AI RAN
Nokia and Rohde & Schwarz collaborate on AI-powered 6G receiver years before IMT 2030 RIT submissions to ITU-R WP5D
Nokia and the test and measurement firm Rohde & Schwarz have created and successfully tested a “6G” radio receiver that uses AI technologies to overcome one of the biggest anticipated challenges of 6G network rollouts, coverage limitations inherent in 6G’s higher-frequency spectrum.
–>This is truly astonishing as ITU-R WP5D doesn’t even plan to evaluate 6G RIT/SRITs till February 2027 when the first submissions are invited to be presented.
Nokia Bell Labs developed the receiver and validated it using 6G test equipment and methodologies from Rohde & Schwarz. The two companies will unveil a proof-of-concept receiver at the Brooklyn 6G Summit on November 6, 2025. Nokia says, “the machine learning capabilities in the receiver greatly boost uplink distance, enhancing coverage for future 6G networks. This will help operators roll out 6G over their existing 5G footprints, reducing deployment costs and accelerating time to market.”
Image Credit: Rohde & Schwarz
Nokia Bell Labs and Rohde & Schwarz have tested this new AI receiver under real world conditions, achieving uplink distance improvements over today’s receiver technologies ranging from 10% to 25%. The testbed comprises an R&S SMW200A vector signal generator, used for uplink signal generation and channel emulation. On the receive side, the newly launched FSWX signal and spectrum analyzer from Rohde & Schwarz is employed to perform the AI inference for Nokia’s AI receiver. In addition to enhancing coverage, the AI technology also demonstrates improved throughput and power efficiency, multiplying the benefits it will provide in the 6G era.
“One of the key issues facing future 6G deployments is the coverage limitations inherent in 6G’s higher-frequency spectrum. Typically, we would need to build denser networks with more cell sites to overcome this problem. By boosting the coverage of 6G receivers, however, AI technology will help us build 6G infrastructure over current 5G footprints,” said Peter Vetter, President, Core Research, Bell Labs, Nokia.
“Rohde & Schwarz is excited to collaborate with Nokia in pioneering AI-driven 6G receiver technology. Leveraging more than 90 years of experience in test and measurement, we’re uniquely positioned to support the development of next-generation wireless, allowing us to evaluate and refine AI algorithms at this crucial pre-standardization stage. This partnership builds on our long history of innovation and demonstrates our commitment to shaping the future of 6G,” said Michael Fischlein, VP, Spectrum & Network Analyzers, EMC and Antenna Test, Rohde & Schwarz.
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Last month, Nokia teamed up with rival kit vendor Ericsson to work on video coding standardization in preparation for 6G. The project, which also involved Berlin’s Fraunhofer Heinrich Hertz Institute (HHI), demonstrated a new video codec which they claim has higher compression efficiency than the current standards (H.264/AVC, H.265/HEVC, and H.266/VVC) without significantly increasing complexity, and its wider aim is to strengthen Europe’s role in next generation standardization, we were told at the time.
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About Nokia:
At Nokia, we create technology that helps the world act together.
As a B2B technology innovation leader, we are pioneering networks that sense, think and act by leveraging our work across mobile, fixed and cloud networks. In addition, we create value with intellectual property and long-term research, led by the award-winning Nokia Bell Labs, which is celebrating 100 years of innovation.
With truly open architectures that seamlessly integrate into any ecosystem, our high-performance networks create new opportunities for monetization and scale. Service providers, enterprises and partners worldwide trust Nokia to deliver secure, reliable and sustainable networks today – and work with us to create the digital services and applications of the future
About Rohde & Schwarz:
Rohde & Schwarz is striving for a safer and connected world with its Test & Measurement, Technology Systems and Networks & Cybersecurity Divisions. For over 90 years, the global technology group has pushed technical boundaries with developments in cutting-edge technologies. The company’s leading-edge products and solutions empower industrial, regulatory and government customers to attain technological and digital sovereignty. The privately owned, Munich-based company can act independently, long-term and sustainably. Rohde & Schwarz generated a net revenue of EUR 3.16 billion in the 2024/2025 fiscal year (July to June). On June 30, 2025, Rohde & Schwarz had more than 15,000 employees worldwide.
References:
ITU-R WP5D IMT 2030 Submission & Evaluation Guidelines vs 6G specs in 3GPP Release 20 & 21
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
Highlights of Nokia’s Smart Factory in Oulu, Finland for 5G and 6G innovation
Verizon’s 6G Innovation Forum joins a crowded list of 6G efforts that may conflict with 3GPP and ITU-R IMT-2030 work
Qualcomm CEO: expect “pre-commercial” 6G devices by 2028
NGMN: 6G Key Messages from a network operator point of view
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Market research firm Omdia (owned by Informa) this week forecast that 6G and AI investments are set to drive industry growth in the global communications market. As a result, global communications providers’ revenue is expected to reach $5.6 trillion by 2030, growing at a 6.2% CAGR from 2025. Investment momentum is also expected to shift toward mobile networks from 2028 onward, as tier 1 markets prepare for 6G deployments. Telecoms capex is forecast to reach $395 billion by 2030, with a 3.6% CAGR, while technology capex will surge to $545 billion, reflecting a 9.3% CAGR.
Fixed telecom capex will gradually decline due to market saturation. Meanwhile, AI infrastructure, cloud services, and digital sovereignty policies are driving telecom operators to expand data centers and invest in specialized hardware.
Key market trends:
- CP capex per person will increase from $74 in 2024 to $116 in 2030, with CP capex reaching 2.5% of global GDP investment.
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Capital intensity in telecom will decline until 2027, then rise due to mobile network upgrades.
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Regional leaders in revenue and capex include North America, Oceania & Eastern Asia, and Western Europe, with Central & Southern Asia showing the highest growth potential.
