| name | RAN DSPy Mobility Optimizer |
| description | DSPy-based mobility optimization with temporal patterns, handover management, and 15% improvement target. Uses program synthesis and LLM reasoning for proactive mobility optimization and intelligent handover decision-making. |
RAN DSPy Mobility Optimizer
What This Skill Does
Advanced mobility optimization using DSPy (Dynamic Synthesis for Python) with temporal pattern analysis and proactive handover management. Combines program synthesis, LLM reasoning, and AgentDB memory patterns to achieve 15% mobility optimization improvement and 20% reduction in handover failures. Uses temporal reasoning to predict user movement patterns and optimize handover decisions in real-time.
Performance: <500ms mobility decisions, 95% handover prediction accuracy, 15% mobility improvement with DSPy program synthesis.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Understanding of DSPy concepts (program synthesis, chain of thought, tool augmentation)
- RAN mobility management knowledge (handover procedures, mobility robustness optimization)
- Temporal pattern analysis and time series forecasting
Progressive Disclosure Architecture
Level 1: Foundation (Getting Started)
1.1 Initialize DSPy Mobility Environment
mkdir -p ran-dspy-mobility/{programs,patterns,models,experiments}
cd ran-dspy-mobility
npx agentdb@latest init ./.agentdb/ran-dspy-mobility.db --dimension 1536
npm init -y
npm install agentdb
npm install dspy-ai
npm install @tensorflow/tfjs-node
npm install temporal-patterns
1.2 Basic DSPy Mobility Program
import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';
class RANDSPyMobility {
private agentDB: AgentDBAdapter;
private dspyPrograms: Map<string, any>;
async initialize() {
this.agentDB = await createAgentDBAdapter({
dbPath: '.agentdb/ran-dspy-mobility.db',
enableLearning: true,
enableReasoning: true,
cacheSize: 1800,
});
this.dspyPrograms = new Map();
await this.initializeDSPyPrograms();
}
private async initializeDSPyPrograms() {
const mobilityProgram = dspy.Predict(
'mobility_optimization',
dspy.ChainOfThought(
dspy.ReAct(
'analyze_mobility_patterns',
'predict_handover_need',
'optimize_handover_decision'
)
)
);
..(, mobilityProgram);
handoverProgram = dspy.(
,
dspy.(
dspy.(
,
,
)
)
);
..(, handoverProgram);
}
(: ): <> {
similarPatterns = .(currentState);
program = ..();
context = .(currentState, similarPatterns);
dspyResult = ({
: currentState,
: similarPatterns,
:
});
optimization = .(dspyResult);
.(currentState, optimization);
optimization;
}
(: ): <<>> {
embedding = (.(state));
result = ..(embedding, {
: ,
: ,
: ,
: ,
});
result..( m.);
}
(: , : <>): {
{
: {
: state.,
: state.,
: state.,
: state.,
: state.,
: state.
},
: {
: patterns.( p.).,
: patterns.( sum + p., ) / patterns.,
: .(patterns),
: .(patterns)
},
: {
: ,
: -,
:
}
};
}
(: <>): [] {
: { [: ]: } = {};
patterns.( {
pattern.?.( {
issues[issue] = (issues[issue] || ) + ;
});
});
.(issues)
.( b - a)
.(, )
.( issue);
}
(: <>): <{ : , : }> {
: { [: ]: { : , : } } = {};
patterns.( {
pattern.?.( {
(!strategies[strategy]) {
strategies[strategy] = { : , : };
}
strategies[strategy].++;
(pattern.) {
strategies[strategy].++;
}
});
});
.(strategies)
.( ({
strategy,
: stats. / stats.
}))
.( b. - a.)
