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AI in Telecom Industry Benefits and Use Cases | Complete Guide

Dr. Jagreet Kaur Gill | 04 Apr 2023

AI in Telecom Industry Benefits and Use Cases

Overview of AI in Telecom Industry

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 functions 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 -
  • To optimize specification
  • To control and management
  • To change additional autonomous operations
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What are the 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

What are the advantages of AI in the Telecom sectors?

The advantages of AI in the Telecom industry are below:

  • Abilities of learning
  • Abilities of understanding and reasoning
  • Ability to collaborate

Use Cases in Telecom Industry

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
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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 -
  • Intelligent initial access and handover.
  • Dynamic scheduling.
  • Resource optimization.
AI at the core - Autonomous VNF scale in\out, up\down.
  • Provision of elasticity.
  • Intelligent network slicing management
  • Service prioritization and resource sharing.
  • Intelligent fault localization and prediction.
AI at the front haul - Traffic pattern estimation and prediction; Versatile, practical split Different general AI applications (RAN, core or end-to-end network) -
  • Energy potency per dynamic traffic pattern, etc
  • End-to-end service orchestration and assurance (e.g., custom­made SLA)
  • End-to-end service optimization, prioritization

Overview of Predictive(Prognostic) Maintenance

Heavy reading sees prognostic maintenance as a subcategory at intervals of network operations instead of a selected field. We tend to find that prognostic maintenance was the highest use case for ML in telecom before security, network management, and fraud/revenue assurance.

AI-based Fraud Mitigation Solutions in Telecom

According to the Communications Fraud Control Association, fraud prices the world telecom business $38 billion annually, of that, roaming fraud accounts for $10.8 billion. We tend to describe how - And use AI to spot revenue leaks and surface discrepancies between expected results and the way events are beaked. Skymind is victimization AI to combat subscriber identity module (SIM) box fraud at Orange. Wise Athena has used AI to spot CSP fraud.

AI in Cybersecurity

The techniques of adversaries are evolving chop­chop (rapidly), and therefore the variety of advanced and unknown threats targeting CSP networks continues to extend. AI/ML algorithms can be trained to adapt to -
  • The dynamic threat landscape,
  • Creating freelance choices concerning whether or not an associate anomaly is malicious or providing context to help human consultants.
  • Developing solutions that may facilitate CSPs to manage IoT devices and services a lot of firmly.
  • Creating the use of automatic identification of these devices.
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Customer Service & Marketing Virtual Digital - Assistants

Applications of AI/ML in the telecom sector up to now have been the utilization of chatbots to enhance or replace human call center agents. Instead, plans to extend the no. of agents handling client inquiries directly via electronic messaging apps like Whatsapp. AI usage in client service/support includes -
  • Information portals and AI assistants for human agents
  • Contact center optimization and compliance
  • Client voice and text sentiment analysis to reinforce the performance of its electronic messaging and chat agents.

Intelligent CRM Systems

  • AI is often applied to CRM in areas like customized promotions, cross­sell/up­sell chance identification, and churn prediction and mitigation.
  • Found the strategy a lot of correct than previous approaches supported by supervised ML classifiers.
  • Victimization AI to supply promoting insights.

CEM (Customer Experience Management)

CEM because the method of managing “all client touch points” confirms a positive relationship with the whole. As digital touchpoints still grow, analytics and AI are essential tools for CSPs to -
  • Perceive the health of the network
  • The client journey (customer care, billing, etc.)
  • Time-period service quality

Base station profitability

  • Total price relies rental on (data coming back from property team)
  • Maintenance (data from operations)
  • Field technician prices
  • Necessary level three support within the NOC
  • Traffic and associated revenue spring from the business team.
  • The gain of every base station is calculated, and an assessment is created of the least profitable base stations to grasp what can be modified.

Enabling Preventive Maintenance

  • Traditional applications to switch parts sporadically supported the vendor’s suggested schedule.
  • By collecting its history, will build a lot of correct predictions of faults supported by the specifics of its cell sites.
  • This has a junction rectifier to the reduction of many website visits in one operating business.

Battery Capex optimization

  • Most of the batteries deployed within the field are never truly used, though their thieving and replacement represent a significant price.
  • Analyzed which internet sites have traditionally suffered from low electrical offer responsibleness.
  • Focusing on the replacement of taken batteries wherever the likelihood of them being required is highest.

Trouble price ticket prioritization

  • Prioritized supported the foreseeable range of customers wedged.
  • Length of your time, the price ticket has been open.
  • Predicting however long the price ticket is probably going to require resolve.
  • Prioritizing tickets supported this life instead of the time march on to date.

Uses Cases of Artificial Intelligence in Communications

AI in SDN (Software-defined networking), AI in NFV (Network Functions Virtualization), AI and network observation, security, and reliability -
  • Isolation
  • Detection
  • Failsafe
  • Redundancy
AI and QoS (Quality of Service), ITU Work/Focus cluster on ML 5G, Challenges of Applying AI/ML to Networking -
  • Data that’s dirty, unavailable, or troublesome to access
  • Provability
  • Algorithm bias
  • Lack of Data Science Talent
  • Lack of clear questions to answer
  • Limitations of tools
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What are the effective applications of AI?

The effective applications of AI are described below

  • Unemployment – what happens when the tip of jobs
  • Inequality – however, will we distribute the wealth created by machines
  • Humanity – however, do machines affect our behavior and interaction
  • Artificial stupidity – however, will we tend to guard against mistakes
  • Racist robot – however, will we manage to eliminate AI bias
  • Security – however, will we keep AI safe from adversaries
  • Evil genies – however, will we shield against accidental consequences
  • Singularity – however, will we keep up to the speed of a fancy intelligent system
  • Robot rights – however, will we outline the humane treatment of AI

AI-based Strategy

The telecom business has been able to extract insights from massive data sets, making it easier to address issues, operate daily operations more efficiently, provide greater customer service and happiness, and much more.