Dario Talmesio, research director at Omdia said, “telecom operators are entering a new phase of strategic investment. With 6G on the horizon and AI infrastructure demands accelerating, the connectivity business is shifting from volume-based pricing to value-driven connectivity.”
Omdia’s forecast is based on a comprehensive model incorporating historical data from 67 countries, local market dynamics, regulatory trends, and technology migration patterns.
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Separately, Dell’Oro Group sees 6G capex ramping around 2030, although it warns that the RAN market remains flat, “raising key questions for the industry’s future.” Cumulative 6G RAN investments over the 2029-2034 period are projected to account for 55% to 60% of the total RAN capex over the same forecast period.
“Our long-term position and characterization of this market have not changed,” said Stefan Pongratz, Vice President of RAN and Telecom Capex research at Dell’Oro Group. “The RAN network plays a pivotal role in the broader telecom market. There are opportunities to expand the RAN beyond the traditional MBB (mobile broadband) use cases. At the same time, there are serious near-term risks tilted to the downside, particularly when considering the slowdown in data traffic,” continued Pongratz.
Additional highlights from Dell’Oro’s October 2025 6G Advanced Research Report:
- The baseline scenario is for the broader RAN market to stay flat over the next 10 years. This is built on the assumption that the mobile network will run into utilization challenges by the end of the decade, spurring a 6G capex ramp dominated by Massive MIMO systems in the Sub-7GHz/cm Wave spectrum, utilizing the existing macro grid as much as possible.
- The report also outlines more optimistic and pessimistic growth scenarios, depending largely on the mobile data traffic growth trajectory and the impact beyond MBB, including private wireless and FWA (fixed wireless access).
- Cumulative 6G RAN investments over the 2029-2034 period are projected to account for 55 to 60 percent of the total RAN capex over the same forecast period.
Dell’Oro Group’s 6G Advanced Research Report offers an overview of the RAN market by technology, with tables covering manufacturers’ revenue for total RAN over the next 10 years. 6G RAN is analyzed by spectrum (Sub-7 GHz, cmWave, mmWave), by Massive MIMO, and by region (North America, Europe, Middle East and Africa, China, Asia Pacific Excl. China, and CALA). To purchase this report, please contact by email at [email protected].
References:
https://www.lightreading.com/6g/6g-momentum-is-building
6G Capex Ramp to Start Around 2030, According to Dell’Oro Group
https://www.lightreading.com/6g/6g-course-correction-vendors-hear-mno-pleas
https://www.lightreading.com/6g/what-at-t-really-wants-from-6g
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
This is not only astonishing but unheard of: the world’s largest and most popular fabless semiconductor company –Nvidia– taking a $1 billion stake in a telco/reinvented data center connectivity company-Nokia.
Indeed, GPU king Nvidia will pay $1 billion for a stake of 2.9% in Nokia as part of a deal focused on AI and data centers, the Finnish telecom equipment maker said on Tuesday as its shares hit their highest level in nearly a decade on hope for AI to lift their business revenue and profits. The nonexclusive partnership and the investment will make Nvidia the second-largest shareholder in Nokia. Nokia said it will issue 166,389,351 new shares for Nvidia, which the U.S. company will subscribe to at $6.01 per share.
Nokia said the companies will collaborate on artificial intelligence networking solutions and explore opportunities to include its data center communications products in Nvidia’s future AI infrastructure plans. Nokia and its Swedish rival Ericsson both make networking equipment for connectivity inside (intra-) data centers and between (inter-) data centers and have been benefiting from increased AI use.
Summary:
- NVIDIA and Nokia to establish a strategic partnership to enable accelerated development and deployment of next generation AI native mobile networks and AI networking infrastructure.
- NVIDIA introduces NVIDIA Arc Aerial RAN Computer, a 6G-ready telecommunications computing platform.
- Nokia to expand its global access portfolio with new AI-RAN product based on NVIDIA platform.
- T-Mobile U.S. is working with Nokia and NVIDIA to integrate AI-RAN technologies into its 6G development process.
- Collaboration enables new AI services and improved consumer experiences to support explosive growth in mobile AI traffic.
- Dell Technologies provides PowerEdge servers to power new AI-RAN solution.
- Partnership marks turning point for the industry, paving the way to AI-native 6G by taking AI-RAN to innovation and commercialization at a global scale.
In some respects, this new partnership competes with Nvidia’s own data center connectivity solutions from its Mellanox Technologies division, which it acquired for $6.9 billion in 2019. Meanwhile, Nokia now claims to have worked on a redesign to ensure its RAN software is compatible with Nvidia’s compute unified device architecture (CUDA) platform, meaning it can run on Nvidia’s GPUs. Nvidia has also modified its hardware offer, creating capacity cards that will slot directly into Nokia’s existing AirScale baseband units at mobile sites.
Having dethroned Intel several years ago, Nvidia now has a near-monopoly in supplying GPU chips for data centers and has partnered with companies ranging from OpenAI to Microsoft. AMD is a distant second but is gaining ground in the data center GPU space as is ARM Ltd with its RISC CPU cores. Capital expenditure on data center infrastructure is expected to exceed $1.7 trillion by 2030, consulting firm McKinsey, largely because of the expansion of AI.
Nvidia CEO Jensen Huang said the deal would help make the U.S. the center of the next revolution in 6G. “Thank you for helping the United States bring telecommunication technology back to America,” Huang said in a speech in Washington, addressing Nokia CEO Justin Hotard (x-Intel). “The key thing here is it’s American technology delivering the base capability, which is the accelerated computing stack from Nvidia, now purpose-built for mobile,” Hotard told Reuters in an interview. “Jensen and I have been talking for a little bit and I love the pace at which Nvidia moves,” Hotard said. “It’s a pace that I aspire for us to move at Nokia.” He expects the new equipment to start contributing to revenue from 2027 as it goes into commercial deployment, first with 5G, followed by 6G after 2030.