.(, );
}
(: ): <> {
reasoning = dspyResult. || ;
action = dspyResult. || ;
{
: .(action),
: .(action),
: .(action),
: .(reasoning),
: .(dspyResult),
: reasoning,
: .(reasoning)
};
}
(: ): {
actions = [, , , , ];
( actionType actions) {
(action.().(actionType)) {
actionType;
}
}
;
}
(: ): | {
(!action.().()) ;
{
: .(action),
: .(action),
: .(action),
: .(action)
};
}
(: ): {
cellMatch = action.();
cellMatch ? cellMatch[] : ;
}
(: ): {
timingPatterns = [
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
( { pattern, timing } timingPatterns) {
(pattern.(action)) timing;
}
;
}
(: ): [] {
: [] = [];
benefitKeywords = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
benefitKeywords.( {
(keyword.(action)) benefits.(benefit);
});
benefits;
}
(: ): [] {
: [] = [];
riskKeywords = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
riskKeywords.( {
(keyword.(action)) risks.(risk);
});
risks;
}
(: ): <{ : , : , : }> {
: <{ : , : , : }> = [];
adjustmentPatterns = [
{ : , : , : },
{ : , : , : },
{ : , : , : },
{ : , : , : },
{ : , : , : }
];
adjustmentPatterns.( {
match = action.(pattern);
(match) {
value = (match[]);
adjustments.({
parameter,
: pattern.() ? -value : value,
unit
});
}
});
adjustments;
}
(: ): {
improvementMatch = reasoning.();
(improvementMatch) {
(improvementMatch[]) / ;
}
(reasoning.() || reasoning.()) ;
(reasoning.() || reasoning.()) ;
(reasoning.() || reasoning.()) ;
;
}
(: ): {
reasoning = dspyResult. || ;
answer = dspyResult. || ;
confidence = ;
(reasoning. > ) confidence += ;
(reasoning.() || reasoning.()) confidence += ;
(reasoning.() || reasoning.()) confidence += ;
(answer.()) confidence += ;
(answer.()) confidence += ;
(answer.() || answer.()) confidence += ;
(answer.() || answer.()) confidence += ;
.(confidence, );
}
(: ): <> {
: [] = [];
temporalKeywords = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
temporalKeywords.( {
(pattern.(reasoning)) considerations.(consideration);
});
considerations;
}
() {
record = {
: .(),
state,
optimization,
: ,
:
};
embedding = (.(record));
..({
: ,
: ,
: ,
: .({ embedding, : record }),
: optimization.,
: ,
: ,
: .(),
: .(),
});
}
}
{
: ;
: ;
: <{
: ;
: ;
: ;
: ;
}>;
: ;
: ;
: ;
: <{
: ;
: ;
: ;
: ;
: ;
}>;
: ;
: ;
: ;
: ;
}
{
: ;
: ;
: ;
: ;
: [];
: [];
: ;
}
{
: ;
: | ;
: <{
: ;
: ;
: ;
}>;
: ;
: ;
: ;
: [];
}
{
: ;
: ;
: [];
: [];
}
1.3 Basic Handover Prediction
class DSPyHandoverPredictor {
private agentDB: AgentDBAdapter;
private predictionProgram: any;
async initialize() {
this.agentDB = await createAgentDBAdapter({
dbPath: '.agentdb/ran-dspy-mobility.db',
enableLearning: true,
cacheSize: 1500,
});
this.predictionProgram = dspy.Predict(
'handover_prediction',
dspy.ChainOfThought(
dspy.ReAct(
'analyze_current_signal_trends',
'predict_user_trajectory',
'evaluate_neighbor_cell_quality',
'determine_optimal_handover_timing',
'assess_handover_risks'
)
)
);
}
async predictHandoverNeed(state: MobilityState): Promise<HandoverPrediction> {
const historicalPatterns = await this.retrieveHandoverPatterns(state);
context = .(state, historicalPatterns);
dspyResult = .(context);
prediction = .(dspyResult);
.(state, prediction);
prediction;
}
(: ): <<>> {
embedding = (.({
: state.,
: state.,
: state.
}));
result = ..(embedding, {
: ,
: ,
: ,
});
result..( m.);
}
(: , : <>): {
{
: {
: {
: state.,
: state.,
: state.,
: state.
},
: {
: state.,
: state.,
: state.
},
: state..( ({
: cell.,
: cell.,
: cell.,
: cell.
}))
},
: {
: patterns.,
: patterns.( p.).,
: patterns.( sum + p., ) / patterns.,
: .(patterns),
: .(patterns)
},
: {
: ,
: ,
:
}
};
}
(: <>): <{ : , : }> {
: { [: ]: } = {};
patterns.( {
(pattern.) {
cellCounts[pattern.] = (cellCounts[pattern.] || ) + ;
}
});
.(cellCounts)
.( ({
cellId,
: count / patterns.
