AI Applications in the Telecommunications Industry: Challenging Telecoms With Machine Learning Solutions

Current Applications of AI in Telecoms

The telecom sector has experienced several automation phases. While previous connections were still made manually by switching cables, hardware later automated this work. These features no longer need specific hardware but are virtually defined through software.

TL;DR

• AI has been applied in the telecom sector for over a decade, with applications focused on optimizing radio signals, power management, and transmission estimation.
• AI in telecom can provide faster and better decision-making, utilize expert knowledge more efficiently, and handle repetitive tasks effectively.
• AI applications can operate autonomously through autonomous learning and action, with scenarios ranging from closed-loop systems to human-in-the-loop interactions.
• Challenges in implementing AI in telecom include technical integration, lack of technical expertise, and dealing with unstructured data. Contact nexocode data engineers for overcome these problems and implement AI in the telecom industry.

Artificial intelligence (AI) has been clearly advancing since around 2010 and has applied to the telecommunication market for more than a decade. Besides the fact that telecom infrastructures are vital to society, more and more applications depend on well-functioning, reliable, and always available telecom services.

Most of the current AI applications focus on improving specific parameters. These are strictly defined applications such as:

1. Optimizing the Parameters of a Radio Signal

Currently, Machine Learning (ML) is used to optimize data flow to and from a Base Station (BTS) in a mobile network. The distance to users, connected users, and certain environmental factors determine the radio parameters. They, in turn, determine the maximum amount of data that can be transmitted per quantity of spectrum per unit of time. Interference also plays a role: radio resources can be coordinated between micro and macro cells. To maximize efficiency, algorithms are being used to dynamically determine what part of the spectrum should be used for which user and with which parameters. The parameters of these algorithms can be “tuned” with AI.

2. Power Management

Machine learning techniques are used to achieve power savings in live mobile networks. Based on meteorological data, the number of users, and their position, antennas actively adjust their radiation pattern, direction, and strength to demand. This results in energy saving, for example, during the night when data demand is relatively low and in more efficient use of the base stations because a larger surface area can be operated at set-up points where the need for capacity is not uniform.

3. Quality of Transmission Estimation

With optical connections, the signal can be disturbed or interrupted and can cause permanent equipment failure. Machine learning is applied to estimate how well the transmission will work over a connection in advance. It calculates the best path based on things like the cable length, other signals within the cable, and the equipment’s age. The traffic is routed based on this assessment. It is also possible that such algorithms are used in wireless networks, for example, determining how much error correction or redundancy (e.g., retransmission) is used. Expert systems and Machine learning algorithms are the two AI techniques that have been widely used in the telecommunication sector, while ML and distributed Artificial Intelligence are the two AI techniques that are most promising for the future.

AI and Machine Learning

AI and ML are disrupting and transforming businesses. Telecommunications companies can leverage these technologies to improve customer retention, enable self-service, improve equipment maintenance, and reduce operational costs at the same time.

The telecommunication industry is riding the waves of the tech revolution and digital transformation to offer a wider variety of services to its consumers. However, consumers in today’s digital world will not be happy with run-of-the-mill products and services – they also demand a better quality of services and more responsive service providers. Data-driven insights relied on solutions powered by AI and ML can help telecom providers fulfill these expectations.

Autonomous Learning and Action

AI applications often take over tasks generally performed by people or support people. This means that these systems have a degree of autonomy. There are two forms of independence: autonomous learning and autonomous action.

Autonomous Learning

Today’s AI model has been developed based on large amounts of (historical) data. An algorithm ’learns’ the desired results from this data with a particular input. There are several ways to shape this learning or ’training':

• Offline Learning - model is trained once or every so often based on a static dataset. Both the model and the data used can be tested and validated before the model goes into production;
• Online Learning - model is trained as with offline learning and then retrained periodically based on new data;
• Continuous Learning - model is continuously updated using incoming data. Unlike with online learning, there are no longer different ‘versions’ of the model: each inference request potentially directly affects the following AI decision.

Autonomous Action

AI applications in the telecommunication industry can be applied in various ways. The most used ones include:

Closed-loop scenario - in this scenario, the AI system performs actions directly. The only action people can achieve is switching off the system, for example - speech recognition software.

Open-loop scenario - AI system’s role is to provide support. AI presents a person with an outcome, and based on this outcome, the person can act. In this scenario, people can deviate from the advice and/or check this advice based on other information. Example - expert systems that help doctors form a diagnosis.