Nvidia has been on a spending spree in recent weeks. The company in September pledged to invest $5 billion in beleaguered chip maker Intel. The investment pairs the world’s most valuable company, which has been a darling of the AI boom, with a chip maker that has almost completely fallen out of the AI conversation.
Later that month, Nvidia said it planned to invest up to $100 billion in OpenAI over an unspecified period that will likely span at least a few years. The partnership includes plans for an enormous data-center build-out and will allow OpenAI to build and deploy at least 10 gigawatts of Nvidia systems.
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Tech Details:
Nokia uses Marvell Physical Layer (1) baseband chips for many of its products. Among other things, this ensured Nokia had a single software stack for all its virtual and purpose-built RAN products. Pallavi Mahajan, Nokia’s recently joined chief technology and AI officer recently told Light Reading that their software could easily adapt to run on Nvidia’s GPUs: “We built a hardware abstraction layer so that whether you are on Marvell, whether you are on any of the x86 servers or whether you are on GPUs, the abstraction takes away from that complexity, and the software is still the same.”
Fully independent software has been something of a Holy Grail for the entire industry. It would have ramifications for the whole market and its economics. Yet Nokia has conceivably been able to minimize the effort required to put its Layer 1 and specific higher-layer functions on a GPU. “There are going to be pieces of the software that are going to leverage on the accelerated compute,” said Mahajan. “That’s where we will bring in the CUDA integration pieces. But it’s not the entire software,” she added. The appeal of Nvidia as an alternative was largely to be found in “the programmability pieces that you don’t have on any other general merchant silicon,” said Mahajan. “There’s also this entire ecosystem, the CUDA ecosystem, that comes in.” One of Nvidia’s most eye-catching recent moves is the decision to “open source” Aerial, its own CUDA-based RAN software framework, so that other developers can tinker, she says. “What it now enables is the entire ecosystem to go out and contribute.”
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Quotes:
“Telecommunications is a critical national infrastructure — the digital nervous system of our economy and security,” said Jensen Huang, founder and CEO of NVIDIA. “Built on NVIDIA CUDA and AI, AI-RAN will revolutionize telecommunications — a generational platform shift that empowers the United States to regain global leadership in this vital infrastructure technology. Together with Nokia and America’s telecom ecosystem, we’re igniting this revolution, equipping operators to build intelligent, adaptive networks that will define the next generation of global connectivity.”
“The next leap in telecom isn’t just from 5G to 6G — it’s a fundamental redesign of the network to deliver AI-powered connectivity, capable of processing intelligence from the data center all the way to the edge. Our partnership with NVIDIA, and their investment in Nokia, will accelerate AI-RAN innovation to put an AI data center into everyone’s pocket,” said Justin Hotard, President and CEO of Nokia. “We’re proud to drive this industry transformation with NVIDIA, Dell Technologies, and T-Mobile U.S., our first AI-RAN deployments in T-Mobile’s network will ensure America leads in the advanced connectivity that AI needs.”
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Editor’s Notes:
1. In more advanced 5G networks, Physical Layer functions have demanded the support of custom silicon, or “accelerators.” A technique known as “lookaside,” favored by Ericsson and Samsung, uses an accelerator for only a single problematic Layer 1 task – forward error correction – and keeps everything else on the CPU. Nokia prefers the “inline” approach, which puts the whole of Layer 1 on the accelerator.
2. The huge AI-RAN push that Nvidia started with the formation of the AI-RAN Alliance in early 2024 has not met with an enthusiastic telco response so far. Results from Nokia as well as Ericsson show wireless network operators are spending less on 5G rollouts than they were in the early 2020s. Telco numbers indicate the appetite for smartphone and other mobile data services has not produced any sales growth. As companies prioritize efficiency above all else, baseband units that include Marvell and Nvidia cards may seem too expensive.
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Other Opinions and Quotes:
Nvidia chips are likely to be more expensive, said Mads Rosendal, analyst at Danske Bank Credit Research, but the proposed partnership would be mutually beneficial, given Nvidia’s large share in the U.S. data center market.
“This is a strong endorsement of Nokia’s capabilities,” said PP Foresight analyst Paolo Pescatore. “Next-generation networks, such as 6G, will play a significant role in enabling new AI-powered experiences,” he added.
Iain Morris, International Editor, Light Reading: “Layer 1 control software runs on ARM RISC CPU cores in both Marvell and Nvidia technologies. The bigger differences tend to be in the hardware acceleration “kernels,” or central components, which have some unique demands. Yet Nokia has been working to put as much as it possibly can into a bucket of common software. Regardless, if Nvidia is effectively paying for all this with its $1 billion investment, the risks for Nokia may be small………….Nokia’s customers will in future have an AI-RAN choice that limits or even shrinks the floorspace for Marvell. The development also points to more subtle changes in Nokia’s thinking. The message earlier this year was that Nokia did not require a GPU to implement AI for RAN, whereby machine-generated algorithms help to improve network performance and efficiency. Marvell had that covered because it had incorporated AI and machine-learning technologies into the baseband chips used by Nokia.”
“If you start doing inline, you typically get much more locked into the hardware,” said Per Narvinger, the president of Ericsson’s mobile networks business group, on a recent analyst call. During its own trials with Nvidia, Ericsson said it was effectively able to redeploy virtual RAN software written for Intel’s x86 CPUs on the Grace CPU with minimal changes, leaving the GPU only as a possible option for the FEC accelerator. Putting the entire Layer 1 on a GPU would mean “you probably also get more tightly into that specific implementation,” said Narvinger. “Where does it really benefit from having that kind of parallel compute system?”