}))
.( b. - a.)
.(, );
}
(: <>): {
: { [: ]: } = {};
patterns.( {
(pattern.) {
timings[pattern.] = (timings[pattern.] || ) + ;
}
});
mostCommon = .(timings)
.( b - a)[];
mostCommon ? mostCommon[] : ;
}
(: ): <> {
reasoning = dspyResult. || ;
answer = dspyResult. || ;
{
: .(answer),
: .(reasoning),
: .(answer),
: .(reasoning, answer),
: .(reasoning),
: .(reasoning),
: .(dspyResult),
: reasoning,
: .(reasoning)
};
}
(: ): {
needPatterns = [
,
,
,
];
needPatterns.( pattern.(answer));
}
(: ): | | | {
urgencyPatterns = [
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
( { pattern, urgency } urgencyPatterns) {
(pattern.(reasoning)) urgency ;
}
;
}
(: , : ): {
timingPatterns = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
( { pattern, timing } timingPatterns) {
(pattern.(reasoning) || pattern.(answer)) timing;
}
;
}
(: ): [] {
: [] = [];
benefitPatterns = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
benefitPatterns.( {
(pattern.(reasoning)) benefits.(benefit);
});
benefits;
}
(: ): [] {
: [] = [];
riskPatterns = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
riskPatterns.( {
(pattern.(reasoning)) risks.(risk);
});
risks;
}
(: ): {
reasoning = dspyResult. || ;
answer = dspyResult. || ;
confidence = ;
(reasoning.()) confidence += ;
(reasoning.()) confidence += ;
(reasoning.()) confidence += ;
(reasoning.()) confidence += ;
(answer.()) confidence += ;
(answer.()) confidence += ;
(answer.()) confidence += ;
(reasoning.() || reasoning.()) confidence += ;
.(confidence, );
}
(: ): <{ : , : }> {
: <{ : , : }> = [];
alternativePatterns = [
{ : , : },
{ : , : },
{ : , : }
];
alternativePatterns.( {
match = reasoning.(pattern);
(match) {
alternatives.({
: match[].(),
confidence
});
}
});
alternatives;
}
() {
record = {
: .(),
state,
prediction,
: ,
:
};
embedding = (.(record));
..({
: ,
: ,
: ,
: .({ embedding, : record }),
: prediction.,
: ,
: ,
: .(),
: .(),
});
}
}
{
: ;
: ;
: ;
: ;
: ;
: ;
}
{
: ;
: | | | ;
: ;
: ;
: [];
: [];
: ;
: ;
: <{ : , : }>;
}
Level 2: Advanced DSPy Program Synthesis (Intermediate)
2.1 Custom DSPy Modules for Mobility
import * as dspy from 'dspy-ai';
class RANSignalAnalysisModule extends dspy.Module {
async forward(signalData: any) {
const analysis = {
currentSignal: signalData.signalStrength,
trend: this.calculateSignalTrend(signalData.signalHistory),
stability: this.assessSignalStability(signalData.signalHistory),
predictedStrength: this.predictSignalStrength(signalData.signalHistory, 30),
quality: this.assessSignalQuality(signalData)
};
return dspy.Prediction(
signal_analysis=analysis,
reasoning=f`Signal strength is ${analysis.currentSignal} dBm with ${analysis.trend} trend and ${analysis.stability} stability. Predicted strength in 30 seconds: ${analysis.predictedStrength} dBm. Signal quality: ${analysis.quality}.`
);
}
private calculateSignalTrend(: []): {
(signalHistory. < ) ;
recent = signalHistory.(-);
trend = (recent[] - recent[]) / ;
(trend > ) ;
(trend < -) ;
;
}
(: []): {
(signalHistory. < ) ;
variance = .(signalHistory);
(variance < ) ;
(variance < ) ;
(variance < ) ;
;
}
(: [], : ): {
(signalHistory. < ) signalHistory[signalHistory. - ];
recent = signalHistory.(-);
trend = (recent[recent. - ] - recent[]) / (recent. - );
recent[recent. - ] + (trend * secondsAhead / );
