Services
Network Solutions and Services
Reimagining 5G and network solutions for future businesses
Highlights
- Faced with increasingly complex and numerous problems, telecom service providers are beginning to embrace AI.
- Weather related disruptions, aging infrastructure, volatility of demand and congestion, and ecological and environmental challenges are precipitating this change.
- As networks grow more complex, AI will no longer be a supportive tool to enhance operations but will become the central force driving innovation.
On this page
On this pageinpage
Introduction
With the surge in demand for faster internet, more reliable connections, and personalized services, existing networks are being pushed to their limits.
The telecom industry in the midst of a major transformation. Customers are feeling the strain, citing frequent outages, slow support responses, and outdated infrastructure as major pain points - even with top providers.
With the increasingly complex and numerous problems, telecom service providers are beginning to embrace AI, in a bid to address this growing challenge.
Challenges for Telcos
Even with all the new services and offerings that have become integral parts of our lives, telecom networks are still experiencing the same problems time and again. There are four particularly prominent pain points:
- Weather-related disruptions
Extreme weather conditions such as flooding, storms etc., potentially put the reliability of infrastructure and services at risk, particularly in remote or vulnerable regions. How can operators predict and be prepared to manage environmental risks to ensure service levels remain constant? - Aged infrastructure and Repairs
From towers and antennas to batteries and fiber links, physical assets degrade over time. Scheduled or reactive maintenance is still the norm for most networks, and it results in inefficiencies, service impairments and spiraling costs. The foundational challenge: How do we move from reactive maintenance to preventive-condition based intervention? - Volatility of demand and congestion
The usage of telecom networks can vary widely over the course of time according to events, time of day or weather. Without precise predictions, networks may be over- or under-utilized. How can providers predict fluctuations in demand and modulate capacity on the fly? - Energy consumption and environmental stress
Large networks require lots of power, and energy prices are increasing. At the same time stakeholders want to minimize carbon emissions and pursue sustainable practices. Can energy efficiency be achieved without compromising performance or dependability?
AI-Powered Lifecycle
To meet challenges, telecom companies need to shift toward a forward-looking, more intelligent manner of managing their network. AI integration throughout the network lifecycle provides a holistic solution.
The Plan → Operate → Optimize Model
- Plan: Anticipate infrastructure requirements, risks, and demand, and forecast using AI pre-deployment.
- Operate: Allow AI to automate performance and action monitoring as well as real-time decision-making.
- Optimize: Refine actions and investments while continuously enhancing derived value and insights.
Use Cases
- Smarter, Safer Maintenance Using AI in combination with drone inspections, telecom companies can remotely assess towers, antennas, and other assets for:
- Structural damage
- Wear and tear
- Compliance or safety issues
- Prediction and control of network requirements AI is tremendously effective at detecting patterns of usage and predicting changes by:
- Historical network traffic
- Real-time user behavior
- Outside forces such as weather or public events
- Automating AI/ML for survey validation Conventional field surveys require human inspection of images to verify the connection points of network elements are properly in place. This method is laborious and error prone. AI/ML can provide:
- Automated image classification (eg., classifying images as good quality or bad quality based upon the quality of installation, picture quality etc.)
- Identification of noncompliance with guidelines or absence of components
- Automated pushing back of the poor quality images to the survey team or contractor for rework and rectification.
- Smart, resilient and efficient operations Weather prediction and forecasting capabilities of metrological departments has improved greatly with time. The weather forecast is made for several time ranges: a short period of 72 hours; a medium period of 3-7 days; and a long period of 8-14 days, as well as monthly mean values and seasonal forecasts. With data from multiple sources (weather feeds, site health logs, fuel levels, battery status), AI-based analytics can aggregate these forecasts to build a predictive operational layer. This enables
- Early alarm systems: These are used for real-time alerts relating to the weather forecast as well as the condition of the machinery.
- Site ranking: This creates a hierarchy of the list of sites that are high risk or mission critical, and require proactive attention. Hub facilities can be prespecified.
- Fuel and battery planning: AI-powered predictions of backups’ charge and readiness
- More intelligent management of energy and infrastructure One of the most successful uses of AI is in energy scheduling. It reduces energy waste without impairing performance, by making network operations more sensitive to real-time conditions, offering several optimizations such as:
- Adaptive cooling solutions: Reduces cooling power consumption by tailoring cooling levels to equipment loads and outdoor temperatures.
- Load-aware power control: Powers down rarely used devices (e.g., microwave ODUs) at low traffic times, to reduce power consumption in an intelligent manner.
- Battery life forecasting: Continuously monitors the health of batteries to predict end-of-life, preventing failures before they occur for up-time and safety.
Key Benefits
AI is no longer a future investment to be assessed; it’s now a pragmatic necessity in the telecom world. The concrete utilities of this system are:
- Improved network reliability By forecasting and mitigating failures, AI provides smoother operations and reduced network outage even in the most adverse environments.
- Higher customer satisfaction Proactive network management and faster issue resolution result in fewer complaints, better user experience, and stronger customer loyalty.
- Lower operating costs Smart automation becomes a source of saving, with little need for manual interventions, emergency maintenance or wasteful energy consumption.
- Sustainability and ESG compliance By using energy efficiently, telecom operators can further enhance their environmental credentials and position themselves with regulators, investors and customers. A simple instance of this is the reduced e-waste of batteries by extending the shelf-life through AI-based prevention of overcharging and discharging.
Long-Term View
Telecom is a rapidly changing landscape with 5G, IoT and edge computing. As networks grow more complex, AI will no longer be a supportive tool to enhance operations but will become central force driving innovation.
The next stage of developments could encompass:
- Self-healing networks: AI systems that self-diagnose and fix problems without human intervention
- Edge-based intelligence: Distributed AI at the point-of-service that takes real-time decisions, which reduces latency, as data need not travel long distances to the central server while reducing load on the central server
- Automated design and deployment: AI-assisted planning tools that generate an optimal network blueprint with minimal involvement.
- Cyber threat detection: Real-time recognition and containment of AI-based threats, improving resilience and confidence.
The progression of AI not only offers improvements to networks themselves, but unprecedented forms of operation that are quicker, more secure, and agile.
Conclusion
The application of AI today is disrupting the telecommunications industry in ways previously thought impossible. It is enabling operators to plan, monitor, provide customer service, and optimize energy. Also, AI prepares operators for a more advanced and connected world. The greatest challenges and expectations telecom companies face can be addressed by AI and the need for it to be deployed in demanding business environments is imperative