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Separately, Nokia and Nvidia will partner with T-Mobile U.S. to develop and test AI RAN technologies for developing 6G, Nokia said in its press release. Trials are expected to begin in 2026, focused on field validation of performance and efficiency gains for customers.
References:
https://nvidianews.nvidia.com/news/nvidia-nokia-ai-telecommunications
https://www.reuters.com/world/europe/nvidia-make-1-billion-investment-finlands-nokia-2025-10-28/
https://www.lightreading.com/5g/nvidia-takes-1b-stake-in-nokia-which-promises-5g-and-6g-overhaul
https://www.wsj.com/business/telecom/nvidia-takes-1-billion-stake-in-nokia-69f75bb6
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Nvidia’s networking solutions give it an edge over competitive AI chip makers
Telecom sessions at Nvidia’s 2025 AI developers GTC: March 17–21 in San Jose, CA
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
FT: Nvidia invested $1bn in AI start-ups in 2024
The case for and against AI-RAN technology using Nvidia or AMD GPUs
Highlights of Nokia’s Smart Factory in Oulu, Finland for 5G and 6G innovation
Nokia & Deutsche Bahn deploy world’s first 1900 MHz 5G radio network meeting FRMCS requirements
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Indosat Ooredoo Hutchison and Nokia use AI to reduce energy demand and emissions
Verizon partners with Nokia to deploy large private 5G network in the UK
Omdia: How telcos will evolve in the AI era
Dario Talmesio, research director, service provider, strategy and regulation at market research firm Omdia (owned by Informa) sees positive signs for network operators.
“After many years of plumbing, now telecom operators are starting to see some of the benefits of their network and beyond network strategies. Furthermore, the investor community is now appreciating telecom investments, after many years of poor valuation, he said during his analyst keynote presentation at Network X, a conference organized by Light Reading and Informa in Paris, France last week.
“What has changed in the telecoms industry over the past few years is the fact that we are no longer in a market that is in contraction,” he said. Although telcos are generally not seeing double-digit percentage increases in revenue or profit, “it’s a reliable business … a business that is able to provide cash to investors.”
Omdia forecasts that global telecoms revenue will have a CAGR of 2.8% in the 2025-2030 timeframe. In addition, the industry has delivered two consecutive years of record free cash flow, above 17% of sales.
However, Omdia found that telcos have reduced capex, which is trending towards 15% of revenues. Opex fell by -0.2% in 2024 and is broadly flatlining. There was a 2.2% decline in global labor opex following the challenging trend in 2023, when labor opex increased by 4% despite notable layoffs.
“Overall, the positive momentum is continuing, but of course there is more work to be done on the efficiency side,” Talmesio said. He added that it is also still too early to say what impact AI investments will have over the longer term. “All the work that has been done so far is still largely preparatory, with visible results expected to materialize in the near(ish) future,” he added. His Network X keynote presentation addressed the following questions:
- How will telcos evolve their operating structures and shift their business focuses in the next 5 years?
- AI, cloud and more to supercharge efficiencies and operating models?
- How will big tech co-opetition evolve and impact traditional telcos?
Customer care was seen as the area first impacted by AI, building on existing GenAI implementations. In contrast, network operations are expected to ultimately see the most significant impact of agentic AI.
Talmesio said many of the building blocks are in place for telecoms services and future revenue generation, with several markets reaching 60% to 70% fiber coverage, and some even approaching 100%.
Network operators are now moving beyond monetizing pure data access and are able to charge more for different gigabit speeds, home gaming, more intelligent home routers and additional WiFi access points, smart home services such as energy, security and multi-room video, and more.
While noting that connectivity remains the most important revenue driver, when contributions from various telecoms-adjacent services are added up “it becomes a significant number,” Talmesio said.
Mobile networks are another important building block. While acknowledging that 5G has been something of a disappointment in the first five years of the deployment cycle, “this is really changing” as more operators deploy 5G standalone (5G SA core) networks, Omdia observed.
Talmesio said: “At the end of June, there were only 66 telecom operators launching or commercially using 5G SA. But those 66 operators are those operators that carry the majority of the world’s 5G subscribers. And with 5G SA, we have improved latency and more devices among other factors. Monetization is still in its infancy, perhaps, but then you can see some really positive progress in 5G Advanced, where as of June, we had 13 commercial networks available with some good monetization examples, including uplink.”
“Telecom is moving beyond telecoms,” with a number of new AI strategies in place. For example, telcos are increasingly providing AI infrastructure in their data centers, offering GPU as-a-service, AI-related colocation, AI-RAN and edge AI functionality.
Dario Talmesio, Omdia
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AI is also being used for network management, with AI productivity tools and AI digital assistants, as well as AI software services including GenAI products and services for consumer, enterprises and vertical markets.
“There is an additional boost for telecom operators to move beyond connectivity, which is the sovereignty agenda,” Talmesio noted. While sovereignty in the past was largely applied to data residency, “in reality, there are more and more aspects of sovereignty that are in many ways facilitating telecom operators in retaining or entering business areas that probably ten years ago were unthinkable for them.” These include cloud and data center infrastructure, sovereign AI, cyberdefense and quantum safety, satellite communication, data protection and critical communications.
“The telecom business is definitely improving,” Talmesio concluded, noting that the market is now also being viewed more favorably by investors. “In many ways, the glass is maybe still half full, but there’s more water being poured into the telecom industry.”
