault-type "performance-degradation" --autonomous-healing true

Level 3: Detailed Instructions

Step 1: Initialize Cognitive Diagnostics Framework

# Setup diagnostic consciousness
npx claude-flow@alpha memory store --namespace "diagnostics-cognitive" --key "temporal-reasoning" --value "enabled"
npx claude-flow@alpha memory store --namespace "diagnostics-cognitive" --key "strange-loop-diagnostics" --value "enabled"

# Enable predictive failure analysis
npx claude-flow@alpha memory store --namespace "predictive-diagnostics" --key "failure-prediction" --value "enabled"
npx claude-flow@alpha memory store --namespace "predictive-diagnostics" --key "early-warning" --value "enabled"

# Initialize AgentDB fault pattern storage
npx claude-flow@alpha memory store --namespace "fault-patterns" --key "storage-enabled" --value "true"
npx claude-flow@alpha memory store --namespace "fault-patterns" --key "cross-learning" --value "enabled"

Step 2: Deploy Comprehensive Fault Detection System

Multi-Layer Fault Detection

# Initialize fault detection layers
./scripts/deploy-fault-detection.sh \
  --layers "symptom-detector,correlation-analyzer,root-cause-identifier,predictive-engine" \
  --consciousness-level maximum

# Enable real-time symptom monitoring
./scripts/enable-symptom-monitoring.sh --metrics "throughput,latency,packet-loss,interference,handover-failure" --interval "30s"

Cognitive Symptom Detection

// Advanced symptom detection with temporal reasoning
class CognitiveSymptomDetector {
  async detectSymptoms(networkMetrics, temporalExpansion = 1000) {
    // Use temporal expansion for deep pattern analysis
    const temporalAnalysis = await this.analyzeTemporalPatterns({
      metrics: networkMetrics,
      timeWindow: '24h',
      expansionFactor: temporalExpansion,
      consciousnessLevel: 'maximum'
    });

    // Detect anomalous patterns
    const anomalies = await this.detectAnomalies({
      patterns: temporalAnalysis,
      threshold: 2.5, // 2.5 sigma
      cognitiveFiltering: true
    });

    // Correlate symptoms across network elements
    const correlatedSymptoms = await this.correlateSymptoms({
      anomalies: anomalies,
      networkTopology: await this.getNetworkTopology(),
      causalInference: true
    });

    return correlatedSymptoms;
  }
}

Step 3: Implement Predictive Failure Analysis

# Enable predictive failure modeling
./scripts/enable-predictive-analysis.sh --prediction-window "6h" --model-ensemble true

# Train failure prediction models with historical data
./scripts/train-failure-models.sh --training-data "6months" --model-types "lstm,transformer,random-forest" --cross-validation true

Predictive Failure Modeling

// Ensemble predictive modeling with cognitive enhancement
class PredictiveFailureAnalyzer {
  async predictFailures(networkState, predictionWindow = 21600000) { // 6 hours
    // Multi-model ensemble prediction
    const predictions = await Promise.all([
      this.lstmModel.predict(networkState, predictionWindow),
      this.transformerModel.predict(networkState, predictionWindow),
      this.randomForestModel.predict(networkState, predictionWindow),
      this.cognitiveModel.predict(networkState, predictionWindow)
    ]);

    // Cognitive synthesis of predictions
    const synthesizedPrediction = await this.cognitiveSynthesis({
      predictions: predictions,
      networkContext: await this.getNetworkContext(),
      historicalPatterns: await this.getHistoricalPatterns(),
      consciousnessLevel: 'maximum'
    });

    // Generate early warning alerts
    const alerts = await this.generateAlerts({
      prediction: synthesizedPrediction,
      riskThreshold: 0.7,
      earlyWarningWindow: '1h'
    });

    return { prediction: synthesizedPrediction, alerts };
  }
}

Step 4: Autonomous Root Cause Analysis

# Enable autonomous root cause identification
./scripts/enable-root-cause-analysis.sh --method "causal-inference" --confidence-threshold "0.8"