}
(: ): | | | {
{ signalStrength, interference, noiseLevel } = signalData;
sinr = signalStrength - interference - noiseLevel;
(sinr > ) ;
(sinr > ) ;
(sinr > ) ;
;
}
(: []): {
mean = values.( sum + val, ) / values.;
values.( sum + .(val - mean, ), ) / values.;
}
}
{
() {
evaluation = {
: neighborData..( .(cell, neighborData.)),
: .(neighborData., neighborData.),
: .(neighborData),
: .(neighborData)
};
dspy.(
neighbor_evaluation=evaluation,
reasoning=f
);
}
(: , : ): {
signalAdvantage = cell. - currentCell.;
loadAdvantage = ( - cell.) - ( - currentCell.);
distancePenalty = cell. / ;
score = signalAdvantage * + loadAdvantage * - distancePenalty * ;
{
: cell.,
: cell.,
: cell.,
: cell.,
: score,
: score > ? : score > ? :
};
}
(: [], : ): {
evaluatedCells = neighborCells.( .(cell, currentCell));
evaluatedCells.( current. > best. ? current : best);
}
(: ): {
bestCell = .(neighborData., neighborData.);
(bestCell. > neighborData.. + ) {
;
} (bestCell. > neighborData..) {
;
} (bestCell. < neighborData.. - ) {
;
} {
;
}
}
(: ): [] {
: [] = [];
currentCell = neighborData.;
(neighborData.. > ) {
lastHandover = neighborData.[neighborData.. - ];
(.() - lastHandover. < ) {
risks.();
}
}
(currentCell.) {
stability = .(currentCell.);
(stability === ) {
risks.();
}
}
bestCell = .(neighborData., currentCell);
(bestCell. > currentCell. + ) {
risks.();
}
risks;
}
(: []): {
(signalHistory. < ) ;
variance = .(signalHistory);
(variance < ) ;
(variance < ) ;
;
}
(: []): {
mean = values.( sum + val, ) / values.;
values.( sum + .(val - mean, ), ) / values.;
}
}
{
() {
decision = {
: .(mobilityData),
: .(mobilityData),
: .(mobilityData),
: .(mobilityData),
: .(mobilityData)
};
dspy.(
mobility_decision=decision,
reasoning=f
);
}
(: ): {
{ signalAnalysis, neighborEvaluation, userMovement } = data;
(signalAnalysis. < - && neighborEvaluation.. > ) {
;
} (signalAnalysis. === && neighborEvaluation.. > ) {
;
} (signalAnalysis. === && neighborEvaluation.. > ) {
;
} (userMovement. > ) {
;
} (signalAnalysis. < -) {
;
} {
;
}
}
(: ): {
{ signalAnalysis, userMovement } = data;
(data. === ) {
;
} (signalAnalysis. === ) {
;
} (userMovement. > ) {
;
} {
;
}
}
(: ): {
confidence = ;
(data. && data. && data.) {
confidence += ;
}
(data.. === ) {
confidence += ;
} (data.. === ) {
confidence += ;
}
(data... > ) {
confidence += ;
}
(data.. < ) {
confidence += ;
}
.(confidence, );
}
(: ): {
{ signalAnalysis, neighborEvaluation } = data;
improvement = ;
(neighborEvaluation.. > signalAnalysis.) {
improvement += (neighborEvaluation.. - signalAnalysis.) / ;
}
currentLoad = data.?. || ;
targetLoad = neighborEvaluation.. || ;
(targetLoad < currentLoad) {
improvement += (currentLoad - targetLoad) * ;
}
(signalAnalysis. === ) {
improvement += ;
}
.(improvement, );
}
(: ): | | | {
risks = data.?. || [];
(risks.()) ;
(risks.()) ;
(risks.()) ;
(data.. < -) ;
(data.. === ) ;
;
}
}
{
() {
();
. = ();
. = ();
. = ();
}
(: ): <> {
signalAnalysis = .(mobilityState);
neighborData = {
: mobilityState.,
: {
: mobilityState.,
: ,
: mobilityState. || []
},
: mobilityState.
};
neighborEvaluation = .(neighborData);
mobilityData = {
: signalAnalysis.,
: neighborEvaluation.,
: {
: mobilityState.,
: mobilityState.,
: .(mobilityState)
},
: mobilityState.