References:
https://networkxevent.com/speakers/dario-talmesio/
https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/pushing-telcos-ai-envelope-on-capital-decisions
Omdia on resurgence of Huawei: #1 RAN vendor in 3 out of 5 regions; RAN market has bottomed
Omdia: Huawei increases global RAN market share due to China hegemony
Dell’Oro & Omdia: Global RAN market declined in 2023 and again in 2024
Omdia: Cable network operators deploy PONs
Qualcomm CEO: expect “pre-commercial” 6G devices by 2028
During his keynote speech at the 2025 Snapdragon Summit in Maui, Qualcomm CEO Cristiano Amon said:
“We have been very busy working on the next generation of connectivity…which is 6G. Designed to be the connection between the cloud and Edge devices, The difference between 5G and 6G, besides increasing the speeds, increasing broadband, increasing the amount of data, it’s also a network that has intelligence to have perception and sensor data. We’re going to have completely new use cases for this network of intelligence — connecting the edge and the cloud.”
“We have been working on this (6G) for a while, and it’s sooner than you think. We are ready to have pre-commercial devices with 6G as early as 2028. And when we get that, we’re going to have context aware intelligence at scale.”
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Analysis: Let’s examine that statement, in light of the ITU-R IMT 2030 recommendations not scheduled to be completed until the end of 2030:
“pre-commercial devices” are not meant for general consumers while “as early as” leaves open the possibility that those 6G devices might not be available until after 2028.
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Looking ahead at the future of devices, Amon noted that 6G would play a key role in the evolution of AI technology, with AI models becoming hybrid. This includes a combination of cloud and edge devices (like user interfaces, sensors, etc). According to Qualcomm, 6G will make this happen. Anon envisions a future where AI agents are a crucial part of our daily lives, upending the way we currently use our connected devices. He firmly believes that smartphones, laptops, cars, smart glasses, earbuds, and more will have a direct line of communication with these AI agents — facilitated by 6G connectivity.
Opinion: This sounds very much like the hype around 5G ushering a whole new set of ultra-low latency applications which never happened (because the 3GPP specs for URLLC were not completed in June 2020 when Release 16 was frozen). Also, very few mobile operators deployed 5G SA core, without which there are no 5G features, like network slicing and security.
Separately, Nokia Bell Labs has said that in the coming 6G era, “new man-machine interfaces” controlled by voice and gesture input will gradually replace more traditional inputs, like typing on touchscreens. That’s easy to read as conjecture, but we’ll have to see if that really happens when the first commercial 5G networks are deployed in late 2030- early 2031.
We’re sure to see faster network speeds with higher amounts of data with 6G with AI in more devices, but standardized 6G is still at least five years from being a commercial reality.
References:
https://www.androidauthority.com/qualcomm-6g-2028-3600781/
https://www.nokia.com/6g/6g-explained/
ITU-R WP5D IMT 2030 Submission & Evaluation Guidelines vs 6G specs in 3GPP Release 20 & 21
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
ITU-R: IMT-2030 (6G) Backgrounder and Envisioned Capabilities
Ericsson and e& (UAE) sign MoU for 6G collaboration vs ITU-R IMT-2030 framework
Highlights of 3GPP Stage 1 Workshop on IMT 2030 (6G) Use Cases
6th Digital China Summit: China to expand its 5G network; 6G R&D via the IMT-2030 (6G) Promotion Group
MediaTek overtakes Qualcomm in 5G smartphone chip market
SoftBank’s Transformer AI model boosts 5G AI-RAN uplink throughput by 30%, compared to a baseline model without AI
Softbank has developed its own Transformer-based AI model that can be used for wireless signal processing. SoftBank used its Transformer model to improve uplink channel interpolation which is a signal processing technique where the network essentially makes an educated guess as to the characteristics and current state of a signal’s channel. Enabling this type of intelligence in a network contributes to faster, more stable communication, according to SoftBank. The Japanese wireless network operator successfully increased uplink throughput by approximately 20% compared to a conventional signal processing method (the baseline method). In the latest demonstration, the new Transformer-based architecture was run on GPUs and tested in a live Over-the-Air (OTA) wireless environment. In addition to confirming real-time operation, the results showed further throughput gains and achieved ultra-low latency.
Editor’s note: A Transformer model is a type of neural network architecture that emerged in 2017. It excels at interpreting streams of sequential data associated with large language models (LLMs). Transformer models have also achieved elite performance in other fields of artificial intelligence (AI), including computer vision, speech recognition and time series forecasting. Transformer models are lightweight, efficient, and versatile – capable of natural language processing (NLP), image recognition and wireless signal processing as per this Softbank demo.
Significant throughput improvement:
- Uplink channel interpolation using the new architecture improved uplink throughput by approximately 8% compared to the conventional CNN model. Compared to the baseline method without AI, this represents an approximately 30% increase in throughput, proving that the continuous evolution of AI models leads to enhanced communication quality in real-world environments.
Higher AI performance with ultra-low latency:
- While real-time 5G communication requires processing in under 1 millisecond, this demonstration with the Transformer achieved an average processing time of approximately 338 microseconds, an ultra-low latency that is about 26% faster than the convolution neural network (CNN) [1.] based approach. Generally, AI model processing speeds decrease as performance increases. This achievement overcomes the technically difficult challenge of simultaneously achieving higher AI performance and lower latency. Editor’s note: Perhaps this can overcome the performance limitations in ITU-R M.2150 for URRLC in the RAN, which is based on an uncompleted 3GPP Release 16 specification.
Note 1. CNN-based approaches to achieving low latency focus on optimizing model architecture, computation, and hardware to accelerate inference, especially in real-time applications. Rather than relying on a single technique, the best results are often achieved through a combination of methods.
