Cognitive Radio-assisted Self-Healing NW Architecture Automatic Troubleshooting System

Abstract

The invention relates to a Cognitive Radio (CR)-based Self-Healing Network (SHN) architecture that enables fault detection, automated problem solving, and network improvement in mobile telecommunications networks. The invention presents an autonomous network management infrastructure that detects dynamic changes in the radio frequency (RF) environment, identifies network performance anomalies in real-time, automatically performs root cause analysis (RCA), and implements corrective actions without human intervention. The invention offers an integrated solution that intelligently detects eNodeB / gNB failures, cell outages, RF interference sources, capacity problems, coverage holes, and performance degradations at the Radio Access Network (RAN) level, optimizes spectrum utilization, allocates alternative frequencies, performs power adjustments, and dynamically reconfigures the network topology (self-configuration, self-optimization, self-healing).

Description

1 TARIFF Cognitive Radio-assisted Self-Healing NW Architecture Automatic Troubleshooting The system Technical Area 5 The invention enables fault detection, automated problem solving, and networking in mobile telecommunications networks. Cognitive Radio (CR) based Self-Healing Network (SHN) architecture that enables improvement It is related to. State of the Art 10 Nowadays, network failures in mobile networks are usually detected by alarm systems. This is being done and manually by Network Operations Center (NOC) teams. is being resolved. The time it takes from fault detection to resolution (Mean Time To Repair - MTTR (Mean Time Between Arrivals and Delays) typically ranges from 2 to 8 hours. During this delay, users... service interruptions are occurring, operators are experiencing revenue loss, and customer satisfaction is decreasing by 15. It is falling. Network management systems (NMS) create alarm floods. It generates information but fails to identify the root cause of the problem. An eNodeB Hundreds of alarms are generated in the event of a failure, but the causality between these alarms is unclear. Causal relationships cannot be analyzed automatically. NOC teams alert 20. He has to do the correlation manually. Cell outages, especially partial outages or degraded mode These conditions cannot be detected quickly enough in current systems. Sleeping cell problem In a situation known as [specific condition], the cell appears active but is unable to provide service to users, and This situation goes unnoticed for hours. 25 External interference sources are detected in existing systems. It is either impossible to detect or takes a very long time to detect. This is called interference hunting. The process requires drive test teams to go into the field and is quite costly. Traditional mobile networks use static frequency planning. It uses licensed spectrum bands that are pre-allocated, and in case of failure or high demand, 30 Dynamic switching to alternative spectrum sources is not possible under traffic conditions. This This situation represents a critical deficiency, particularly in disaster recovery scenarios. When a malfunction occurs in one cell, neighboring cells automatically respond. It cannot be reconfigured to close the coverage gap. Antenna tilt adjustment. Transmit power optimization and frequency reforming operations are performed manually. 35 is being carried out. 2 Current systems use a reactive approach - intervention after a failure occurs. Hardware degradation, temperature increases, and electrical issues are all contributing factors. Fault precursors, such as power supply problems, cannot be detected and proactive measures are not taken. Predictive maintenance cannot be performed. Self-Organizing Network (SON) functions operate independently of each other and conflict 5 Resolution mechanisms are insufficient. Self-healing capability is lacking within the SON ecosystem. It is not integrated. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention The invention represents a new breakthrough in this field, unlike the structures used in existing technology. The aim is to create a structure with different technical specifications that bring these elements together. The main purpose of the invention is to detect faults and automatically troubleshoot problems in mobile telecommunication networks. Self-Healing Network 15, based on Cognitive Radio (CR), provides solution and network improvement. The goal is to present (SHN) architecture. Another aim of the invention is to create a network that detects dynamic changes in the radio frequency (RF) environment. Real-time detection of performance anomalies and root cause analysis (Root Cause Analysis) Analysis (RCA) is automatically performed and corrective actions are taken without human intervention. The goal is to provide an autonomous network management infrastructure that implements this. 20 Another aim of the invention is to detect eNodeB / gNB failures at the Radio Access Network (RAN) level. cell outage, RF interference sources, capacity By intelligently identifying problems, coverage holes, and performance degradations, Optimizing spectrum usage, allocating alternative frequencies, and adjusting power settings. performs and dynamically reconstructs the network topology (self-25 Cognitive Radio aims to provide an integrated solution (configuration, self-optimization, self-healing). thanks to their capabilities, they can utilize vacant frequency channels in licensed and unlicensed spectrum bands. Dynamic Spectrum Access (DSA) strategies can detect (spectrum holes). It can implement and automatically switch to backup frequency sources in case of malfunctions. This approach is able to do so. This approach, instead of traditional reactive network management, is proactive and predictive. 