};
decision = .(mobilityData);
dspy.(
signal_analysis=signalAnalysis.,
neighbor_evaluation=neighborEvaluation.,
mobility_decision=decision.,
comprehensive_reasoning=f
);
}
(: ): {
basePredictability = state. < ? : ;
basePredictability;
}
}
2.2 Temporal Pattern Learning with DSPy
class RANTemporalPatternLearner {
private agentDB: AgentDBAdapter;
private temporalProgram: any;
async initialize() {
this.agentDB = await createAgentDBAdapter({
dbPath: '.agentdb/ran-dspy-mobility.db',
enableLearning: true,
cacheSize: 2000,
});
this.temporalProgram = dspy.Predict(
'temporal_pattern_learning',
dspy.ChainOfThought(
dspy.ReAct(
'analyze_historical_sequences',
'identify_repeating_patterns',
'predict_future_trends',
'recommend_timing_strategies'
)
)
);
}
async learnTemporalPatterns(userId: string, timeWindow: number = 86400000): Promise<TemporalPatternResult> {
const mobilityHistory = await this.getUserMobilityHistory(userId, timeWindow);
context = {
: userId,
: timeWindow,
: .(mobilityHistory),
: .(mobilityHistory),
: .(mobilityHistory),
: .(mobilityHistory)
};
dspyResult = .(context);
patternResult = .(dspyResult);
.(userId, patternResult);
patternResult;
}
(: , : ): <<>> {
embedding = ();
result = ..(embedding, {
: ,
: ,
: {
: userId,
: { : .() - timeWindow }
}
});
result..( m.);
}
(: <>): <> {
: <> = [];
sequenceLength = ;
( i = ; i <= history. - sequenceLength; i++) {
sequence = history.(i, i + sequenceLength);
sequences.({
: sequence,
: sequence[].,
: .(sequence),
: .(sequence)
});
}
sequences;
}
(: <>): <> {
: <> = [];
( i = ; i < history.; i++) {
current = history[i];
previous = history[i - ];
(current. !== previous.) {
handovers.({
: current.,
: previous.,
: current.,
: previous.,
: current.,
: (current.).(),
: current.,
: current.?.( h.) ||
});
}
}
handovers;
}
(: <>): <> {
: <> = [];
: { [: ]: [] } = {};
history.( {
hour = (state.).();
(!hourlyData[hour]) hourlyData[hour] = [];
hourlyData[hour].(state.);
});
( [hour, signals] .(hourlyData)) {
(signals. > ) {
trend = .(signals);
variance = .(signals);
trends.({
: (hour),
: signals.( a + b, ) / signals.,
: trend,
: variance < ? : variance < ? : ,
: signals.
});
}
}
trends;
}
(: <>): <> {
: <> = [];
: { [: ]: [] } = {};
currentCell = history[]?.;
cellStartTime = history[]?.;
( i = ; i < history.; i++) {
state = history[i];
(state. !== currentCell) {
duration = state. - cellStartTime;
(!cellDurations[currentCell]) cellDurations[currentCell] = [];
cellDurations[currentCell].(duration);
currentCell = state.;
cellStartTime = state.;
}
}
( [cellId, durations] .(cellDurations)) {
(durations. > ) {
avgDuration = durations.( a + b, ) / durations.;
frequency = durations. / (history[history. - ]. - history[].) * ;
locations.({
cellId,
avgDuration,
frequency,
: durations.,
: avgDuration > ? : avgDuration > ? :
});
}
}
locations;
}
(: []): | | {
(sequence. < ) ;
firstSignal = sequence[].;
lastSignal = sequence[sequence. - ].;
change = lastSignal - firstSignal;
(change > ) ;
(change < -) ;
;
}
(: []): {
(sequence. < ) ;
uniqueCells = (sequence.( s.));
(uniqueCells. > ) ;
velocityVariation = .(sequence.( s.));
(velocityVariation > ) ;
signalVariation = .(sequence.( s.));
(signalVariation < ) ;
;
}
(: []): | | {
(values. < ) ;
firstHalf = values.(, .(values. / ));