Using the new architecture, SoftBank conducted a simulation of “Sounding Reference Signal (SRS) prediction,” a process required for base stations to assign optimal radio waves (beams) to terminals. Previous research using a simpler Multilayer Perceptron (MLP) AI model for SRS prediction confirmed a maximum downlink throughput improvement of about 13% for a terminal moving at 80 km/h.*2
In the new simulation with the Transformer-based architecture, the downlink throughput for a terminal moving at 80 km/h improved by up to approximately 29%, and by up to approximately 31% for a terminal moving at 40 km/h. This confirms that enhancing the AI model more than doubled the throughput improvement rate (see Figure 1). This is a crucial achievement that will lead to a dramatic improvement in communication speeds, directly impacting the user experience.
The most significant technical challenge for the practical application of “AI for RAN” is to further improve communication quality using high-performance AI models while operating under the real-time processing constraint of less than one millisecond. SoftBank addressed this by developing a lightweight and highly efficient Transformer-based architecture that focuses only on essential processes, achieving both low latency and maximum AI performance. The important features are:
(1) Grasps overall wireless signal correlations
By leveraging the “Self-Attention” mechanism, a key feature of Transformers, the architecture can grasp wide-ranging correlations in wireless signals across frequency and time (e.g., complex signal patterns caused by radio wave reflection and interference). This allows it to maintain high AI performance while remaining lightweight. Convolution focuses on a part of the input, while Self-Attention captures the relationships of the entire input (see Figure 2).
(2) Preserves physical information of wireless signals
While it is common to normalize input data to stabilize learning in AI models, the architecture features a proprietary design that uses the raw amplitude of wireless signals without normalization. This ensures that crucial physical information indicating communication quality is not lost, significantly improving the performance of tasks like channel estimation.
(3) Versatility for various tasks
The architecture has a versatile, unified design. By making only minor changes to its output layer, it can be adapted to handle a variety of different tasks, including channel interpolation/estimation, SRS prediction, and signal demodulation. This reduces the time and cost associated with developing separate AI models for each task.
The demonstration results show that high-performance AI models like Transformer and the GPUs that run them are indispensable for achieving the high communication performance required in the 5G-Advanced and 6G eras. Furthermore, an AI-RAN that controls the RAN on GPUs allows for continuous performance upgrades through software updates as more advanced AI models emerge, even after the hardware has been deployed. This will enable telecommunication carriers to improve the efficiency of their capital expenditures and maximize value.
Moving forward, SoftBank will accelerate the commercialization of the technologies validated in this demonstration. By further improving communication quality and advancing networks with AI-RAN, SoftBank will contribute to innovation in future communication infrastructure. The Japan based conglomerate strongly endorsed AI RAN at MWC 2025.
References:
https://www.softbank.jp/en/corp/news/press/sbkk/2025/20250821_02/
https://www.telecoms.com/5g-6g/softbank-claims-its-ai-ran-tech-boosts-throughput-by-30-
https://www.telecoms.com/ai/softbank-makes-mwc-25-all-about-ai-ran
https://www.ibm.com/think/topics/transformer-model
https://www.itu.int/rec/R-REC-M.2150/en
Softbank developing autonomous AI agents; an AI model that can predict and capture human cognition
Dell’Oro Group: RAN Market Grows Outside of China in 2Q 2025
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
OpenAI announces new open weight, open source GPT models which Orange will deploy
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Ericsson reports ~flat 2Q-2025 results; sees potential for 5G SA and AI to drive growth
Ericsson’s second-quarter results were not impressive, with YoY organic sales growth of +2% for the company and +3% for its network division (its largest). Its $14 billion AT&T OpenRAN deal, announced in December of 2023, helped lift Swedish vendor’s share of the global RAN market by +1.4 percentage points in 2024 to 25.7%, according to new research from analyst company Omdia (owned by Informa). As a result of its AT&T contract, the U.S. accounted for a stunning 44% of Ericsson’s second-quarter sales while the North American market resulted in a 10% YoY increase in organic revenues to SEK19.8bn ($2.05bn). Sales dropped in all other regions of the world! The charts below depict that very well:
Ericsson’s attention is now shifting to a few core markets that Ekholm has identified as strategic priorities, among them the U.S., India, Japan and the UK. All, unsurprisingly, already make up Ericsson’s top five countries by sales, although their contribution minus the US came to just 15% of turnover for the recent second quarter. “We are already very strong in North America, but we can do more in India and Japan,” said Ekholm. “We see those as critically important for the long-term success.”
Opportunities: As telco investment in RAN equipment has declined by 12.5% (or $5 billion) last year, the Swedish equipment vendor has had few other obvious growth opportunities. Ericsson’s Enterprise division, which is supposed to be the long-term provider of sales growth for Ericsson, is still very small – its second-quarter revenues stood at just SEK5.5bn ($570m) and even once currency exchange changes are taken into account, its sales shrank by 6% YoY.
On Tuesday’s earnings call, Ericsson CEO Börje Ekholm said that the RAN equipment sector, while stable currently, isn’t offering any prospects of exciting near-term growth. For longer-term growth the industry needs “new monetization opportunities” and those could come from the ongoing modest growth in 5G-enabled fixed wireless access (FWA) deployments, from 5G standalone (SA) deployments that enable mobile network operators to offer “differentiated solutions” and from network APIs (that ultra hyped market is not generating meaningful revenues for anyone yet).
Cost Cutting Continues: Ericsson also has continued to be aggressive about cost reduction, eliminating thousands of jobs since it completed its Vonage takeover. “Over the last year, we have reduced our total number of employees by about 6% or 6,000,” said Ekholm on his routine call with analysts about financial results. “We also see and expect big benefits from the use of AI and that is one reason why we expect restructuring costs to remain elevated during the year.”