30 It creates a (predictive) self-healing paradigm. Figures that will help understand the invention. Şekil 1, buluşa konu olan sistemin genel mimarisini göstermektedir. 35 3 Parça Referanslarının Açıklaması 1.Multi-Interface Data Collection Layer 2.Cognitive Radio Spectrum Sensing Module 3.Network Performance Monitoring Engine 4.Alarm Management System 5 5.Data Preprocessing and Normalization Module 6.Anomaly Detection Engine 7.Cell Outage Detection Module 8.Interference Analysis Module 9.Fault Classification and Categorization System 10 10.Root Cause Analysis (RCA) Engine 11.Historical Fault Database 12.Cognitive Decision Making Engine 13.Healing Action Repository 14.Dynamic Spectrum Allocation Module 15 15.RF Parameter Optimization Engine 16.Network Topology Reconfiguration Module 17.Action Orchestration and Execution Layer 18.Configuration Management Interface 19.Healing Effectiveness Validator 20 20.Feedback Loop and Learning Module 21.SON Coordination Interface 22.Visualization and Reporting Dashboard Detailed Description of the Invention 25 In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. This is intended to facilitate understanding and will not impose any limiting effects. The invention is mobile. fault detection, automated problem solving, and network improvement in telecommunication networks. It is a Cognitive Radio (CR) based Self-Healing Network (SHN) architecture that provides radio Detecting dynamic changes in the frequency (RF) environment, it accurately portrays network performance anomalies. automatic root cause analysis (RCA) that detects root causes in a timely manner. an autonomous network that performs and implements corrective actions without human intervention It provides management infrastructure. The invention solves eNodeB / gNB failures and cell interruptions at the Radio Access Network (RAN) level. (cell outage), RF interference sources, capacity problems, 35 By intelligently detecting coverage holes and performance degradations, the spectrum 4 optimizing usage, allocating alternative frequencies, performing power adjustments, and Dynamically reconstructing the network topology (self-configuration, self- It offers an integrated solution (optimization, self-healing). Thanks to its Cognitive Radio capabilities, the system can detect empty spectrum bands in both licensed and unlicensed spectrum. Dynamic Spectrum Access (DSA) 5 is able to detect frequency channels (spectrum holes). They are able to implement strategies and automatically switch to backup frequency resources in case of failures. It is possible to make the transition. This approach, instead of traditional reactive network management, is proactive and It creates a predictive self-healing paradigm. In the preferred application of the invention, data is collected from the network via multiple interfaces, each S1-MME (control plane signaling), 10, includes a dedicated parser and protocol handler for the interface. X2-AP (inter-eNodeB communication), SNMP traps (alarm notifications), NETCONF / YANG (configuration data), Streaming Telemetry (high-frequency performance metrics) data There is a multi-interface data collection layer (1) that collects data. The invention utilizes Software Defined Radio (SDR) hardware to transmit data in the 700 MHz - 6 GHz band. continuous scanning, Energy detection, cyclostationary feature detection and matched filtering 15 Power Spectral Density (PSD) analysis algorithms detect spectrum occupancy. By localizing interference sources, it provides 100 MHz instantaneous bandwidth and 10 ms response time. Cognitive radio spectrum sensing module that creates spectrum map during measurement period (2) is located. In invention, RACH success rate, RRC connection establishment success rate, ERAB setup 20 success rate, handover success rate, throughput, latency, packet loss, PRB utilization, CQI Collecting KPIs like distribution in real time, statistical baseline and for each metric There is a network performance monitoring engine (3) that calculates the dynamic threshold. The invention involves critical, major, minor, and warning severity levels from network elements. Collecting alarms, applying intelligent filtering in alarm flooding situations, duplicate 25 suppression, event correlation and alarm enrichment (adding context) operations There is an alarm management system (4) that performs this. The invention uses timestamps that homogenize heterogeneous data from different sources. synchronization, unit conversion (dBm, Watt, percentage), missing value imputation (linear interpolation, forward fill), outlier removal (IQR method, Z-score), feature scaling (min-max 30 data preprocessing that performs normalization and standardization operations. There is a normalization module (5). The invention describes a system that runs multiple anomaly detection algorithms in parallel using statistical methods (Z- score, modified Z-score, Tukey's method), time-series methods (ARIMA residual analysis, STL decomposition, exponential smoothing), ML methods (Isolation Forest, One-Class SVM, 35 Generating a final anomaly score via ensemble voting using autoencoder-based detection. There is an anomaly detection engine (6). The invention uses specialized algorithms for cell outage detection, with direct indicators (no RACH preamble detection, zero successful RRC setups, no active bearers), indirect indicators (sudden neighbor cell load increase, handover failure spike, UE attachment 5 multi-criteria scoring for sleeping cell detection (RACH_success < 10%, RRC_success < 20%, Throughput < 5% of expected) by partial outage detection There is a cell outage detection module (7) that analyzes degradation patterns. In the invention, interference is achieved by working integrated with the cognitive radio spectrum sensing module (2). classification by, co-channel interference (same frequency), adjacent channel 10 interference, detecting external interference (radar, satellite, illegal transmitters), time- Interference signature with frequency analysis (STFT - Short-Time Fourier Transform) geolocation algorithms that perform extraction (TDOA - Time Difference of Arrival, AOA - Angle interference analysis module (8) which performs interference source localization with (of Arrival) It is located. 