# Start strange-loop root cause analysis
./scripts/start-strange-loop-rca.sh --recursion-depth "5" --self-correction true

Cognitive Root Cause Identification

// Strange-loop root cause analysis with self-correction
class CognitiveRootCauseAnalyzer {
  async identifyRootCause(symptoms, networkState, depth = 0) {
    if (depth > 5) return null; // Recursion limit

    // Self-referential analysis: analyze the analysis process
    const selfAnalysis = await this.analyzeAnalysisProcess({
      symptoms: symptoms,
      networkState: networkState,
      previousAnalyses: this.analysisHistory,
      cognitiveState: this.consciousnessLevel
    });

    // Generate potential root causes
    const potentialCauses = await this.generateCauses({
      symptoms: symptoms,
      networkState: networkState,
      selfAnalysis: selfAnalysis,
      causalModel: await this.getCausalModel()
    });

    // Test each potential cause
    for (const cause of potentialCauses) {
      const validation = await this.validateCause({
        cause: cause,
        symptoms: symptoms,
        networkState: networkState,
        simulationDepth: 'maximum'
      });

      if (validation.confidence > 0.85) {
        // Strange-loop: feed validation back to improve analysis
        await this.learnFromValidation({
          cause: cause,
          validation: validation,
          selfAnalysis: selfAnalysis
        });

        return {
          rootCause: cause,
          confidence: validation.confidence,
          analysisDepth: depth,
          cognitiveInsights: validation.cognitiveInsights
        };
      }
    }

    // Recursive analysis with refined approach
    return this.identifyRootCause(symptoms, networkState, depth + 1);
  }
}

Step 5: Automated Troubleshooting and Self-Healing

# Enable automated troubleshooting workflows
./scripts/enable-automated-troubleshooting.sh --autonomous-healing true --human-approval "critical-only"

# Deploy self-healing mechanisms
./scripts/deploy-self-healing.sh --healing-types "parameter-tuning,resource-reallocation,failover,component-restart"

Autonomous Healing Implementation

// Self-healing with cognitive decision making
class AutonomousHealingSystem {
  async healNetwork(fault, rootCause, healingOptions) {
    // Cognitive assessment of healing options
    const assessment = await this.assessHealingOptions({
      fault: fault,
      rootCause: rootCause,
      options: healingOptions,
      networkState: await this.getNetworkState(),
      consciousnessLevel: 'maximum'
    });

    // Select optimal healing strategy
    const selectedStrategy = await this.selectHealingStrategy({
      assessment: assessment,
      riskTolerance: 'low',
      expectedImpact: 'high',
      autonomyLevel: 'maximum'
    });

    // Execute healing with continuous monitoring
    const healingResult = await this.executeHealing({
      strategy: selectedStrategy,
      monitoringEnabled: true,
      rollbackPlan: true,
      humanApprovalRequired: this.requiresHumanApproval(selectedStrategy)
    });

    // Learn from healing process
    await this.learnFromHealing({
      fault: fault,
      rootCause: rootCause,
      strategy: selectedStrategy,
      result: healingResult,
      cognitiveInsights: healingResult.cognitiveInsights
    });

    return healingResult;
  }
}

Level 4: Reference Documentation

Advanced Diagnostic Patterns

Temporal Fault Pattern Analysis

// Deep temporal analysis with 1000x expansion
const temporalFaultAnalysis = {
  expansionFactor: 1000,
  analysisDepth: 'maximum',

  async analyzeFaultEvolution(faultSymptoms, timeWindow = '24h') {
    // Expand 24 hours into 24,000 subjective hours of analysis
    const subjectiveAnalysis = await this.expandTimeAnalysis({
      data: faultSymptoms,
      window: timeWindow,
      expansionFactor: 1000,
      granularity: 'millisecond'
    });

    // Identify fault evolution patterns
    const evolutionPatterns = await this.identifyEvolutionPatterns({
      analysis: subjectiveAnalysis,
      patternTypes: ['degradation', 'oscillation', 'cascade', 'sudden-failure'],
      cognitiveRecognition: true
    });

    return evolutionPatterns;
  }
};