Use of AI: Ericsson sees AI as an opportunity to enable network automation and new industry revenue opportunities. The company is now using AI as an aid in network design – a move that could have negative ramifications for staff involved in research and development. Ericsson is already using AI for coding and “other parts of internal operations to drive efficiency… We see some benefits now. And it’s going to impact how the network is operated – think of fully autonomous, intent-based networks that will require AI as a fundamental component. That’s one of the reasons why we invested in an AI factory,” noted the CEO, referencing the consortium-based investment in a Swedish AI Factory that was announced in late May. At the time, Ericsson noted that it planned to “leverage its data science expertise to develop and deploy state-of-the-art AI models – improving performance and efficiency and enhancing customer experience.
Ericsson is also building AI capability into the products sold to customers. “I usually use the example of link adaptation,” said Per Narvinger, the head of Ericsson’s mobile networks business group, on a call with Light Reading, referring to what he says is probably one of the most optimized algorithms in telecom. “That’s how much you get out of the spectrum, and when we have rewritten link adaptation, and used AI functionality on an AI model, we see we can get a gain of 10%.”
Ericsson hopes that AI will boost consumer and business demand for 5G connectivity. New form factors such as smart glasses and AR headsets will need lower-latency connections with improved support for the uplink, it has repeatedly argued. But analysts are skeptical, while Ericsson thinks Europe is ill equipped for more advanced 5G services.
“We’re still very early in AI, in [understanding] how applications are going to start running, but I think it’s going to be a key driver of our business going forward, both on traffic, on the way we operate networks, and the way we run Ericsson,” Ekholm said.
Europe Disappoints: In much of Europe, Ericsson and Nokia have been frustrated by some government and telco unwillingness to adopt the European Union’s “5G toolbox” recommendations and evict Chinese vendors. “I think what we have seen in terms of implementation is quite varied, to be honest,” said Narvinger. Rather than banning Huawei outright, Germany’s government has introduced legislation that allows operators to use most of its RAN products if they find a substitute for part of Huawei’s management system by 2029. Opponents have criticized that move, arguing it does not address the security threat posed by Huawei’s RAN software. Nevertheless, Ericsson clearly eyes an opportunity to serve European demand for military communications, an area where the use of Chinese vendors would be unthinkable.
“It is realistic to say that a large part of the increased defense spending in Europe will most likely be allocated to connectivity because that is a critical part of a modern defense force,” said Ekholm. “I think this is a very good opportunity for western vendors because it would be far-fetched to think they will go with high-risk vendors.” Ericsson is also targeting related demand for mission-critical services needed by first responders.
5G SA and Mobile Core Networks: Ekholm noted that 5G SA deployments are still few and far between – only a quarter of mobile operators have any kind of 5G SA deployment in place right now, with the most notable being in the US, India and China. “Two things need to happen,” for greater 5G SA uptake, stated the CEO.
- “One is mid-band [spectrum] coverage… there’s still very low build out coverage in, for example, Europe, where it’s probably less than half the population covered… Europe is clearly behind on that“ compared with the U.S., China and India.
- “The second is that [network operators] need to upgrade their mobile core [platforms]... Those two things will have to happen to take full advantage of the capabilities of the [5G] network,” noted Ekholm, who said the arrival of new devices, such as AI glasses, that require ultra low latency connections and “very high uplink performance” is starting to drive interest. “We’re also seeing a lot of network slicing opportunities,” he added, to deliver dedicated network resources to, for example, police forces, sports and entertainment stadiums “to guarantee uplink streams… consumers are willing to pay for these things. So I’m rather encouraged by the service innovation that’s starting to happen on 5G SA and… that’s going to drive the need for more radio coverage [for] mid-band and for core [systems].”
Ericsson’s Summary -Looking Ahead:
- Continue to strengthen competitive position
- Strong customer engagement for differentiated connectivity
- New use cases to monetize network investments taking shape
- Expect RAN market to remain broadly stable
- Structurally improving the business through rigorous cost management
- Continue to invest in technology leadership
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References:
https://www.telecomtv.com/content/5g/ericsson-ceo-waxes-lyrical-on-potential-of-5g-sa-ai-53441/
https://www.lightreading.com/5g/ericsson-targets-big-huawei-free-places-ai-and-nato-as-profits-soar
Ericsson revamps its OSS/BSS with AI using Amazon Bedrock as a foundation
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
AI RAN [1.] is projected to account for approximately a third of the RAN market by 2029, according to a recent AI RAN Advanced Research Report published by the Dell’Oro Group. In the near term, the focus within the AI RAN segment will center on Distributed-RAN (D-RAN), single-purpose deployments, and 5G.
“Near-term priorities are more about efficiency gains than new revenue streams,” said Stefan Pongratz, Vice President at Dell’Oro Group. “There is strong consensus that AI RAN can improve the user experience, enhance performance, reduce power consumption, and play a critical role in the broader automation journey. Unsurprisingly, however, there is greater skepticism about AI’s ability to reverse the flat revenue trajectory that has defined operators throughout the 4G and 5G cycles,” continued Pongratz.
Note 1. AI RAN integrates AI and machine learning (ML) across various aspects of the RAN domain. The AI RAN scope in this report is aligned with the greater industry vision. While the broader AI RAN vision includes services and infrastructure, the projections in this report focus on the RAN equipment market.
Additional highlights from the July 2025 AI RAN Advanced Research Report:
- The base case is built on the assumption that AI RAN is not a growth vehicle. But it is a crucial technology/tool for operators to adopt. Over time, operators will incorporate more virtualization, intelligence, automation, and O-RAN into their RAN roadmaps.
- This initial AI RAN report forecasts the AI RAN market based on location, tenancy, technology, and region.
- The existing RAN radio and baseband suppliers are well-positioned in the initial AI-RAN phase, driven primarily by AI-for-RAN upgrades leveraging the existing hardware. Per Dell’Oro Group’s regular RAN coverage, the top 5 RAN suppliers contributed around 95 percent of the 2024 RAN revenue.
- AI RAN is projected to account for around a third of total RAN revenue by 2029.