15 The invention categorizes the identified problems, including hardware faults (baseband unit failure, RF failure, etc.). unit failure, antenna system failure, power supply issue), software faults (process crash, memory leak, configuration error), environmental (high temperature, poor backhaul), external (interference, vandalism) classified as such, using the ITU-T M.3400 fault taxonomy, severity assessment (critical (service-affecting), major (graded performance), minor 20 (potential future issue)) yapan fault classification and categorization sistemi (9) bulunmaktadır. Buluşta, multi-method RCA approach kullanan, Bayesian Networks (probabilistic graphical model ile cause-effect relationships modeling, conditional probability hesaplamaları), Decision Trees (CART (Classification and Regression Trees) ile symptom-to-cause 25 mapping), Rule-based Expert System (domain knowledge encoded as IF-THEN rules (300+ rules)), Causal Inference (Granger causality test, transfer entropy ile temporal dependencies) ile ensemble fusion yaparak %92-96 accuracy elde eden root cause analysis (RCA) engine (10) bulunmaktadır. Buluşta, geçmiş fault events'leri structured format'ta saklayan, fault_type, root_cause, 30 symptoms, applied_actions, resolution_time, success_status, KPI_before, KPI_after verilerini tutan, time-series database (InfluxDB) + document store (MongoDB) hybrid architecture kullanan, fault_type, cell_id, timestamp indeksleme yapan, 2 years detailed data, 5 years aggregated statistics retention policy uygulayan, case-based reasoning için knowledge base oluşturan historical fault database (11) bulunmaktadır. 35 6 Buluşta, Reinforcement Learning framework ile optimal healing strategies öğrenen, Markov Decision Process (MDP) formulation (State = (fault_type, network_conditions, resource_availability), Action = (healing_strategy), Reward = (recovery_success, recovery_time, service_impact)) kullanan, Q-Learning ve Deep Q-Network (DQN) algoritmaları ile action-value function approximation yapan, experience replay buffer (100K 5 samples) ile off-policy learning gerçekleştiren, epsilon-greedy exploration strategy (ε=0.1) ile exploration-exploitation balance sağlayan cognitive decision making engine (12) bulunmaktadır. Buluşta, healing action templates library içeren, RF optimization actions (power adjustment (±3dB steps), antenna tilt change (±5 degree), azimuth rotation), spectrum actions (carrier 10 switch, frequency refarming, DSA activation), topology actions (neighbor list update, handover parameter tuning, cell activation / deactivation), hardware actions (soft reset, failover to backup unit, remote diagnostics) içeren, her action için preconditions, parameters, expected impact, rollback procedure tanımlı olan healing action repository (13) bulunmaktadır. 15 Buluşta, cognitive radio modülü (2) tarafından tespit edilen spectrum holes'larda dynamic spectrum access sağlayan, Licensed Shared Access (LSA) protocol compliance sunan, TV White Space (TVWS), which connects to the spectrum marketplace with the spectrum broker interface Using FCC / Ofcom standards for database queries, channel bonding and carrier aggregation. Reconfiguring, power control and sensing threshold 20 for interference mitigation. Dynamic spectrum includes primary user protection mechanisms that perform adjustments. There is an allocation module (14). The invention uses a Genetic Algorithm that optimizes RF parameters for coverage gap compensation. (GA) based multi-objective optimization (maximize coverage, minimize interference, maintain QoS) using objective function (F = w1×coverage_score + w2×(1-interference_level) + 25 working with w3×throughput_improvement), constraints (max_power_limit, tilt_range [-10°, Proposed changes impact with simulation engine, including +10°], regulatory compliance) prediction maker, gradual rollout strategy (test on 5% cells, A / B testing, full deployment) There is an RF parameter optimization engine (15) that implements it. The invention describes Automatic Neighbor 30, which dynamically reconfigures the network topology in the event of a malfunction. Relation (ANR) updates (deletion of failed cell from neighbor list, addition of new cells to neighboring cells) addition of neighbor), X2 interface establishment (alternative routing paths), handover parameter adjustment (hysteresis reduction for neighboring cells, Time-to-Trigger decrease), cell range expansion (offset values ​​tuning) with graph theory algorithms network topology reconfiguration module that calculates optimal connectivity matrix (16) 35 It is located. 