In the current world, telecommunication networks are the backbone of the global economy, powering everything from business communications to social media.** The COVID-19 pandemic showed us the necessity and power of telecom networks enabling millions of people to stay connected within the safety of their homes**. As the demand for connectivity, speed, and data is evergrowing, so does the need for smarter and more efficient networks. With recent developments in generative AI and chatGPT and the race for making the best AI that doesn't kill humans, I ventured into a study project to figure out where and how AI can help the telecom Industry.

Telecom Industry has been slow in innovation but faster in mergers and acquisitions. It has missed many opportunities where it could have been the driving force behind Fintech, smarter next-generation experiences, or a vision of one connected world. But lack of deep knowledge in the Telecom space and slow pace of developments and meeting complex interoperability standards created a deadlock. This is where I feel Artificial Intelligence (AI) comes in to help this industry. AI has the potential to revolutionize the way telecom networks are designed, deployed, and managed. It can help improve network optimization, Smarter configurations, fault management, and predictive maintenance.

Telecom operators and vendors can equally leverage AI to create smarter networks that are more reliable, cost-effective, and capable of delivering a better user experience as well as better monetization.

In this article, I will list down the obvious various areas in which AI can be and is being used in telecom networks, including network optimization, fault management, and predictive maintenance. But specifically, I will dig deeper into the 7th one as I feel this will be a game-changer for Telcos. I will also delve into the AI algorithms ( with limited experience in AI space) and technologies that can make some of these applications possible.

The most obvious areas of use of AI in Telecom Networks are as below.

1. Network Optimization: My first patent was in the field of network optimization. This is when mobile phones were getting smarter at a faster pace than the network call flows. We optimized a call flow that optimized the network TPS by 20%. If humans can then definitely AI can help in network optimization by identifying network bottlenecks, predicting traffic patterns, and optimizing the allocation of network resources. This will improve network performance and reduces costs.

2. Predictive Maintenance: The days when an engineer gets an SMS of an Alarm and acts on it are over. AI-based predictive maintenance systems can detect potential network faults before they occur, preventing network downtime and reducing maintenance costs.

3. Fraud Detection: AI algorithms can detect fraudulent activities in telecom networks, such as SIM box fraud, call masking, and revenue leakage and most importantly spam and fraudulent calls. In developing countries with inadequate privacy regulations, spam and fraudulent calls will increase with digitization of customer base. A AI-based solution can filter such spammers.

4. Customer Service: The Chatbot revolution has already occurred in many industries like e-commerce. AI-powered chatbots and virtual assistants can handle customer queries, and complaints, and provide personalized recommendations, improving the overall customer experience. NVIDIA’s leap into conversational AI for Telcos is a step toward this.

5. Resource Management: AI can optimize the allocation of network resources, such as bandwidth, energy, computing power, speed, latency, slicing, improving network efficiency and reducing costs. As 5G becomes more prominent AI based Dynamic Network slicing will be one of the things to look out for.

6. Network Security: As networks become they need to get more secure too. AI-based security systems can detect and prevent network breaches, cyber-attacks, and data theft, ensuring network integrity and protecting sensitive customer data.

7. AI-driven Smart Catalogue: This is one area where AI can be a boon to this Industry. In the last 10 years, Industry has seen mergers between vendors and Operators. This has led to complex architectures with a multitude of vendors. Creating a Business use case is a convoluted task that touches many layers of architecture, and multi-vendor nodes. The expertise and skill required to do all this is reducing day by day. AI can be a super alternative to doing smarter configurations in the Network nodes and move this industry a decade in the future.

What is **AI-driven Smart Catalogue? **Visualize a prototype in the below video

With recent developments in conversation chatbots and their ability to use plugins, interpret data, and use it to make a decision is an excellent way the same can be done for creating configurations in the network which are smarter. Can this be used in Telecom Networks?

Developing an AI Telco assistant or conversational AI interface that uses natural language processing (NLP) to create and configure a plan with has an impact on different Nodes in a Network. For e.g in a 5G architecture NSSF, SMF, NEF, NWDAF, CHF, PCF.

The AI-assisted chatbot would interact with the user through a natural language interface where the user could describe their requirements and preferences for a plan. On the other side, the AI application will use plugins with standardized APIs ( Open, ™ Forum, REST, CAMARA, NEF ) to communicate with other network layers and elements to fetch, ingest and interpret data. The AI Telco assistant would then use AI algorithms to analyze the user’s input and provide a customized plan that meets their needs. Please note the user here is a telecom Operator.

For example, a user might request a plan that includes unlimited voice calls, 60 GB of data, and 500 SMS messages per month. The AI Telco assistant would then analyze this request, check the market data, analyze the market behavior, check out the options to be clubbed, predict the user behavior, and determine the best plan that meets the user’s needs, and as a next step with the use of plugins configure this plan in various network nodes. This will be a giant leap in Telco networks as both vendors and telecom operators will focus more on innovation towards customer experience rather than deployments in complex multi-vendor networks

The benefits of using AI for configuring plans are many.