In a rule-constrained closed-loop scenario, an AI system can perform direct actions but is restricted by specific “hard” rules. Breaking the rules results in the system being disabled or failing to act. Autonomous vehicles are an example of this strategy. They are often equipped with various ‘fail safe’ rules that ensure a car makes an emergency stop in unsafe situations;

In human in the loop scenario - AI can perform actions directly, but humans can stop or adjust these actions if necessary. Example: autonomous vehicles where people have to keep their hands on the steering wheel;

Multiple AI systems in the loop - one or more other AI systems monitor an AI system that performs actions. The controlling AI model can view the original inputs and the AI’s decision and assess whether this decision is correct.

Added-Value of AI in the Telecommunications Industry

The use of Artificial Intelligence in telecommunications can help solve several complex and sometimes long problematic issues and at the same time yield tons of added value to both consumers and operators alike. The latter has always been collecting substantial amounts of telemetry and service usage statistics, but most of it never got used in a meaningful way due to the lack of the right software.

With AI, this massive array of previously unused data can be turned into fertile soil for growing new services, improving the quality of existing ones, taking customer experience to a new level, and optimizing business operations. According to relatively recent studies, AI in telecom companies will be generating nearly 11 billion dollars by 2025 — a staggering amount that is likely to keep growing as the scope of AI applications expands.

AI generally provides several advantages and opportunities:

AI Can Make Faster and Sometimes Better Decisions Than People

Whereas a person sometimes needs a few seconds to minutes to make a decision, a Machine learning model can often process thousands of data items in a fraction of a second.

For example, fraud detection AI can monitor thousands of credit card transactions in real-time and block potentially fraudulent transactions.

With AI, Rare Expert Knowledge Can Be Used More Efficiently

People often undergo training for several years before entering the labor market. A person will not yet be at the top of their game, even a few years later. With ML, an expert’s knowledge can be distilled into a model, and thus, this knowledge can be applied more widely.

For example, Google has developed an AI system that can identify tumors from CT scans just as well as an experienced radiation oncologist. Such application of AI can provide insufficient expert knowledge in specific scenarios and thereby free up the human experts to do other work more efficiently.

Artificial Intelligence Is Good at Repetitive Tasks

People often perceive repetitive tasks as boring and unsatisfying. However, if these tasks are well framed, the AI framework is ideally suited to take them over. AI does not have to sleep, rest, or take breaks because it will not get bored or tired. Repetitive tasks also lend themselves well to AI because (if humans currently perform the job) there is probably sufficient Big Data available to train the AI.

Challenges of Using AI and ML for Telecom Companies

Even though the global AI in the telecommunication market is proliferating, implementing it can still be tricky for many businesses. Besides being unable to recognize the need for AI or identify appropriate business use cases, the most common challenges of implementing AI in telecoms are:

1. Technical integration - although old legacy systems are one of the most common reasons why many AI integration fails, several things could be prepared before starting any AI project:

Set up a unified database where all the data required by the system will be stored;Use data lakes, as well as edge or cloud computing to eliminate any issues that can occur when storing large amounts of data;Do not hesitate to give your data entry and storage process a complete overhaul if you notice that collected data is disparate or unstructured;Ensure that you have the required hardware and software to handle the new system.

1. Lack of technical expertise - AI is a relatively new technology. With limited local talent, building an in-house team can take a significant amount of time and yield little result. A better option is looking for a technical partner to implement AI in telecommunications. However, finding a vendor that has both enough competence and experience to build an AI system successfully can be a challenge in itself. Moreover, implementing AI can be quite pricey, so starting your project with the right partner is crucial.
2. Unstructured data - Implementing an AI system without access to relevant data is a fruitless endeavor. Many organizations struggle with data collection because of several common issues:

Fragmented data - information is collected and stored by different systems without a single unified database from where it can be accessed;Unstructured data - an enormous mass of uncategorized data without any context or explanation of what it is related to is not very useful to any AI algorithm;Incomplete data - using data with missing components can lead to inconsistent or faulty learning by the AI system.

Since AI algorithms require clean, well-structured data, around 80% of the time of any ML project is dedicated to ETL (extracting, transforming, loading) and data clean-up. Therefore, it is essential to put an appropriate Big Data engineering ecosystem to collect, integrate, store, and process data from numerous siloed data sources.

AI for Telco - Most Common Use Cases

AI has helped the telecom sector redefine customer satisfaction, bringing new opportunities and complicating business models. Here are some of the ways that AI has contributed to the telecom industry already:

1. Predictive Maintenance

AI and ML have enabled the telecommunication industry to extract valuable business insights. Since telecoms have a massive amount of Big Data, AI can use it to make efficient and effective decisions through customer segmentation, predicting the lifetime value of a consumer, and making purchase recommendations.