7 The invention features a workflow engine (BPMN (Business Process Management) that orchestrates healing actions. Model and Notation) based orchestration), parallel execution (independent actions concurrent çalıştırılır), sequential execution (dependent actions ordered execution), transaction management (ACID properties, rollback capability), timeout handling (action execution monitoring, stuck action detection), retry logic (exponential backoff strategy (3 5 retries, 2x backoff multiplier)) içeren action orchestration and execution layer (17) bulunmaktadır. Buluşta, network element'lere configuration changes push eden, NETCONF / YANG (modern interface standardized data models ile), TR-069 (CWMP) (legacy eNodeB'ler için), CLI automation (Expect scripts, Ansible playbooks), SNMP SET (simple parameter changes) 10 kullanan, version control (configuration backup before change, diff tracking, rollback capability) sağlayan configuration management interface (18) bulunmaktadır. Buluşta, healing action'larının effectiveness'ini validate eden, post-healing verification window (5-15 dakika) içinde KPI monitoring (fault_resolved (boolean), KPI_improvement (percentage), service_impact_reduction) yapan, success criteria (alarms cleared, KPIs 95% 15 of baseline, no recurring faults for 1 hour, user complaints decrease) kullanan, validation failure durumunda alternative healing strategy trigger eden veya manual escalation yapan healing effectiveness validator (19) bulunmaktadır. Buluşta, sistem performance'ını sürekli iyileştiren, outcome analysis (successful vs failed healing attempts statistical analysis), model retraining (ML models monthly retrain with new 20 data), policy update (RL agent policy parameters fine-tuning), threshold adaptation (anomaly detection thresholds seasonal adjustment), rule refinement (expert system rules update incremental learning for online learning based on false positive / negative analysis) There is a feedback loop and learning module (20) that uses algorithms. The invention incorporates ANR (neighbor relation optimization 25), which coordinates with other SON functions. (in this case, sharing cell outage information), MLB (load balancing actions and healing actions) conflict resolution), MRO (RF parameter changes synchronization with handover optimization), CCO (healing-triggered configuration changes coordination with capacity expansion), energy saving (sleeping cells energy-saving initiated sleep vs fault-induced sleep differentiation) It operates with functions such as priority arbitration (self-healing > MLB > MRO > energy saving) 30 There is a SON coordination interface (21) that does this. The invention provides a GUI for NOC operators, showing real-time status (active faults, ongoing healing). actions, system health indicators), network topology view (cell status color-coded (green=healthy, yellow=degraded, red=outage), geographic map overlay), timeline view (fault detection-to-resolution timeline visualization), analytics (healing success rate trends, MTTR 35 statistics, most common faults, cognitive radio spectrum utilization), reports 8 (daily / weekly / monthly automated reports, customizable dashboards) sunan, REST API ile 3rd-party integration support sağlayan visualization and reporting dashboard (22) bulunmaktadır. Buluşun tercih edilen uygulamasında, multi-interface data collection layer (1), S1-MME interface üzerinden UE Context Release messages, Initial Context Setup Failure indications 5 toplar. X2-AP üzerinden Handover Failure messages, Load Information exchanges alır. Equipment alarms (power supply failure, high temperature, RF unit fault) with SNMP traps It captures the messages. It runs a message parser (ASN.1 decoder, SNMP PDU decoder) for each interface. The collected data is published to the message queue (Apache Kafka topics): s1mme_events, x2ap_events, snmp_traps, kpi_metrics. 10 In the preferred application of the invention, the cognitive radio spectrum sensing module (2), USRP The B210 SDR (Universal Software Radio Peripheral) hardware covers the 700 MHz - 6 GHz band. scans. FFT (Fast Fourier Transform) size 2048, overlap 50%, Hann window function Performs PSD estimation using Energy detection threshold: Pth = σ_n² × (Q⁻¹(Pfa) / √N + 1) Calculated using the formula (σ_n²: noise variance, Pfa: false alarm probability, N: sample count). 15 Calculates the Spectral Correlation Function (SCF) for cyclostationary feature detection: S_α(f) = E[X(f)X*(f-α)] (α: cyclic frequency). Spectrum for 100 MHz bandwidth every 10 ms Generates occupancy map: binary matrix [time × frequency] format. In the preferred application of the invention, the network performance monitoring engine (3) monitors each cell Collects performance counters for: RACH attempts, RACH successes, RRC connection 20 requests, RRC connection setups, RRC connection reestablishments. Every 15 minutes KPI calculations: RACH_SR = (RACH_success / RACH_attempts) × 100%, RRC_SR = (RRC_setups / RRC_requests) × 100%. Statistical baseline calculation: 7-day moving average, standard deviation. Dynamic threshold: threshold_t = baseline_t ± k × σ_t (k=3 for anomaly detection). 25 In the preferred application of the invention, alarm management system (4), alarm flooding It detects the status: if there are >100 alarms in 1 minute, flooding detected. Alarm Correlation rules apply: When an "eNodeB_unreachable" alarm occurs, all signals from the same eNodeB should be monitored. child alarms are suppressed (duplicate elimination). Alarm enrichment: cell_id to each alarm, site_location, vendor_type, hardware_version metadata is added. Alarm severity auto-30 escalation: Escalate to 3 major alarms → 1 critical alarm (for same root cause). In the preferred application of the invention, the data preprocessing and normalization module (5), It uses NTP (Network Time Protocol) reference for timestamp synchronization, all Converts events to UTC timestamp, millisecond precision. Missing value imputation: If RACH success rate data is missing, linear interpolation: y_t = y_(t-1) + ((y_(t+1) - y_(t-1)) / 2). 