The AI Defined Cellular Networks

- Overview

The Third Generation Partnership Project (3GPP) has been working hard to develop specifications to further integrate AI/ML into 5G and Beyond networks. The great work continues to be done as wireless cellular networks prepare to take advantage of some of the latest advances in AI and ML. 

When it comes to traditional applications of AI and ML in wireless cellular networks, efforts tend to focus on a few key areas where intelligent classification and regression are useful, including:

• Enhance performance through traffic forecasting and management, where ML models analyze traffic patterns to predict demand surges and adjust network resources accordingly. It then automates resource allocation by dynamically allocating bandwidth and other network resources where they are needed most, optimizing the performance of high-demand applications such as streaming media, gaming and virtual reality. 
• Improve security through anomaly detection, where AI can monitor network traffic in real time to detect and respond to unusual patterns that may indicate security threats, such as DDoS attacks or unauthorized access attempts. In addition, AI and ML can enhance security protocols, including developing more secure biometric authentication methods and detecting vulnerabilities in network infrastructure.
• AI can help with network slicing, as this network feature allows operators to create multiple virtual networks with different characteristics on a single physical infrastructure. This is critical to supporting a variety of applications, from IoT devices with lower data requirements to high-bandwidth applications such as 4K video streaming, which have specific requirements for latency, speed and reliability. 
• Enhanced user experience. Even without network slicing, artificial intelligence can be used to analyze network conditions and user behavior to dynamically adjust quality of service (QoS) settings to ensure optimal service levels for various applications and services. Artificial intelligence is used together with predictive analytics to evaluate data on user behavior and device performance, helping to predict user needs and adjust services accordingly to enhance user experience.