Predictive analytics, by finding patterns in the historical data, can accurately anticipate and warn about possible hardware failures. Furthermore, created algorithms and data science models can identify the reason behind each failure, making it possible to fight the problem at its root. Preventive maintenance allows telecom companies to be very proactive at maintaining their equipment, fixing issues before they occur, and minimizing support requests. This proactive approach brings an overall boost to the customer experience.

2. Improved Network Optimization

Artificial Intelligence applications in the telecommunications industry help Communication Service Providers (CSPs) build self-optimizing networks (SONs) to improve customer satisfaction, lower customer service costs, prevent outages and keep certain network quality. Such networks are automatically monitored by AI algorithms that detect and accurately predict network anomalies. Furthermore, they can proactively optimize and reconfigure the network to ensure higher quality services. You can read more on data management and network optimization in our past article - Machine Learning to the Rescue: Facilitate Data Management Needs of Telecommunications Industry.

3. Network Anomalies

AI-based anomaly detection effectively augments and automates early detection, predictions, and decision-making in operations and business processes where humans can’t deal with data volume or velocity. Improving the overall time to detect invariably leads to quicker resolution of incidents. It thus results in reduced costs associated with outages and aids in the prevention of lost revenue and brand impact.

Furthermore, AI/ML-based anomaly detection solutions can analyze multiple dimensions of data sources, looking at the cell, subscriber, and device-level KPIs, monitoring for faults in network equipment, and correlating alerts across domains for noise reduction and root cause analysis. This gives engineers a transparent view of both network & service performance and subscriber experience — at any given time and enables them to leverage data and predict network anomalies in time. You can read more on network anomaly detection and network automation in our recent article.

4. Robotic Process Automation (RPA)

Robotic process automation is a form of digital transformation that relies on implementing AI. The Telecom sector can use RPA and natural language processing (NLP) to automate data entry, order processing, billing, and other back-office processes that require lots of time and manual work. This frees up employees’ time, lets them focus on more critical tasks, and reduces the number of errors that manual labor is prone to. Thanks to robotic process automation solutions, the office runs smoother, employees are more productive, and customers enjoy error-free service.

You can read more about specific use cases of applying RPA and NLP solutions in the telecom sector in our recent article.

5. Fraud Prevention

One of the things that AI in telecom can do exceptionally well is fraud detection and prevention. Anti-fraud analytical systems can detect suspicious behavioral patterns and immediately block complementary services or user accounts by processing call and data transfer logs in real-time. The addition of ML enables such systems to be even more accurate and fast.

Real-Life Case Studies

1. Nokia

Nokia released AVA Telco AI Ecosystem, which provides Artificial Intelligence solutions that can be delivered through the cloud with Microsoft Azure or other public options. The solutions enable Communications Service Providers to automate network management, capacity planning, and service assurance, cutting operational costs, increasing agility, and boosting subscriber experience.

2. Vodafone

The British telecom giant Vodafone Group launched an assistant app called TOBi for customer care management, an intelligent virtual assistant capable of supporting users in dealing with issues, subscription management, and purchasing new equipment and services.

3. Deutsche Telecom

Deutsche Telekom has been making considerable investments in AI at various levels. From an AI-powered chatbot called Tinka, capable of providing over 1500 answers to customers’ questions through interactive voice response, to intelligent business planning tools, this CSP is actively embedding AI and data science elements into its infrastructure and service portfolio.

The Future of AI for Telecom Companies

Cloud, 5G, and AI, cognitive computing technologies engagement with consumer insights have made it possible to answer a wide variety of questions, all in the customer’s language. However, in the future, as businesses get comfortable turning customer insights over to machines, human customer-service agents might become a thing of the past, allowing customers to engage with virtual assistants and boots.

AI is also predicted to leap from dealing with insights to predicting consumer behavior and impacting business decisions. This should lower costs and enhance customer experience, increasing their lifetime value.

The range of potential Artificial Intelligence applications in telecommunications and AI telecom use cases is surprisingly broad. There is no doubt that key market players will see increasingly intelligent automation systems being rolled out to streamline day-to-day operations and deliver more value to customers.

The success of telecommunication companies embarking on a digital transformation journey will rely on their ability to put AI to good use as early as possible and develop corresponding software. With cognitive technologies-powered data collected, reliable insights, and manual expertise, there may be no limit to what AI can help us achieve.