Role and Applications of AI in Telecom

Role of AI in Telecom

The complexions of communications networks appear to extend inexorably with the deployment of the latest services, such as -Software-defined wide-area networking (SDWAN) and new technology paradigms, such as network function virtualization (NFV). This Insight discusses the advantages of enabling AI in Telecom.

To meet ever-rising client expectations, communications service providers (CSPs) got to increase the intelligence of their network operations, planning, and improvement.

To move to period time closed-loop automation, CSPs would like systems that square measure capable of learning autonomously. That is solely doable with AI/ML.

Researchers in communication networks square measure are trapping into AI/ML techniques -

Best trends in Communication Networks and Services

Characterized requirements
Multimedia services
Precision management
Predictable future
Intellectualization
More attention to security and safety
Trends of mobile network
Big data for development and ICT monitoring

Potential AI Use Cases in Telecom

Artificial Intelligence for Telecommunications Applications identifies seven critical telecom AI use cases -

Network operations monitoring and management
Predictive maintenance
Fraud mitigation
Cybersecurity
Customer service and marketing virtual digital assistants
Intelligent CRM systems
CEM
Base station profitability
Preventive maintenance
Battery Capex optimization
Trouble price ticket prioritization

Network Operations Monitoring & Management

Increased quality in networking and networked applications is driving the necessity for redoubled network automation and lightness. Applications of AI/ML include -

Anomaly detection for operations, administration, maintenance and provisioning (OAM&P)
Performance watching and optimization
Alert/alarm suppression
Bother price ticket action recommendations
Automated resolution of bother tickets (self-healing)
Prediction of network faults
Network capability designing (congestion prediction)

AI/ML might use clustering to search out correlations between alarms that had antecedently been undiscovered or use classification to coach the system to rank alarms.

The following potential use cases with AI and ML algorithms in a very mobile context -

AI at the RAN -

AI at the core — Autonomous VNF scale in\out, up\down.