35 Outlier detection: The IQR (Interquartile Range) method is used, Q1 - 1.5×IQR > value or 9 If value > Q3 + 1.5×IQR, it is marked as an outlier and winsorization applied (cap at 5th / 95th percentile). In the preferred application of the invention, the anomaly detection engine (6) is used for RACH success rate. Z-score calculation: z = (x - μ) / σ, if |z| > 3, indicates an anomaly. For time-series anomaly detection. STL (Seasonal-Trend decomposition using Loess) decomposition: X_t = T_t + S_t + R_t 5 (trend + seasonal + residual). If the absolute value of the residual component exceeds the threshold anomaly: |R_t| > 3 × MAD(R) (MAD: Median Absolute Deviation). Isolation Forest algorithm: anomaly score = 2^(-E(h(x)) / c(n)) (h: path length, c: average path length normalization). Ensemble voting: If 3 / 5 algorithms detect anomalies, set final_anomaly to True. In the preferred application of the invention, the cell outage detection module (7) uses multi-criteria scoring 10 system ile sleeping cell detect eder. Criteria: C1 = RACH_success_rate < 10%, C2 = RRC_connection_success_rate < 20%, C3 = Throughput < 5% × expected_throughput, C4 = Active_UE_count = 0, C5 = Neighbor_cell_load_increase > 30%. Cell outage score: COS = Σ(w_i × C_i), weights: [0.3, 0.25, 0.2, 0.15, 0.1]. Eğer COS > 0.7 ise cell outage confirmed. Partial outage detection: 0.4 < COS < 0.7 ise degraded mode. Detection latency target: 1-3 15 dakika (achieved 95% of time). Buluşun tercih edilen uygulamasında, interference analysis modülü (8), cognitive radio spectrum sensing data (2) kullanarak interference classification yapar. Co-channel interference detection: PSD spike in serving frequency ± 200 kHz. Adjacent channel interference: PSD elevation in adjacent carrier (±10 MHz). External interference signature 20 matching: radar pulse patterns (pulse width, pulse repetition interval), satellite downlink continuous wave. Geolocation için TDOA (Time Difference of Arrival) algorithm: hyperbolic equations solve edilerek interference source coordinates estimate edilir. Accuracy: urban area 50-200m, suburban 100-500m. Buluşun tercih edilen uygulamasında, fault classification and categorization sistemi (9), 25 detected anomalies ve alarms'ları ITU-T M.3400 taxonomy'ye göre classify eder. Hardware faults subcategories: RF_unit_failure (alarm code 201), BBU_failure (202), antenna_VSWR_high (203), power_supply_fault (204). Software faults: process_crash (301), memory_exhaustion (302), software_mismatch (303). Environmental: temperature_high (401), humidity_high (402), backhaul_degradation (403). Impact analysis: service_affecting 30 (SA) vs non-service_affecting (NSA) classification based on active user impact. Buluşun tercih edilen uygulamasında, root cause analysis (RCA) engine (10), Bayesian Network inference çalıştırır. Network structure: 50 nodes (symptoms, intermediate causes, root causes), 120 edges (causal relationships). Inference: Variable Elimination algorithm ile posterior probability calculation P(root_cause | observed_symptoms). Örnek inference: 35 P(RF_unit_failure | RACH_SR_drop=True, RSRP_drop=True, Alarms=[RF_fault]) = 0.87. Decision Tree (CART) ile rule extraction: IF (RACH_SR < 10% AND alarm_type = "RF_fault") THEN root_cause = "RF_unit_failure" (confidence 0.85). Expert System: rule base query ile matching rules retrieve edilir, conflict resolution strategy: highest confidence rule selected. Buluşun tercih edilen uygulamasında, historical fault database (11), her fault event'i structured document olarak saklar: {fault_id, timestamp, cell_id, fault_type, symptoms: 5 [RACH_SR_drop, alarms], root_cause, applied_actions: [action1, action2], resolution_time: 180s, success: True, KPI_before: {RACH_SR: 85%, RRC_SR: 90%}, KPI_after: {RACH_SR: 95%, RRC_SR: 96%}}. MongoDB collection: faults, index: composite index on (fault_type, cell_id, timestamp). Time-series KPI data InfluxDB'de: measurement = "cell_kpis", tags = {cell_id, kpi_name}, field = kpi_value. Aggregation queries ile statistics: average resolution 10 time per fault type, success rate trends. Buluşun tercih edilen uygulamasında, cognitive decision making engine (12), Reinforcement Learning agent training yapar. State space: s = (fault_type: enum[10 types], network_load: float[0-1], time_of_day: int[0-23], neighbor_cell_status: vector[5], spectrum_availability: float[0-1]). Action space: a = (healing_strategy: enum[20 strategies], parameters: dict). 15 Reward function: R = w1×(1 if fault_resolved else -1) + w2×(-resolution_time / max_time) + w3×(-service_impact) + w4×(KPI_improvement). Q-Learning update: Q(s,a) ← Q(s,a) + α[R + γ max_a' Q(s',a') - Q(s,a)] (α=0.1: learning rate, γ=0.95: discount factor). Experience replay buffer: store (s, a, r, s') tuples, sample random mini-batch (size 64) for training. Buluşun tercih edilen uygulamasında, healing action repository (13), action templates JSON 20 format'ta saklanır: {"action_id": "RF_power_boost", "type": "RF_optimization", "parameters": {"power_increase_dB": [1, 2, 3], "duration_minutes": [10, 30, 60]}, "preconditions": {"adjacent_cell_interference_level": "<-90dBm"}, "expected_impact": {"coverage_increase": "5-10%", "interference_risk": "low"}, "rollback": {"action": "restore_previous_power", "timeout": "15min"}}. Repository size: 45 distinct action types, 200+ parametrized variants. Template 25 selection: based on fault_type mapping table + current network conditions filtering. Buluşun tercih edilen uygulamasında, dynamic spectrum allocation modülü (14), cognitive radio spectrum sensing data (2) kullanarak available spectrum identify eder. TV White Space database query: FCC database API call ile available channels (470-698 MHz). Licensed Shared Access: spectrum broker API ile temporary spectrum lease request. Decision 30 algorithm: spectrum_score = f(bandwidth, signal_quality, interference_level, regulatory_compliance, cost). Carrier aggregation reconfiguration: primary carrier failed ise, secondary carrier promote to primary, available spectrum'dan yeni secondary carrier assign. NETCONF / YANG message ile eNodeB'ye RRC reconfiguration push edilir. Buluşun tercih edilen uygulamasında, RF parameter optimization engine (15), coverage gap 35 compensation için Genetic Algorithm çalıştırır. Chromosome encoding: [power_cell1, 11 tilt_cell1, power_cell2, tilt_cell2,...] for neighboring cells. Fitness function: F = coverage_area × (1 - interference_probability) × throughput_factor. Coverage estimation: propagation model (COST-231 Hata for urban) ile path loss calculation, coverage polygon generation (GIS integration). Interference calculation: inter-cell interference matrix based on antenna patterns. GA parameters: population_size=100, generations=50, crossover_rate=0.8, 5 mutation_rate=0.1. Convergence: typical 20-30 generations için optimal solution bulunur. Buluşun tercih edilen uygulamasında, network topology reconfiguration modülü (16), failed cell için neighbor relation updates yapar. ANR (Automatic Neighbor Relation) table modification: remove failed_cell from all neighbor lists (X2-AP: Cell Activation Request with cell_unavailable indication). Komşu cellere new neighbor additions: graph traversal algorithm 10 ile 2-hop neighbors identify edilir, yeni neighbor relations establish (X2 Setup Request messages). Handover parameter optimization: neighboring cells için TTT (Time-to-Trigger) reduction (512ms → 256ms), hysteresis decrease (3dB → 1.5dB) ile faster handover trigger. Cell Individual Offset (CIO) adjustment: neighbor cells için +3dB offset ile UE'leri çekme tendency artırılır. 15 Buluşun tercih edilen uygulamasında, action orchestration and execution layer (17), healing actions için execution workflow oluşturur. Workflow example for cell_outage: Step1: [verify_fault_persistent (5s timeout)], Step2: [parallel: {acquire_spectrum_resource, calculate_RF_optimization, update_ANR_tables}], Step3: [sequential: {push_RF_config → validate_config → activate_changes}], Step4: [monitor_KPI (300s duration)]. BPMN engine 20 (Camunda) ile workflow execution. Transaction management: compensation actions defined for each step (rollback scenario). Distributed tracing (OpenTelemetry) ile end-to-end latency tracking: 95th percentile latency 120 seconds for complex healing workflows. Buluşun tercih edilen uygulamasında, configuration management interface (18), NETCONF protocol ile eNodeB configuration push eder. NETCONF session establishment: SSH 25 transport over port 830, hello message exchange (capability negotiation). YANG data model: ietf-interfaces, ietf-lte-ran custom augmentations. Edit-config operation: ... RPC message. Configuration example: RF power change için 12340. Commit confirmation mechanism: ile 120 saniye içinde validation yapılmazsa auto-rollback. TR-069 legacy devices için: CPE WAN Management Protocol (CWMP) SetParameterValues RPC. 30 Buluşun tercih edilen uygulamasında, healing effectiveness validator (19), post-healing KPI monitoring yapar. Validation window: healing action completion sonrası 5 dakika. KPI collection: same metrics as baseline (RACH_SR, RRC_SR, throughput, PRB utilization). Success criteria evaluation: (KPI_after / KPI_baseline) > 0.95 for critical KPIs. Alarm status check: eğer original alarms cleared ise validation_alarm_status = True. Statistical 35 significance test: paired t-test ile KPI before vs after comparison, p-value < 0.05 requirement. 12 Validation failure handling: eğer 2 consecutive validation failures ise alternative_strategy_trigger veya manual_escalation (ticket creation in ITSM system). Buluşun tercih edilen uygulamasında, feedback loop and learning modülü (20), healing outcomes analysis yaparak sistem improvement sağlar. Success rate calculation per strategy: SR_strategy = successful_cases / total_cases. Low performing strategies (SR < 5 70%) için deep dive analysis: failed cases'lerin common characteristics identify edilir (fault_type, network_conditions, timing). ML model retraining: her ay yeni data ile models retrain edilir (Anomaly Detection models, RCA Decision Trees). Reinforcement Learning agent policy update: successful experiences higher weight (positive reinforcement), failed Penalty (negative reinforcement) for experiences. Threshold adaptation: seasonal patterns 10 threshold adjustment (e.g., taking into account traffic pattern changes during holiday seasons). In the preferred application of the invention, the SON coordination interface (21) and other SON functions It performs conflict resolution. Example scenario: An MLB (Mobility Load Balancing) function It's trying to offload the cell (handover parameters loosen), while simultaneously performing Self-Healing. It wants to detect that the cell is outaging and tighten the parameters. Conflict detection: 15 action impact analysis, overlapping parameter set identification. Priority-based arbitration: Self-Healing priority=1 (highest), MLB priority=3, MRO priority=4, Energy Saving priority=5. Coordination protocol: SON Coordination Function (SCF) ile message exchange, coordination request approval / rejection. Cooperative optimization: bazı durumlarda joint optimization (Self-Healing + MLB combined strategy) ile better overall outcome. 20 Buluşun tercih edilen uygulamasında, visualization and reporting dashboard (22), NOC operators için real-time ve historical analytics sağlar. Real-time view: WebSocket connection ile 1-second update frequency, fault status indicators (color-coded), active healing actions progress bars. Geographic view: OpenStreetMap integration, cell locations plot edilir, status overlay (green / yellow / red circles). Timeline view: Gantt chart style visualization - fault 25 detection timestamp, RCA duration, healing action execution timeline, validation period. Analytics: healing success rate trend chart (last 30 days), MTTR histogram, top 10 fault types pie chart. Cognitive radio visualization: spectrum waterfall display (time vs frequency), occupancy heatmap. Custom dashboards: drag-and-drop widgets (KPI cards, charts, tables), saved layouts per user. 30 35

