What you’ll learn:
- How 6G technology will support wireless in the long run.
- Why it's essential to evaluate every use case and determine if and how to deploy AI.
The future of wireless is closer than you might think, with 6G networks promising higher performance and flexibility to enable use cases that go far beyond what we do with today’s wireless systems. Standards and specifications development has just begun with the first 3rd Generation Partnership Project (3GPP) release, including 6G expected “no later” than March 2029.
Research is beginning its shift to development. We can expect early trials of 6G technologies starting in 2027. 6G research and early development is progressing in tandem with AI maturity, ensuring that 6G will be the first generation of wireless to be AI-native. The technology will be pivotal in realizing 6G’s potential for intelligent, autonomous, and transformative wireless communications—opening new possibilities across industries.
A Hyper-Connected World in the 6G Era
6G wireless networks will be able to process unprecedented volumes of data in real-time, along with better latency, security, and reliability. With unrivaled compute and connectivity, traditional on-and-offline boundaries will disappear as virtual, mixed, and augmented reality become part of our lives.
Possibilities include enterprise metaverses where employees teleport to work regardless of their physical location, or haptic sensory suits that remotely control industrial machines and react to scenarios in real-time. The massive amount of data processing and networking that’s involved with these and other 6G applications, combined with the ever-increasing complexity of wireless networks, make AI a critical element to 6G’s success.
What follows are some of the key ways the technology will support the future of wireless:
Network optimization and automation
Channel-state information (CSI) is used in real-time throughout a wireless system to adapt radio transmissions to current conditions while maintaining optimal performance. Precise CSI is a computational- and resource-intensive task, making it ideal for AI. Algorithms could send the minimum information needed, delivering significant gains in performance, resource utilization, and energy efficiency.
The technology can also enhance channel-state predictions and thus improve beamforming. This would enable more efficient, effective, and reliable management of radio links adjusting to changing environments while consuming fewer resources, supporting sustainability goals.
Solving spectrum-sharing challenges
Policymakers are anxious for the new spectrum under consideration for 6G to be shared rather than be exclusive. This means sharing with non-cellular systems (radar, satellite, government) and other mobile operators, leading to concerns around spectrum utilization, coexistence, and reliability.
AI can address these issues in numerous ways. They include intelligent sensing capabilities to more accurately identify available spectrum and any potential interference, adaptive allocation based on network conditions and user demands, and learning from network data to optimize spectrum-sharing protocols.
Scenario planning to optimize resource allocation
AI can optimize resource allocation based on usage patterns. For example, by analyzing data from a city's commute times versus traffic during midday, the model would predict how much bandwidth is needed at peak times and then shift to other areas after the commute is over. In addition, it can aid traffic management of network data once data messages get into the wired part of the wireless network. This dynamic approach, which is enabled by AI, enhances sustainability goals.
Improved security and resilience
Another benefit of AI-native 6G is enhancing security and resiliency through real-time threat detection, vulnerability management, security analytics, and automated security controls. The intelligence supports context-aware defenses and distributed security at the network edge.
AI is No Silver Bullet
AI is an exciting technology that can solve numerous wireless challenges that plague us today, but it’s a fallacy to assume it should be the default tool for every 6G problem. There are some situations where AI's performance is on par—or worse—than traditional methods. In these scenarios, it's not worth developing an algorithm to replace the legacy approach.
Moreover, there are areas where the technology may not make economic sense because it’s too power-hungry. That’s why it's essential to evaluate every use case and determine if and how to deploy AI.
Evaluating interdependencies is another important step. For example, if AI replaces one element in a system, the training and integration process is straightforward. However, if there are multiple elements, not only is training more complex, but trained systems can “fight” with each other, resulting in suboptimal behavior.
Trustworthiness is another vital consideration. For the industry to rely on the technology, it's critical to understand the pattern AI will follow to arrive at a specific output based on the information provided. Trustworthy AI models must be reliable, work as expected every time, and be explainable.
The Path Ahead for 6G
We are in the early days of learning AI's limits and best practices, but its revolutionary potential impact on wireless communication can’t be overstated. AI-powered 6G will bring new applications to every sector, ranging from immersive consumer experiences to high-powered machine-to-machine communication.
Such technologies will continue to work in lockstep together as the industry prepares for this next generation of mobility, creating a future in which communication and connection will know no bounds.