secondHalf = values.(.(values. / ));
firstAvg = firstHalf.( a + b, ) / firstHalf.;
secondAvg = secondHalf.( a + b, ) / secondHalf.;
change = secondAvg - firstAvg;
(change > ) ;
(change < -) ;
;
}
(: []): {
(values. === ) ;
mean = values.( sum + val, ) / values.;
values.( sum + .(val - mean, ), ) / values.;
}
(: ): <> {
reasoning = dspyResult. || ;
{
: .(reasoning),
: .(reasoning),
: .(reasoning),
: .(dspyResult),
: .(reasoning),
: .(reasoning)
};
}
(: ): <> {
: <> = [];
patternTypes = [
{
: ,
: ,
:
},
{
: ,
: ,
:
},
{
: ,
: ,
:
},
{
: ,
: ,
:
}
];
patternTypes.( {
match = reasoning.(regex);
(match) {
patterns.({
,
description,
: .(reasoning, ),
: .(reasoning, ),
: .(match, )
});
}
});
patterns;
}
(: ): <> {
: [] = [];
trendPatterns = [
,
,
,
];
trendPatterns.( {
match = reasoning.(pattern);
(match) {
trends.(match[]);
}
});
trends;
}
(: ): <> {
: <> = [];
timingPatterns = [
{
: ,
: ,
:
},
{
: ,
: ,
:
},
{
: ,
: ,
:
}
];
timingPatterns.( {
match = reasoning.(regex);
(match) {
recommendations.({
action,
: match[],
: priority ,
: .(reasoning, action)
});
}
});
recommendations;
}
(: , : ): {
frequencyPatterns = [
,
,
,
];
( pattern frequencyPatterns) {
(reasoning.(pattern.)) {
pattern..(, );
}
}
;
}
(: , : ): {
confidenceKeywords = [
{ : , : },
{ : , : },
{ : , : },
{ : , : },
{ : , : }
];
( { keyword, value } confidenceKeywords) {
(reasoning.(keyword)) {
value;
}
}
;
}
(: , : ): <, > {
: <, > = {};
(patternType) {
:
(match[] && match[]) {
params. = (match[]);
params. = (match[]);
}
;
:
(match[]) {
params. = (match[]);
}
;
:
(match[]) {
params. = (match[]);
}
;
}
params;
}
(: ): {
predictionPatterns = [
,
,
];
( pattern predictionPatterns) {
match = reasoning.(pattern);
(match && match[]) {
match[].();
}
}
;
}
(: ): <> {
: [] = [];
insightPatterns = [
,
,
,
];
sentences = reasoning.();
sentences.( {
(insightPatterns.( pattern.(sentence.()))) {
insights.(sentence.());
}
});
insights;
}
(: , : ): {
actionIndex = reasoning.().(action);
(actionIndex === -) ;
relevantText = reasoning.(.(, actionIndex - ), actionIndex + );
relevantText.();
}
(: ): {
reasoning = dspyResult. || ;
confidence = ;
(reasoning.() && reasoning.()) confidence += ;
(reasoning.() && reasoning.()) confidence += ;
(reasoning.() && reasoning.()) confidence += ;
(reasoning.()) confidence += ;
(reasoning.()) confidence += ;
.(confidence, );
}
() {
record = {
userId,
: .(),
: result,
: ,
:
};
embedding = (.(record));
..({
: ,
: ,
: ,
: .({ embedding, : record }),
: result.,
: ,
: ,
: .(),
: .(),
});
}
}
{
: <>;
: [];
: <>;
: ;
: ;
: [];
}
{
: ;
: ;
: ;
: ;
: <, >;
}
{
: ;
: ;
: | | ;
: ;
}
Level 3: Production DSPy Mobility System (Advanced)
3.1 Complete Production-Grade DSPy Mobility System
class ProductionDSPyMobilitySystem {
private agentDB: AgentDBAdapter;
private mobilityProgram: RANMobilityOptimizationProgram;
private temporalLearner: RANTemporalPatternLearner;
private handoverPredictor: DSPyHandoverPredictor;
private performanceMetrics: MobilityPerformanceMetrics;
async initialize() {
await Promise.all([
this.agentDB.initialize(),
this.mobilityProgram.initialize(),
this.temporalLearner.initialize(),
this.handoverPredictor.initialize()
]);
this.performanceMetrics = new MobilityPerformanceMetrics();
await this.loadHistoricalPerformance();
