Artificial Intelligence

Artificial Intelligence (AI) in Telecommunications: How to Build Successful AI Infrastructure

AI in Telecommunications

Compared to predecessors, modern computing devices are more powerful and come with a higher capacity – all with lower power consumption. They can process higher volumes of data at a fraction of the cost, leading to innovations for the telecommunications industry.

Technologies like the Internet of Things (IoT) and 5G support telecommunications infrastructure that can scale data processing at an incredible rate, enabling the use of artificial intelligence (AI).

AI in Telecommunications

AI is not one technology, but an umbrella of technologies that mimic human cognition to solve problems without the need for human input.

With the capabilities of AI, telecommunications companies can automate repetitive tasks to alleviate the burden on workers, minimizing the risks of errors while improving operational efficiency.

The costs associated with client data processing can be outsourced to an edge-computing network that prepares inputs for complex decision-making algorithms in the main network. Together, AI, IoT, 5G, and edge computing elevate the capabilities for network performance, security, and energy efficiency.

But because AI is a data-driven technology, telecommunications companies and their clients have to prioritize robust data management practices to ensure compliance with industry standards.

Data Centers and Distributed Networks with AI Infrastructure in Telecom

One of the greatest challenges for AI adoption is the construction of the AI infrastructure. In the past, telecommunications networks were designed to support telephony, but with the advances in networks – particularly 4G LTE networks – these networks now rely on the power of digital signals for operations.

AI technologies require the allocation of tremendous computing resources to manage data inputs for processing, however. These resources depend on servers located in physical data centers and networking and application servers that transmit data.

Hardware Servers vs. Virtual servers

Physical servers offer a lot of computing resources to devote to a single application. They can take on large data volumes and cases where data privacy is paramount as well. Still, physical servers require physical storage, which is finite and comes at a high cost.

Virtualization can run a computer entirely within an existing hard drive, or virtual machine (VM). The physical server can run multiple VMs, all customized to client specifications. Though this is not as powerful of an option as physical servers, it’s a scalable solution for running AI and machine learning (ML) applications.

Distributed Computing Resources

In reality, computer systems in telecommunication networks are working with limited bandwidth and resources, so they can only process and transmit a limited amount of data. There’s also a limit to the calculations they can perform within a time frame.

To offset these limits, telecommunications may use distributed networks to allocate core tasks to a central server with a sufficient amount of resources. The central server offers high processing speeds and low latency.

Computers that lack sufficient computing power or bandwidth are separated from the central server. They can be located close to the primary server if they have the bandwidth or close to application servers if low latency is a requirement. Together, they form the edge network.

Centralized Training and Inference

Centralized servers provide high computing resources and bandwidth. They’re located far from the application layer, and they’re equipped with AI/ML inputs to be relayed from a different location.

These resources are necessary when:

  • High volumes of data need to be processed
  • Latency or real-time efficiency is not a priority
  • The processing is complex

Distributed Training and Inference

Distributed computers are used to run training and interference models and reside on opposite ends of the network. The algorithms that run on these computers need to be simplified to run on limited resources.

The data these computers process keeps them close to the application and it can be sent to the central server for additional computing power. Distributed systems do require additional work to synchronize them with the network.

These resources are necessary when:

  • Data inputs are simple
  • Ultra-low latency is necessary for user experience or performance
  • Computing resources are limited

Hybrid (Centralized/Distributed) Training and Inference

Infrastructure design in telecommunications is designed with agility in mind. Because telecommunications companies deal with high volumes of structured and unstructured data, the computing resources need to be split to ensure performance, latency, and security are not compromised.

A telecommunications company that requires low latency for its services may need to compromise on security or performance. This isn’t always possible, but there is a “happy medium” for acceptable latency. In this case, hybrid infrastructure with AI/ML modeling is helpful.

Some of the modeling techniques include:

Extract, Transform, Load

An extract, transform, load (ETL) model is an option when edge devices are not powerful and require a central server. With these cases, edge computers send data to a centralized server in batches.

The server then simplifies the data structure and sends it back to the edge to help with AI training. This relieves some of the burden on edge devices and supports training without demanding high computing resources.

Centralized Initial Training

Centralized initial training relieves some of the burden on edge-computing networks, and less resource-intensive training can be transferred to the edge network.

The data size can be reduced to meet bandwidth and computing requirements, which can be done one of two ways:

Quantization: This is the process of reducing the precision of input values sent for algorithmic computation to reduce the data size.

Sparsification: This is the process of minimizing the coding length of stochastic gradients within a neural network to reduce its memory and costs.

Pipeline Complexity and Maintenance Costs

AI/ML pipelines typically have a few stages of model experimentation, testing, and deployment that require manual effort.  The complexity is usually proportional to the features of the AI models.

The costs for the work completed by supporting operations staff, such as engineers and developers, the steps in the pipeline can be automated. Without automation, an ML process can significantly increase operational costs.

Leverage AI in Telecommunications

IoT and 5G systems allow telecommunications to create distributed systems that support cost-effective and scalable AI solutions. These benefits are only possible with innovative implementation of hardware infrastructure that supports AI algorithms, however. The telecommunications sector can rely on smart infrastructure to build AI systems that provide value to businesses and their customers.

Subbu Seetharaman

Subbu Seetharaman is the Director Of Engineering, at Lantronix, a global provider of turnkey solutions and engineering services for the internet of things (IoT). Subbu is an engineering executive with over 25 years experience in leading software development teams, building geographically distributed, high performing teams involved in developing complex software products around programmable hardware devices.

I am enthusiastic blogger & SEO expert. I am digitally savvy and love to learn new things about the world of digital technology. I loves challenges come in my way. I also prefer to share useful information such as SEO, Google Algorithm Update, SMM, PPC, WordPress, Web Hosting, Affiliate Marketing etc.

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