Claims

13 REQUESTS 1. Fault detection, automated problem solving, and network management in mobile telecommunications networks. Cognitive Radio (CR) based Self-Healing Network (SHN) architecture that enables improvement and its feature is; 5 • Collects data from the network via multiple interfaces, with a dedicated parser for each interface. including protocol handler, S1-MME (control plane signaling), X2-AP (inter-eNodeB communication), SNMP traps (alarm notifications), NETCONF / YANG (configuration data), Multi-interface that collects Streaming Telemetry (high-frequency performance metrics) data data collection layer (1), 10 • Continuously monitoring the 700 MHz - 6 GHz band using Software Defined Radio (SDR) hardware. scanning, Energy detection, cyclostationary feature detection and matched filtering algorithms by detecting spectrum occupancy through PSD (Power Spectral Density) analysis Localizing interference sources, 100 MHz instantaneous bandwidth with 10 ms measurement. Cognitive radio spectrum sensing module (2), which creates spectrum map in period, 15 • RACH success rate, RRC connection establishment success rate, ERAB setup success rate, handover success rate, throughput, latency, packet loss, PRB utilization, CQI Collecting KPIs like distribution in real time, statistical baseline and for each metric network performance monitoring engine (3), which calculates dynamic threshold • Alarms with critical, major, minor, and warning severity levels coming from network elements. Collecting data, applying intelligent filtering in alarm flooding situations, duplicate suppression, An alarm system that performs event correlation and alarm enrichment (adding context) operations. management system (4), • timestamp synchronization, unit that homogenizes heterogeneous data from different sources conversion (dBm, Watt, percentage), missing value imputation (linear interpolation, forward 25 fill), outlier removal (IQR method, Z-score), feature scaling (min-max normalization, Data preprocessing and normalization module (5) which performs standardization operations. • Statistical methods (Z-score, which run multiple anomaly detection algorithms in parallel) modified Z-score, Tukey's method), time-series methods (ARIMA residual analysis, STL decomposition, exponential smoothing), ML methods (Isolation Forest, One-Class SVM, 30 Generating a final anomaly score via ensemble voting using autoencoder-based detection. anomaly detection engine (6), • Direct indicators (no RACH) using specialized algorithms for cell outage detection. preamble detection, zero successful RRC setups, no active bearers), indirect indicators (sudden neighbor cell load increase, handover failure spike, UE attachment redistribution) ile 35 çalışan, sleeping cell detection için multi-criteria scoring (RACH_success < 10%, 14 RRC_success < 20%, Throughput < 5% of expected) yapan, partial outage detection için degradation patterns analiz eden cell outage detection modülü (7), • cognitive radio spectrum sensing modülü (2) ile entegre çalışarak interference classification yapan, co-channel interference (same frequency), adjacent channel interference, external interference (radar, satellite, illegal transmitters) tespit eden, time-frequency analysis (STFT - 5 Short-Time Fourier Transform) ile interference signature extraction yapan, geolocation algoritmaları (TDOA - Time Difference of Arrival, AOA - Angle of Arrival) ile interference source localization gerçekleştiren interference analysis modülü (8), • tespit edilen problemleri kategorize eden, hardware faults (baseband unit failure, RF unit failure, antenna system failure, power supply issue), software faults (process crash, memory 10 leak, configuration error), environmental (high temperature, poor backhaul), external (interference, vandalism) olarak sınıflandıran, ITU-T M.3400 fault taxonomy kullanan, severity assessment (critical (service-affecting), major (degraded performance), minor (potential future issue)) yapan fault classification and categorization sistemi (9), • multi-method RCA approach kullanan, Bayesian Networks (probabilistic graphical model ile 15 cause-effect relationships modeling, conditional probability hesaplamaları), Decision Trees (CART (Classification and Regression Trees) ile symptom-to-cause mapping), Rule-based Expert System (domain knowledge encoded as IF-THEN rules (300+ rules)), Causal Inference (Granger causality test, transfer entropy ile temporal dependencies) ile ensemble fusion yaparak %92-96 accuracy elde eden root cause analysis (RCA) engine (10), 20 • geçmiş fault events'leri structured format'ta saklayan, fault_type, root_cause, symptoms, applied_actions, resolution_time, success_status, KPI_before, KPI_after verilerini tutan, time-series database (InfluxDB) + document store (MongoDB) hybrid architecture kullanan, fault_type, cell_id, timestamp indeksleme yapan, 2 years detailed data, 5 years aggregated statistics retention policy uygulayan, case-based reasoning için knowledge base oluşturan 25 historical fault database (11), • Reinforcement Learning framework ile optimal healing strategies öğrenen, Markov Decision Process (MDP) formulation (State = (fault_type, network_conditions, resource_availability), Action = (healing_strategy), Reward = (recovery_success, recovery_time, service_impact)) kullanan, Q-Learning ve Deep Q-Network (DQN) algoritmaları ile action-value la-value 30 function approximation yapan, experience replay buffer (100K samples) ile off-policy learning gerçekleştiren, epsilon-greedy exploration strategy (ε=0.1) ile exploration-exploitation balance sağlayan cognitive decision making engine (12), • healing action templates library içeren, RF optimization actions (power adjustment (±3dB steps), antenna tilt change (±5 degree), azimuth rotation), spectrum actions (carrier switch, 35 frequency refarming, DSA activation), topology actions (neighbor list update, handover parameter tuning, cell activation / deactivation), hardware actions (soft reset, failover to backup unit, remote diagnostics), preconditions, parameters, expected for each action impact, rollback procedure defined healing action repository (13), • dynamic spectrum in spectrum holes detected by cognitive radio module (2) providing access, offering Licensed Shared Access (LSA) protocol compliance, spectrum 5 TV White Space (TVWS) database, connected to spectrum marketplace via broker interface Using FCC / Ofcom standards for querying, channel bonding and carrier aggregation. Reconfiguring, power control and sensing threshold for interference mitigation. Dynamic spectrum includes primary user protection mechanisms that perform adjustments. allocation module (14), 10 • coverage gap compensation için RF parameters optimize eden, Genetic Algorithm (GA) based multi-objective optimization (maximize coverage, minimize interference, maintain QoS) kullanan, objective function (F = w1×coverage_score + w2×(1-interference_level) + w3×throughput_improvement) ile çalışan, constraints (max_power_limit, tilt_range [-10°, +10°], regulatory compliance) içeren, simulation engine ile proposed changes impact 15 prediction yapan, gradual rollout strategy (test on 5% cells, A / B testing, full deployment) uygulayan RF parameter optimization engine (15), • arıza durumunda network topology'yi dinamik reconfigure eden, Automatic Neighbor Relation (ANR) updates (failed cell'in neighbor list'inden silinmesi, komşu cell'lere yeni neighbor eklenmesi), X2 interface establishment (alternative routing paths), handover 20 parameter adjustment (neighboring cells için hysteresis reduction, Time-to-Trigger decrease), cell range expansion (offset values tuning) yapan, graph theory algoritmaları ile optimal connectivity matrix hesaplayan network topology reconfiguration modülü (16), • healing actions'ları orchestrate eden, workflow engine (BPMN (Business Process Model and Notation) based orchestration), parallel execution (independent actions concurrent 25 çalıştırılır), sequential execution (dependent actions ordered execution), transaction management (ACID properties, rollback capability), timeout handling (action execution monitoring, stuck action detection), retry logic (exponential backoff strategy (3 retries, 2x backoff multiplier)) içeren action orchestration and execution layer (17), • network element'lere configuration changes push eden, NETCONF / YANG (modern 30 interface standardized data models ile), TR-069 (CWMP) (legacy eNodeB'ler için), CLI automation (Expect scripts, Ansible playbooks), SNMP SET (simple parameter changes) kullanan, version control (configuration backup before change, diff tracking, rollback capability) sağlayan configuration management interface (18), • healing action'larının effectiveness'ini validate eden, post-healing verification window (5-15 35 dakika) içinde KPI monitoring (fault_resolved (boolean), KPI_improvement (percentage), 16 service_impact_reduction) yapan, success criteria (alarms cleared, KPIs 95% of baseline, no recurring faults for 1 hour, user complaints decrease) kullanan, validation failure durumunda alternative healing strategy trigger eden veya manual escalation yapan healing effectiveness validator (19), • sistem performance'ını sürekli iyileştiren, outcome analysis (successful vs failed healing 5 attempts statistical analysis), model retraining (ML models monthly retrain with new data), policy update (RL agent policy parameters fine-tuning), threshold adaptation (anomaly detection thresholds seasonal adjustment), rule refinement (expert system rules update based on false positive / negative analysis) yapan, online learning için incremental learning algoritmaları kullanan feedback loop and learning modülü (20), 10 • diğer SON functions ile koordinasyon sağlayan, ANR (neighbor relation optimization durumunda cell outage bilgisi paylaşımı), MLB (load balancing actions ile healing actions conflict resolution), MRO (handover optimization ile RF parameter changes synchronization), CCO (capacity expansion ile healing-triggered configuration changes coordination), energy saving (sleeping cell'leri energy-saving initiated sleep vs fault-induced sleep differentiation) 15 fonksiyonları ile çalışan, priority arbitration (self-healing > MLB > MRO > energy saving) yapan SON coordination interface (21), • NOC operators için GUI sağlayan, real-time status (active faults, ongoing healing actions, system health indicators), network topology view (cell status color-coded (green=healthy, yellow=degraded, red=outage), geographic map overlay), timeline view (fault detection-to-20 resolution timeline visualization), analytics (healing success rate trends, MTTR statistics, most common faults, cognitive radio spectrum utilization), reports (daily / weekly / monthly automated reports, customizable dashboards) sunan, REST API ile 3rd-party integration support sağlayan visualization and reporting dashboard (22) içermesidir. 25