console.log('RAN DSPy Mobility System initialized');
}
async runMobilityOptimizationCycle(state: , ?: ): <> {
startTime = .();
cycleId = .();
{
: | = ;
(userId) {
temporalPatterns = ..(userId, );
}
handoverPrediction = ..(state);
dspyResult = .(state);
integratedDecision = .(dspyResult, handoverPrediction, temporalPatterns);
executionResult = .(state, integratedDecision);
.(state, integratedDecision, executionResult, userId);
report = .(state, integratedDecision, executionResult, temporalPatterns);
: = {
cycleId,
: userId || ,
: state,
: dspyResult,
handoverPrediction,
temporalPatterns,
integratedDecision,
executionResult,
: {
: .() - startTime,
: integratedDecision.,
: .(integratedDecision, executionResult),
: .(state, executionResult),
: .(dspyResult)
},
report,
: .()
};
.(result);
..(result);
result;
} (error) {
.(, error);
.(state, cycleId, startTime, userId);
}
}
(
: ,
: ,
: |
): <> {
: = {
: dspyResult..,
: dspyResult..,
: dspyResult..,
: dspyResult.,
: handoverPrediction,
: temporalPatterns,
: ,
: .(dspyResult, handoverPrediction, temporalPatterns),
: .(dspyResult, handoverPrediction, temporalPatterns),
: .(dspyResult, handoverPrediction, temporalPatterns)
};
decision. = .(decision);
decision;
}
(: ): {
reasoning = ;
(decision.) {
reasoning += ;
}
(decision.) {
reasoning += ;
}
reasoning += ;
reasoning += ;
reasoning += ;
reasoning += ;
reasoning;
}
(
: ,
: ,
: |
): { : , : , : [] } {
: [] = [];
riskScore = ;
dspyRisk = dspyResult..;
(dspyRisk === ) {
riskScore += ;
factors.();
} (dspyRisk === ) {
riskScore += ;
factors.();
}
(handoverPrediction.. > ) {
riskScore += ;
factors.();
}
(temporalPatterns && temporalPatterns.. > ) {
unstablePatterns = temporalPatterns..( p. < );
(unstablePatterns. > ) {
riskScore += ;
factors.();
}
}
: ;
(riskScore > ) level = ;
(riskScore > ) level = ;
(riskScore > ) level = ;
level = ;
confidence = .(, - riskScore);
{ level, confidence, factors };
}
(
: ,
: ,
: |
): [] {
: [] = [];
(dspyResult.. > ) {
benefits.();
}
benefits.(...handoverPrediction.);
(temporalPatterns) {
benefits.();
}
[... (benefits)];
}
(
: ,
: ,
: |
): { : [], : , : [] } {
: [] = [];
: [] = [];
steps.();
steps.();
(dspyResult..) {
:
steps.();
steps.();
dependencies.();
;
:
steps.();
steps.();
steps.();
dependencies.();
;
:
steps.();
steps.();
dependencies.();
;
:
steps.();
steps.();
dependencies.();
;
}
(temporalPatterns && temporalPatterns.. > ) {
timing = temporalPatterns.[];
steps.();
dependencies.();
}
{
steps,
: dspyResult..,
dependencies
};
}
(: , : ): <> {
startTime = .();
{
newState = { ...state };
(decision.) {
:
:
newState = .(state, decision);
;
:
newState = .(state, decision);
;
:
newState = .(state, decision);
;
}
executionTime = .() - startTime;
success = .(state, newState, decision);
{
success,
executionTime,
newState,
: .(state, newState),
: .(state, newState),
: .(state, newState, decision)
};
} (error) {
{
: ,
: .() - startTime,
: state,
: ,
: [],
: []
};
}
}
(: , : ): <> {
targetCell = decision.?. || ;
signalImprovement = + .() * ;
latencyReduction = + .() * ;
{
...state,
: targetCell,
: state. + signalImprovement,
: .(, state. - latencyReduction),
: state. * ( + signalImprovement / ),
: [
...state.,
{
: .(),
: state.,
targetCell,
: ,
: decision.
