What Is AI in Telecommunications?

AI has the potential to drive optimizations, efficiencies, and innovation in the telecom industry.

One area that has garnered a lot of optimism and interest is using machine learning to create partially—and, one day, fully—autonomous networks. The idea is that an AI-enabled RAN can make intelligent predictions based on network data and then automatically enact decisions to enhance the network’s overall performance.

AI tools and automation can also help simplify the increasingly complicated process of managing modern telecommunication networks. By automating many of the routine tasks involved in keeping a wireless network up and running, machine learning algorithms can help service providers optimize operations, drive growth, and secure the network.

Looking closer at AI on the edge, edge AI lets service providers deliver services for applications like computer vision, autonomous devices, and immersive experiences. By processing data at the edge, innovation and growth can thrive over wireless 5G networks.

Benefits of AI in Telecommunications

Telecom networks generate massive amounts of data. AI can help service providers leverage their data to better optimize their networks. Likewise, AI can help them overcome the complexities of managing modern telecom infrastructure. The benefits of AI in telecom include:
 

• Optimized, intelligent networks: One benefit is that networks will become more optimized, intelligent, and autonomous. By analyzing network data, AI can boost network performance and make networks more sustainable and energy efficient. This improves the overall quality of the network and lowers energy costs.
• Increased operational effectiveness: Another benefit of AI is that it allows telecom service providers to achieve operational effectiveness by automating routine management tasks. For example, operators can use AI models to help identify and resolve critical issues that occur within the network. This saves time, reduces operational expenses, and improves the quality of service provided to customers.
• Security and trustworthiness: AI-assisted security helps operators defend networks against malicious threats as they evolve and bad actors change their tactics.

AI Use Cases in Telecommunications

Some use cases for AI in telecom are already a reality, while others are a bit further out on the horizon. Some examples include:
 

• Enhanced energy efficiency: AI models can make intelligent choices about RAN energy consumption and radio resource allocation, adjusting resources and turning off bands as needed to increase energy efficiency.
• Network traffic forecasting: AI can analyze network data and predict usage patterns to keep networks operating smoothly during spikes in traffic, allocating more resources or rerouting traffic to prevent bottlenecks.
• Network planning and design: AI models can assist in various phases of planning and implementing cellular networks, including network mapping, radio frequency (RF) map generation, and capacity planning, helping architects design more-effective networks.
• AI-assisted cybersecurity and electromagnetic defense: AI-assisted threat monitoring can detect abnormal patterns in network traffic and help defend cellular networks against increasingly sophisticated cyberattack techniques—including distributed denial-of-service attacks—jamming attacks to RAN and fake base stations.
• Fault management and anomaly detection: Network operators are using AI models to detect faults, malfunctions, and anomalies within the network, which can cause service interruptions. Once an abnormal behavior is identified, models can be trained to reduce future occurrences.
• AI-driven root cause analysis: AI-driven analysis assists in pinpointing the source of problems and threats that arise within the network across multiple layers of virtualization.
• Targeted services: AI has become more effective at gathering insights from vast amounts of unstructured data, such as customer behavior data on the network. This could potentially assist in creating tailored services and offerings that align with customer needs.

The Future of AI in Telecommunications

The industry is making strides toward implementing AI into their infrastructure strategies. Service providers still need to attain their digital transformation goals in order to do so. The industry faces several challenges in that regard, including breaking down huge data silos and embracing virtualization. Looking forward, the arrival of 6G wireless networks is expected to accelerate AI for telecom use cases. Research is underway on AI-based techniques that bring flexible, intelligent automation to RAN—enhancing many key performance metrics—and also on standards that will boost the network edge for emerging autonomous and immersive services.