Artificial intelligence (AI) programs like ChatGPT, Gemini, and Microsoft Copilot have made headline news in recent years, as millions of people have tapped into the power of generative AI to produce copy, create graphics, and check code.
However, AI and machine learning (ML) can do far more than simply serve curious creatives or speed up graphic design projects. When integrated into next-generation network management, ML tools can transform how systems operate by automating complex tasks, optimizing bandwidth usage, and improving overall security. This includes the potential to create networks within networks that spot errors, improve connectivity, and enhance the functionality of future 6G networks.
That said, AI and ML programs will require significant oversight to be effective. Proper oversight is critical, as AI programs can generate real-time insights but often lack the wider context necessary to make informed decisions.
AI and the 6G Revolution
ML is sure to improve the efficiency and performance of next-gen networks. However, contrary to the hype, artificial intelligence has actually been a part of existing networks for half a decade. As Rohde & Schwarz’s Andreas Roessler explains, 5G AI “involved gathering lots of data” and that advanced AI analytics have been used to “network functions and simplify operations.”
However, as AI’s power has progressed, more has become possible. This rapid advancement is why many speculate that 6G will do far more than build on previous progress — it will create an entirely new way of leveraging AI to improve networks. This expected leap is typically pitched as an “AI-native network” and contains several key features, including:
- AI implementation as a foundational element, rather than an “add-on” used in specific scenarios.
- Processes like network protocols, signal processing, and system optimization will use AI as a built-in feature.
- Increased ability to transfer AI models between networks, thus improving integration.
- Improved lifecycle management thanks to enhanced data availability.
ML systems must be tested relentlessly before release for AI to be implemented successfully. However, analog disruptions like carrier frequency offset can disrupt accurate testing. But not all is lost. ML systems can use “impaired data” — information affected by these analog disruptions. As Roessler states, “Neural receiver models must be trained with impaired data to handle analog impairments.” By training ML programs with impaired data, the roll-out of 6G techniques like neural receiving can be made that much smoother.
Next-gen AI can enhance efforts to train neural receivers with impaired data by generating training data. In this sense, generative artificial intelligence is best used to mimic user input without causing delays or disruptions to real users. This will minimize error rates when technology becomes public and improve neural receiver capabilities.
Network Monitoring
AI can be used to improve the resiliency and reliability of next-gen networks. Many even predict that zero-touch cognitive networks will empower intelligent network devices to complete their own maintenance and monitoring. This approach can improve connections between decentralized networks and improve local learning and decision-making at distributed sites.
However, for the transition to 6G and Industry 4.0 to be successful, ML programs must be trained properly in network monitoring. This requires extensive data preparation, algorithm refinement, and ongoing updates to ensure the models can adapt to evolving network demands. Additionally, collaboration between human telecom experts and AI systems will be vital, as experts can provide the strategic oversight and contextual understanding that AI lacks. By complementing human expertise, AI systems can streamline complex processes, reduce downtime, and free up resources for innovation. That said, AI can follow and even improve upon network monitoring best practices such as:
- Key performance indicator (KPI) monitoring;
- Proactive testing and changes based on historical data;
- Documentation and data cleaning;
- Identification of security threats.
AI models can be trained to understand more than one data type and can leverage SNMP, custom scripts, and API data with relative ease. As such, leveraging AI within monitoring plans can enhance the efficiency, resilience, and security of next-gen networks. This is crucial, as emergent technology like 6G has to convince users that the tech is safe, reliable, and meaningfully improves upon 5G networks.
Addressing Connectivity Issues
Slow or unreliable connectivity is no longer a mild inconvenience — it costs companies money. Unreliable connectivity can result in revenue loss by impairing employee productivity, reducing revenue, causing delays and disruptions to video conferences, and derailing the customer experience. Addressing these concerns is crucial today, as many modern businesses now rely on high-speed connections for everyday tasks.
AI and ML can significantly reduce the risk of disruption in the era of 6G by creating networks within networks. This approach minimizes delays and poor connectivity by creating a seamless link between access technologies, such as cellular, Wi-Fi, and satellite, enhancing the end user’s experience. Moreover, predictive analytics powered by AI can proactively identify potential connectivity issues, allowing network administrators to address them before they escalate.
Using AI to prioritize end-user experience can hasten the expansion of 6G connectivity, too. Advanced network analytics can help businesses track and optimize performance metrics in real time, ensuring consistently high-quality service. Put simply, businesses are far more likely to invest in 6G when they believe the tech will minimize the risk of disruption and enhance their overall digital connectivity.
Conclusion
AI and machine learning have been part of networks for nearly a decade. However, the next-gen revolution will transform AI from a case-by-case tool into foundational elements of connectivity. If leveraged correctly, this will enhance efficiency, improve performance, and minimize the risk of disruption due to the prevalence of autonomous networks within networks.
Moreover, these technologies hold the potential to redefine how businesses approach connectivity, enabling a shift from reactive to proactive network management. As AI-driven systems evolve, they will provide smarter resource allocation, stronger cybersecurity measures, and enhanced adaptability to rapidly changing demands. This transformation will not only boost operational capabilities but also foster trust in emergent technologies like 6G. By integrating AI and ML as core components, next-gen networks can deliver the speed, reliability, and resilience that modern businesses and end-users require in an increasingly connected world.