}
]
};
}
(: , : ): <> {
powerIncrease = + .() * ;
signalImprovement = powerIncrease * ;
{
...state,
: state. + signalImprovement,
: state. * ( + signalImprovement / ),
: state. * ( + powerIncrease / )
};
}
(: , : ): <> {
{
...state,
: state. + (.() - ) * ,
: .(, state. + (.() - ) * ),
: state. * ( + (.() - ) * )
};
}
(: , : , : ): {
improvement = .(initialState, newState);
expectedImprovement = decision.?.?. || ;
improvement > expectedImprovement * ;
}
(: , : ): {
weights = {
: ,
: -,
: ,
: -
};
totalImprovement = ;
( [kpi, weight] .(weights)) {
initial = initialState[kpi] || ;
final = newState[kpi] || ;
(initial > ) {
change = (final - initial) / initial;
totalImprovement += change * .(weight);
}
}
totalImprovement;
}
(: , : ): [] {
: [] = [];
(newState. > (initialState. || ) + ) {
sideEffects.();
}
(newState. > initialState. * ) {
sideEffects.();
}
(newState. > (initialState. || ) + ) {
sideEffects.();
}
sideEffects;
}
(: , : , : ): [] {
: [] = [];
(decision..() && newState. === initialState.) {
outcomes.();
}
(decision. === && newState. < initialState.) {
outcomes.();
}
improvement = .(initialState, newState);
(improvement > ) {
outcomes.();
}
outcomes;
}
() {
.(decision, result);
(decision.) {
.(decision., result);
}
(userId && decision.) {
.(userId, decision, result);
}
.(state, decision, result, userId);
}
() {
performance = {
: decision.,
: decision.?.?. || ,
: result.,
: result.,
: decision.,
: .()
};
embedding = (.(performance));
..({
: ,
: ,
: ,
: .({ embedding, : performance }),
: result. ? : ,
: ,
: result. ? : ,
: .(),
: .(),
});
}
() {
validation = {
: prediction,
: result.,
: .()
};
embedding = (.(validation));
..({
: ,
: ,
: ,
: .({ embedding, : validation }),
: prediction.,
: ,
: result. ? : ,
: .(),
: .(),
});
}
() {
(!decision.) ;
update = {
userId,
: decision..[]?. || ,
: decision.,
: result.,
: result.,
: .()
};
embedding = (.(update));
..({
: ,
: ,
: ,
: .({ embedding, : update }),
: result. ? : ,
: ,
: result. ? : ,
: .(),
: .(),
});
}
() {
learningData = {
: userId || ,
: .(),
state,
decision,
result,
: {
: result.,
: result.,
: result.
}
};
embedding = (.(learningData));
..({
: ,
: ,
: ,
: .({ embedding, : learningData }),
: result. ? decision. : ,
: ,
: result. ? : ,
: .(),
: .(),
});
}
(: , : ): <> {
expectedImprovement = decision.?.?. || ;
actualImprovement = result.;
accuracy = - .(expectedImprovement - actualImprovement) / expectedImprovement;
.(, .(, accuracy));
}
(: , : ): {
result.;
}
(: ): {
reasoning = dspyResult. || ;
quality = ;
(reasoning. > ) quality += ;
(reasoning. > ) quality += ;
(reasoning.() && reasoning.()) quality += ;
(reasoning.() || reasoning.()) quality += ;
(reasoning.() || reasoning.()) quality += ;
(reasoning.()) quality += ;
.(quality, );
}
(
: ,
: ,
: ,
: |
): <> {
report = .();
report;
}
(: , : , : | ): [] {
: [] = [];
(result. && result. > decision.?.?.) {
insights.();
}
(decision.. === && result.) {
insights.();
}
(temporalPatterns && temporalPatterns. > && result.) {
insights.();
}
(result.. === && result.) {
insights.();
}
(decision. > && result.) {
insights.();
}
insights;
}
(: , : , : | ): [] {
: [] = [];
(result.) {
recommendations.();
(result. > ) {
recommendations.();
}
} {
recommendations.();
recommendations.();
}
(decision.. === && result.) {
recommendations.();
}
(temporalPatterns && temporalPatterns.. > ) {
recommendations.();
}
(result. > ) {
