Generative AI

THE DIGITAL TRANSFORMATION IN THE TELECOMMUNICATIONS INDUSTRY

Generative AI

Scenario

The telecommunications sector is currently undergoing a transformative revolution across various dimensions. Emerging technologies such as the Internet of Things (IoT), 5G, and edge computing are gaining significant traction, propelling both the telecom industry and the broader business landscape into a new era.

This revolution extends beyond technical advancements, influencing the business strategies and paradigms guiding the field.

Telecom companies are transitioning from a monolithic business model to unbundling into distinct legal entities: NetCo, representing the actual network infrastructure, and ServCo, handling the customer-facing side.

This shift is facilitated, among other factors, by an emerging cloud model known as Network-as-a-Service (NaaS), empowering users to operate a network without the need for ownership or direct management of the infrastructure.

The key features of this technology are:

• Cloud-native architecture which allows performances resilience, reliability, scalability and monitoring;

• Network disaggregation that enables the compatibility and independence of different parts of both the hardware and the software;

• AI integration to improve efficiency, reliability and security and make networks able to dynamically adapt to constantly changing environments;

• 5G bandwidth allowing up to 20 times the current load capabilities.

GENERATIVE AI

Generative AI (GenAI) represents a relatively new paradigm in deep learning, contrasting with the common Discriminative AI approach. In Discriminative AI the model is provided with data as input and it is able to regress, classify or identify patterns within it, essentially replicating straightforward human tasks. An example could be text classification: given a text the algorithm classifies its underlying topic. Generative AI takes a step beyond this, involving algorithms capable of generating completely novel data different from what they have seen in training, akin to human creativity. In this scenario, the model isn't confined to addressing a specific task; instead, it must comprehensively and deeply grasp the input data, understanding its structure and the underlying probability distribution in order to being able to reproduce them by itself. This includes the generation of diverse types of data, ranging from images that closely resemble real ones to well-structured texts or audio files. Considering the earlier example, one could perceive it as a reverse process: inputting a topic, and having the algorithm generate a corresponding text.

We have different types of models, all deep learning-based:

• Variational Autoencoders: these models learn to compress data into a latent space corresponding to the parameters of a variational distribution and then reconstruct the data;

• Generative adversarial network: composed by a generator (a network that generates data) and a discriminator (a network that attempts to distinguish between real and generated data) that play against each other in a zero-sum game;

• Transformers-like architecture: these include encoder-decoder models as well as encoder only (BERT) or decoder-only (GPT) architectures.

GENERATIVE AI IN TELECOMMUNICATIONS

Artificial Intelligence and in particular Generative Artificial Intelligence represent a powerful tool to tackle the challenges derived from telecommunication transformation and innovations as well as the increasing complexity of today’s networks. Telecom companies are progressively integrating these tools across various facets of their business, despite still being in the early stages of adoption.

AI can be used in network automation, to enhance customer experience or optimize business processes.


MAIN USE CASES

Intelligent NOC (Digital twin)

A Network Operations Center (NOC) serves as a centralized hub for monitoring and managing telecommunication systems. When issues arise, NOC operators take appropriate actions to address them. Currently, many of these actions rely on the expertise of human operators. Technicians consistently assess the network's status and incoming alarms to determine how, where, and when interventions are needed.

Due to the high complexity of the system a very small subset of simpler actions can actually be automated, essentially rendering the work of the NOC a predominantly manual process. Furthermore, the escalating complexity of networks is posing challenges even for human operators in identifying the most effective actions. This is why a transition is taking place towards intelligent NOCs, where human supervision is augmented and potentially replaced by algorithms.

To construct an intelligent NOC, the essential components include a Reinforcement Learning (RL) algorithm and a network model.

RL, a type of Machine Learning algorithm, consists of an environment (in this case, the network) and an agent representing the algorithm exploring and interacting with the environment. The agent learns how to act based on predefined objectives. In this case the agent monitors various variables and features of the network and responds in order to address any issues, recommend an action, optimize certain variables or minimize the risk of future failures. RL can deal with very sophisticated environments, it can continuously interact with and learn from them, making it suitable for complex and fast-changing environments like todays networks. An RL algorithm can handle a multitude of variables far beyond the capacity of a team of humans, leveraging much more intricate relationships between them; all this resulting in more effective solutions.

The environment can be embodied by a digital twin, serving as a high-precision, virtual representation of a corresponding physical entity. With GenAI we can create such interactive digital twins to train our RL algorithms without disrupting the actual architecture. Additionally, digital twins usually entail considerable expenses in terms of time, finances, resources, and data. This presents a significant drawback to the technology. To address this issue, GenAI allows us to surpass these limitations by training our digital twins using generated data instead of rigidly defining their behavior. We can even construct less complex models of digital twins that maintain the desired performance and functionality while incurring lower costs and resource utilization.


NET REPLY ROLE

At NetReply, we pride ourselves on being a networking system company that combines years of expertise and knowledge with a passion for innovation and cutting-edge technologies. Our team of experienced professionals is dedicated to staying ahead of the curve and exploring new ways to improve our products and services. By integrating new technologies into our projects, we aim to provide our clients with the most advanced and efficient solutions available. We believe that continuous learning and growth are essential for staying at the forefront of the industry, and we're committed to equipping our team with the skills and knowledge they need to excel with a strong training and certification philosophy.


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