• Firstly, the AI Telco assistant can provide a much more intuitive and user-friendly interface compared to traditional configuration interfaces, which require users to have technical knowledge of the underlying systems which will come from various network vendors each using a different methodology to get the same result

• With a conversational AI interface and assistant, users can describe their requirements in plain language, making it easier and faster to create customized plans. This reduces the training cost of tech force on individual vendor products. The focus will be more on business rather than technology towards implementing it.

• A** “plan”** in a telecom system touches various nodes of a Network where each node requires unique expertise. With reinforced learning and older data set training, the AI can be trained to gain knowledge of each node for implementing a configuration.

• Any “plan” in the market is the brainchild of many data analytical points which includes studying patterns, analyzing data keeping track of competition. This matrix is impossible for a human to do. An AI can do this much better at a faster pace and the final decision will still be of the human.

But most importantly the training data for doing such configurations would be the key to this application

A supervised learning algorithm such as a decision tree or a neural network suits the best for this application. The ML engine would need to be trained using historical data of rate plans and their configurations in various network nodes. The data could be gathered from previous configurations done by users or it could be synthesized using simulated data.

The ML engine would need to be integrated into the network nodes and the configuration process. It would learn from each configuration made by the user and update its knowledge of the rate plans and their configurations. As the ML engine learns, it would become better at predicting the best configuration for new rate plans and the network nodes it needs to interact with.

The ML engine could be implemented using Python or any other scripting language that supports machine learning libraries such as TensorFlow, Scikit-Learn, or PyTorch. The ML engine would need to be trained on this large dataset of historical data on rate plans and their configurations and data analytics.

The training process would involve the following steps:

1. Data Collection: Collect a large dataset of historical data on rate plans and their configurations. This data could be gathered from previous configurations done by users or it could be synthesized using simulated data.

2. Data Preprocessing: Clean and preprocess the data. This includes removing duplicates, handling missing values, and transforming the data into a format that can be used by the ML algorithm.

3. Feature Engineering: Identify the key features that are relevant for predicting the configuration of rate plans. This could include data on the user, the type of plan, usage patterns, and so on.

4. Model Training: Train the ML model using the preprocessed data. This involves selecting the appropriate ML algorithm and tuning its parameters to achieve the best performance.

5. Model Evaluation: Evaluate the performance of the ML model on a validation set of data. This will help you to identify any issues with the model and to fine-tune it further.

6. Deployment: Deploy the trained ML model in the BSS system and integrate it with the configuration process. As the ML engine learns from each configuration made by the user, it will update its knowledge of the rate plans and their configurations, becoming better at predicting the best configuration for new rate plans.

For the above project, we would need to design an architecture that can handle data collection, processing, and analysis. The architecture would consist of the following components:

1. Data Collection Layer: This layer will consist of components that will collect data from different sources, such as the BSS system, CRM system, and Payment Gateway, GGSNs, mediations, API gateways, Dataware house among others.

2. Data Storage Layer: This layer will consist of components that will store the collected data. We can use big data technologies such as Hadoop or Spark for this layer.

3. Data Processing Layer: This layer will consist of components that will preprocess the data before it is fed into the ML model. This layer can be implemented using technologies such as Apache Kafka, Apache NiFi, or Apache Storm.

4. Machine Learning Layer: This layer will consist of the ML model that will be trained on the collected data. We can use supervised learning algorithms such as Decision Trees, Random Forest, or Gradient Boosting for this layer.

5. User Interface Layer: This layer will consist of a user interface that will allow the user to interact with the system and provide input for creating the plans.

6. Integration Layer: This layer will consist of components that will integrate the ML model with the BSS system.

On a High-level Technology Stack:

For the above architecture, we can use the following technologies:

1. Hadoop or Spark for data storage

2. Apache Kafka, Apache NiFi, or Apache Storm for data processing

3. Python or R for ML model development

4. REST API for integrating the ML model with the BSS system

5. React for the user interface

AI has the great potential to transform the telecom space by improving network optimization, fault management, predictive maintenance, billing and rating, and customer experience. With the advancements happening in Machine Learning algorithms, Natural Language Processing, Deep Learning, and Computer Vision, AI can be used to make telecom networks smarter and more efficient. However, implementing AI in telecom networks poses several challenges such as data quality, integration with legacy systems, and lack of skills. A project like this would involve a lot of data processing, feature engineering, and machine learning. It would require expertise in data science, machine learning, and software engineering to design and implement an effective ML engine for rate plan configuration

So what do you think will be done in the future in the Telcom Industry?

Let's analyze two parallel realities of the universe where on one side all this is continued in the traditional way and on the other side this is done via AI.

Reality 1 — Without AI, network optimization is a time-consuming and complex process that requires human intervention at various stages. The network data is analyzed manually, and decisions are made based on historical data and best practices. This approach can be slow and often leads to suboptimal results due to the complexity and dynamic nature of the network. The cost of human training is high, and with high attrition rates, there will be tipping points when the plans are put in the back seat.

**Reality 2 **— With AI, network optimization, and configuration can be automated and performed in real-time. AI algorithms can analyze vast amounts of data from various sources, such as network performance data, and user behavior data, including internet data, to identify patterns and predict network issues before they occur. AI can also be used to optimize network resources and traffic management to ensure that the network is operating at maximum efficiency as well as create plans for monetization which will be a boon for this Industry.

If you are associated with the telecom Industry which reality do you want to live in?