Introduction
The rollout of 5G networks, with their ultra-fast speeds and high data capacity, poses critical operation challenges from complexity, vast data, and immense service requirements. AIOps (Artificial Intelligence for IT Operations) is emerging as a key technology to tackle these challenges by enabling real-time analytics, minimizing downtime, and automating network issue resolution.
By integrating Machine Learning (ML), big data, and automation, AIOps facilitates proactive and predictive network management for maximum connectivity and top-notch quality services.
This article explores the role of AIOps in 5G networks, its key capabilities, real-world applications, and future potential.
Understanding AIOps in Telecom
AIOps applies AI and ML to automate IT operations such as event correlation, anomaly detection, and root cause analysis. AIOps, in telecom, is the backbone of managing 5G network complexity efficiently through:
1. Real-Time Network Data Analysis
AIOps leverages predictive analytics, big data, and machine learning models (e.g., LSTMs, Transformers) to examine real-time telemetry. It identifies differences and trends with unsupervised learning methods (LSTMs, Isolation Forests) and failure predictions using time-series models (ARIMA, RNNs) to prevent performance degradation. With technologies like digital twin and closed-loop automation, AIOps supports proactive remediation of issues, reducing downtime and improving service reliability.
2. Automated Responses & Self-Healing Mechanisms
Automation is central to 5G network optimization, with self-healing methods, Intent-Based Networking (IBN), and Reinforcement Learning (DQN, PPO) that control network resources in real time. AI-driven network splitting, auto-fault remediation, and dynamic spectrum allocation ensure optimal load balancing and resilience. AI-driven NFV balance (ONAP, ETSI MANO) also ensures dynamic scaling of virtual network functions for greater efficiency at lower operational costs.
Also, read inMorphis: AI Automation in ServiceNow Event Management
3. AI-Driven Decision-Making for Network Optimization
AI-powered analytics allow real-time traffic engineering, SLA predictive management, and cybersecurity threat detection. Techniques such as Multi-Armed Bandits, Graph Neural Networks (GNNs), and Explainable AI (XAI) help transform the complexity of network data into actionable intelligence, enhancing both 5G network performance and laying the foundation for AI-native 6G networks where autonomous decision-making and self-optimizing infrastructure will drive future connectivity.
Example
AIOps tools employ unsupervised ML methods such as clustering (e.g., K-Means) to identify outliers in network traffic. For instance, a telecommunication network operator can implement an AIOps tool that identifies an unusual increase in latency, relates it to congestion in nearby cell towers, and sends an automatic resource realignment to ensure optimal service.
Also, read New Features of ServiceNow AIOps Xanadu
Challenges of 5G Networks Without AIOps
Despite several advantages, 5G networks also poses some challenges that cannot be dealt with using traditional network administration techniques:
1. Increased Network Complexity:
5G networks support ultra-speed data transmission, edge computing, and massive IoT connectivity compared to previous generations. Handling such a heterogeneous workload manually is inefficient and error prone.
2. Limited Fault Detection and Response Time:
Legacy monitoring solutions are passive; therefore, issues are resolved only after affecting users. This causes higher service outages and dissatisfied customers.
3. Scalability Issues:
With billions of devices and real-time traffic, telecom operators need sophisticated analytics to dynamically predict network congestion and allocate resources.
4. Security Threats and Cyberattacks:
5G networks are vulnerable to sophisticated cyberattacks, such as Distributed Denial of Service (DDoS) and artificial intelligence-based malware. Without AIOps-based security, it is impractical to offer real-time security against such attacks.
Key Features of AIOps Enhancing 5G Network Performance
AIOps offers communications service providers an intelligent, automated, and predictive network management method. The following five capabilities are vital to optimizing the performance of 5G:
1. Predictive Analytics for Proactive Maintenance
AIOps predict faults before they happen through historical and real-time analysis. It allows operators to do maintenance at the best possible moment, eliminating unplanned downtime.
Example
AI-driven RCA tools use Recurrent Neural Networks (RNNs) and Transformers to analyse network logs, identifying patterns indicative of imminent equipment failures.
