Written By: Sarah LaSelva, Director of 6G Marketing, Keysight Technologies
The artificial intelligence (AI) revolution is here. People are receiving firsthand experiences through the power and potential of deep neural networks and machine learning (ML) with the public release of applications like ChatGPT. ChatGPT is a language model trained on text from the vast amount of text-based content on the internet and books allowing it to generate human-like text. This type of application is a perfect example of the strengths of AI. It can optimise an output to a complex scenario based on large sets of training data.
Wireless networks are inherently complex, generate massive amounts of data, and have been growing in complexity with each new generation of technology that is introduced. This makes AI an ideal tool to optimise wireless networks.
AI in 5G Networks
As 5G matures, AI and ML are already being introduced for study by the 3rd Generation Partnership Project (3GPP), the standardisation body that maintains cellular standards. Applications of AI under consideration are primarily in the air interface, including network energy saving, load balancing, and mobility optimisation. Potential use cases in the air interface are so numerous that a small subset has been selected for study in the upcoming 3GPP Release 18, including channel state information (CSI) feedback, beam management, and positioning. It is important to note that 3GPP is not developing AI/ML models. Rather it seeks to create common frameworks and evaluation methods for AI/ML models being added into different air interface functions.
Outside of the 3GPP and the air interface, O-RAN ALLIANCE is exploring how AI/ML can be used to improve network orchestration. For example, O-RAN ALLIANCE has a unique feature in its architecture called the RAN Intelligent Controller (RIC) that is designed to host AI/ML optimisation applications. The RIC can host xApps, which run in near real-time, and rApps, which run in non-real-time. xApps for improving spectral efficiency and energy efficiency and rApps for network orchestration that leverage AI already exist today. More xApps/rApps and applications using AI/ML in the RIC will become available as the O-RAN ecosystem grows and matures.
Fig. 1. ORAN Network
AI-Native 6G Networks
6G is in its infancy, but it is already clear that AI/ML will be a fundamental part of all aspects of future wireless systems. On the network side, the term “AI native” is used widely in the industry despite not being officially defined. One way of looking at these AI-native networks is to extrapolate the diagram above (Figure 1) based on current trends of virtualisation and disaggregation of the RAN (Radio Access Network). Each block of the network is likely to contain AI/ML models that will vary from vendor to vendor and application to application (Figure 2).
Fig. 2. ORAN 6G Network
AI-native networks can also mean networks that were built to natively run AI/ML models. Consider the design flow below (Figure 3). In traditional 5G networks, the air interface is made up of different processing blocks, each designed by humans. In 5G Advanced, each block will leverage ML to optimise a specific function. In 6G, it is possible that AI will design the entire air interface using deep neural networks.
Fig. 3. Progression from AI infused to AI native networks
AI/ML Optimisation
Building on the idea that AI/ML can be used to improve network management orchestration, 6G looks to leverage AI and ML to solve optimisation challenges. For example, AI could be used to optimise the power consumption of the network by turning on and off components based on real-time operating conditions. Today, xApps and rApps accomplish this at a base station level by turning on and off power-hungry components like power amplifiers when they are not in use.
“Wireless networks are inherently complex, generate massive amounts of data, and have been growing in complexity with each new generation of technology that is introduced. This makes AI an ideal tool to optimise wireless networks.”
However, the ability of AI to quickly solve challenging compute problems and analyse large amounts of data opens the possibility of optimising our networks at a larger, city-wide, or national scale. Entire base stations could be turned off during low use and cells could be reconfigured to service real-time demand in an energy optimized way using the least possible resources. It is not possible to reconfigure base stations and city-wide networks in this way today—it takes days or weeks to reconfigure and test any changes made to network configurations. Though, advances in different AI techniques remain promising and are top of mind for infrastructure providers.
Conclusion
Wireless networks will not wait for 6G to start leveraging the power of AI. Active research is happening across the entire ecosystem to develop new models and integrate them into the wireless systems of both today and tomorrow. However, these models are still new and need to be evaluated for rigor and reliability. Properly training AI models on diverse data sets, quantifying their improvement over traditional techniques, and defining new test methodology for AI enabled modules are critical steps that must be taken as this new tech is adopted. As AI models and testing best practices mature, there is no doubt that AI will revolutionise wireless communications in the next 5–10 years.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)