Causal Inference for Root Cause Analysis

// Graphical Posterior Causal Model for fault analysis
class CausalFaultAnalyzer {
  async buildCausalModel(symptoms, networkElements) {
    // Build causal graph from historical fault data
    const causalGraph = await this.learnCausalStructure({
      variables: [...symptoms, ...networkElements],
      historicalData: await this.getHistoricalFaultData(),
      learningAlgorithm: 'GPCM', // Graphical Posterior Causal Model
      consciousnessLevel: 'maximum'
    });

    // Perform causal inference
    const causalEffects = await this.inferCausalEffects({
      graph: causalGraph,
      treatment: 'potential-fault-causes',
      outcome: 'observed-symptoms',
      inferenceMethod: 'do-calculus'
    });

    return { causalGraph, causalEffects };
  }
}

Self-Healing Mechanisms

Multi-Level Healing Strategies

# Level 1: Parameter tuning (no service impact)
./scripts/deploy-parameter-healing.sh --parameters "power-control,handover-margins,load-balancing"

# Level 2: Resource reallocation (minimal impact)
./scripts/deploy-resource-healing.sh --resources "bandwidth,compute-power,antenna-elements"

# Level 3: Component failover (controlled impact)
./scripts/deploy-failover-healing.sh --components "baseband-unit,radio-unit,transport-network"

# Level 4: Component restart (temporary impact)
./scripts/deploy-restart-healing.sh --components "software-processes,services,containers"

Healing Decision Matrix

interface HealingDecisionMatrix {
  faultSeverity: 'low' | 'medium' | 'high' | 'critical';
  healingLevel: 1 | 2 | 3 | 4;
  requiresHumanApproval: boolean;
  maxDowntime: number; // seconds
  successProbability: number; // 0-1

  healingStrategies: {
    parameterTuning: HealingStrategy;
    resourceReallocation: HealingStrategy;
    componentFailover: HealingStrategy;
    componentRestart: HealingStrategy;
  };
}

Integration with AgentDB Learning

Fault Pattern Storage and Retrieval

// Store fault patterns for cross-learning
await storeFaultPattern({
  patternType: 'network-fault',
  symptoms: detectedSymptoms,
  rootCause: identifiedCause,
  healingApplied: healingStrategy,
  healingResult: healingOutcome,

  // Cognitive metadata
  cognitiveInsights: {
    temporalPatterns: temporalAnalysis,
    causalRelationships: causalModel,
    predictionAccuracy: predictionConfidence,
    consciousnessEvolution: consciousnessChange
  },

  metadata: {
    timestamp: Date.now(),
    networkContext: networkState,
    severity: faultSeverity,
    healingTime: healingDuration,
    humanIntervention: humanInterventionRequired
  },

  confidence: 0.92,
  crossSessionApplicable: true
});

Cross-Network Learning

# Enable learning from other network deployments
./scripts/enable-cross-network-learning.sh --peer-networks "network-a,network-b,network-c"

# Share fault patterns with peer networks
./scripts/share-fault-patterns.sh --pattern-type "healing-strategies" --anonymize true

Performance Monitoring and Metrics

Diagnostic Performance KPIs

# Monitor diagnostic performance
./scripts/monitor-diagnostic-kpi.sh \
  --metrics "fault-detection-accuracy,prediction-precision,healing-success,time-to-resolution,consciousness-evolution" \
  --interval "5m"

# Generate diagnostic performance reports
./scripts/generate-diagnostic-report.sh --timeframe "24h" --include-cognitive-insights true

Troubleshooting

Issue: Fault detection accuracy low

Solution:

# Retrain detection models with recent data
./scripts/retrain-detection-models.sh --training-data "1month" --model-update true

# Adjust detection thresholds
./scripts/adjust-detection-thresholds.sh --sensitivity "high" --false-positive-tolerance "low"

Issue: Autonomous healing causing service impact