2. Automated Root Cause Analysis (RCA)
AIOps detects and isolates the root cause of network problems, decreasing mean time to resolution (MTTR) and improving service availability.
Example
An AI-integrated RCA system detects and identifies patterned packet loss in a specific geographic region, relating it to external factors such as weather or infrastructure and activating autonomous support procedures.
3. Smart Traffic Management and Load Balancing
AIOps dynamically redistributes network traffic across nodes to prevent congestion and ensure optimal service performance.
Example
Using reinforcement learning algorithms, an AIOps platform can optimize more bandwidth-intensive traffic (e.g., video streams) over lower-priority traffic to provide an uninterrupted user experience.
4. Anomaly Detection and Security Enhancement
AI-powered security analytics identify unusual network behavior, providing real-time threat prevention.
Example
AIOps security solutions leverage unsupervised deep learning methods, e.g., autoencoders, to identify zero-day attacks from network traffic anomalies.
5. Dynamic Resource Optimization and Cost Optimization
AIOps improve network slicing, allowing telecom operators to dynamically assign resources proportional to the service's demand, lowering operational expenditures.
Example
AIOps adjusts Multi-Access Edge Computing (MEC) resources according to consumption patterns for industrial IoT applications' ultra-low latency requirements by business customers.
Real-World Case Studies: AIOps in Action
Case Study 1: AI-Driven Network Monitoring by One of the Indian Telecom Players
One of the Indian Telecom Players1 implemented AIOps-powered fault detection, reducing network incidents by 35% and response time by 40%, improving customer satisfaction.
Case Study 2: Self-Driving Network Optimization by a Global Telecom
A multinational telecom provider2 used AIOps to automate self-driven network operations with RCA by AI and predictive maintenance. Downtime decreased by 30%, and operational efficiency grew exponentially.
Case Study 3: Smart Traffic Routing by One of the Largest Wireless Carriers
They employed AIOps to optimize traffic routing in real-time intelligently, increasing bandwidth usage by twofold to 25%3 and relieving rush-hour traffic jams.
Challenges in Implementing AIOps
Despite its advantages, AIOps adoption in telecom faces challenges:
- Data Silos and Integration Issues – Telecom networks consist of diverse infrastructures from multiple vendors, complicating AI model training.
- Computational Costs – Real-time AI processing demands significant computational resources, requiring edge computing solutions.
- False Positives in Anomaly Detection – AI models sometimes misclassify normal fluctuations as failures, triggering unnecessary alerts.
Future Trends: Preparing for 6G with AIOps
With the upcoming generation progressing in light speed toward 6G networks, AIOps is going to be instrumental in actualizing the efficiency of next-gen networks. Trends that can be predicted for the future are as follows:
1. AI-Native 6G Networks
Future 6G networks will feature AI-driven self-optimizing architectures, enabling autonomous service orchestration and performance tuning.
2. AI-Powered Security & Threat Intelligence
AIOps-driven cybersecurity solutions will leverage federated learning and blockchain for real-time threat mitigation and secure data processing.
Also, read Key Benefits of ServiceNow GenAI for Compliance
3. Ultra-Low Latency and Edge AI
AIOps will facilitate real-time edge processing with low latency for autonomous transportation, AR, and IoT applications 4.
4. Human-Centric AI Networks
6G will be a human-centric network experience, using AI-driven user behavior analytics to customize service delivery.
5. AI-Driven Regulatory Compliance
With increasing regulatory oversight, AIOps will incorporate Explainable AI (XAI) to ensure data privacy laws (e.g., GDPR, CCPA) compliance.
Conclusion
AIOps is revolutionizing the telecommunication industry by intelligently automating, anticipating issues before being reported, and enhancing 5G network security. Network downtime decreases, and efficiency increases with AI, ML, and big data analysis through AIOps.
To explore how our AIOps solutions can optimize your telecom infrastructure, connect with us at inMorphis and unlock the full potential of AI-driven network automation.