In the first quarter of 2025, Dell’Oro said the top five RAN suppliers based on revenues outside of China are Ericsson, Nokia, Huawei, Samsung and ZTE. In terms of worldwide revenue, the ranking changes to Huawei, Ericsson, Nokia, ZTE and Samsung.
About the Report: Dell’Oro Group’s AI RAN Advanced Research Report includes a 5-year forecast for AI RAN by location, tenancy, technology, and region. Contact: [email protected]
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Author’s Note: Nvidia’s Aerial Research portfolio already contains a host of AI-powered tools designed to augment wireless network simulations. It is also collaborating with T-Mobile and Cisco to develop AI RAN solutions to support future 6G applications. The GPU king is also working with some of those top five RAN suppliers, Nokia and Ericsson, on an AI-RAN Innovation Center. Unveiled last October, the project aims to bring together cloud-based RAN and AI development and push beyond applications that focus solely on improving efficiencies.
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The one year old AI RAN Alliance has now increased its membership to over 100, up from around 84 in May. However, there are not many telco members with only Vodafone joining since May. The other telco members are: Turkcell ,Boost Mobile, Globe, Indosat Ooredoo Hutchison (Indonesia), Korea Telecom, LG UPlus, SK Telecom, T-Mobile US and Softbank. This limited telco presence could reflect the ongoing skepticism about the goals of AI-RAN, including hopes for new revenue opportunities through network slicing, as well as hosting and monetizing enterprise AI workloads at the edge.
Francisco Martín Pignatelli, head of open RAN at Vodafone, hardly sounded enthusiastic in his statement in the AI-RAN Alliance press release. “Vodafone is committed to using AI to optimize and enhance the performance of our radio access networks. Running AI and RAN workloads on shared infrastructure boosts efficiency, while integrating AI and generative applications over RAN enables new real-time capabilities at the network edge,” he added.
Perhaps, the most popular AI RAN scenario is “AI on RAN,” which enables AI services on the RAN at the network edge in a bid to support and benefit from new services, such as AI inferencing.
“We are thrilled by the extraordinary growth of the AI-RAN Alliance,” said Alex Jinsung Choi, Chair of the AI-RAN Alliance and Principal Fellow at SoftBank Corp.’s Research Institute of Advanced Technology. “This milestone underscores the global momentum behind advancing AI for RAN, AI and RAN, and AI on RAN. Our members are pioneering how artificial intelligence can be deeply embedded into radio access networks — from foundational research to real-world deployment — to create intelligent, adaptive, and efficient wireless systems.”
Choi recently suggested that now is the time to “revisit all our value propositions and then think about what should be changed or what should be built” to be able to address issues including market saturation and the “decoupling” between revenue growth and rising TCO. He also cited self-driving vehicles and mobile robots, where low latency is critical, as specific use cases where AI-RAN will be useful for running enterprise workloads.
About the AI-RAN Alliance:
The AI-RAN Alliance is a global consortium accelerating the integration of artificial intelligence into Radio Access Networks. Established in 2024, the Alliance unites leading companies, researchers, and technologists to advance open, practical approaches for building AI-native wireless networks. The Alliance focuses on enabling experimentation, sharing knowledge, and real-world performance to support the next generation of mobile infrastructure. For more information, visit: https://ai-ran.org
References:
https://www.delloro.com/advanced-research-report/ai-ran/
https://www.delloro.com/news/ai-ran-to-top-10-billion-by-2029/
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
AI RAN Alliance selects Alex Choi as Chairman
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
The case for and against AI-RAN technology using Nvidia or AMD GPUs
Deloitte and TM Forum : How AI could revitalize the ailing telecom industry?
IEEE Techblog readers are well aware of the dire state of the global telecommunications industry. In particular:
- According to Deloitte, the global telecommunications industry is expected to have revenues of about US$1.53 trillion in 2024, up about 3% over the prior year.Both in 2024 and out to 2028, growth is expected to be higher in Asia Pacific and Europe, Middle East, and Africa, with growth in the Americas being around 1% annually.
- Telco sales were less than $1.8 trillion in 2022 vs. $1.9 trillion in 2012, according to Light Reading. Collective investments of about $1 trillion over a five-year period had brought a lousy return of less than 1%.
- Last year (2024), spending on radio access network infrastructure fell by $5 billion, more than 12% of the total, according to analyst firm Omdia, imperilling the kit vendors on which telcos rely.
Deloitte believes generative (gen) AI will have a huge impact on telecom network providers:
Telcos are using gen AI to reduce costs, become more efficient, and offer new services. Some are building new gen AI data centers to sell training and inference to others. What role does connectivity play in these data centers?
There is a gen AI gold rush expected over the next five years. Spending estimates range from hundreds of billions to over a trillion dollars on the physical layer required for gen AI: chips, data centers, and electricity.16 Close to another hundred billion US dollars will likely be spent on the software and services layer.17 Telcos should focus on the opportunity to participate by connecting all of those different pieces of hardware and software. And shouldn’t telcos, whose business is all about connectivity, be able to profit in some way?
There are gen AI markets for connectivity: Inside the data centers there are miles of mainly copper (and some fiber) cables for transmitting data from board to board and rack to rack. Serving this market is worth billions in 2025,18 but much of this connectivity is provided by data centers and chipmakers and have never been provided by telcos.
There are also massive, long-haul fiber networks ranging from tens to thousands of miles long. These connect (for example) a hyperscaler’s data centers across a region or continent, or even stretch along the seabed, connecting data centers across continents. Sometimes these new fiber networks are being built to support sovereign AI—that is, the need to keep all the AI data inside a given country or region.
Historically, those fiber networks were massive expenditures, built by only the largest telcos or (in the undersea case) built by consortia of telcos, to spread the cost across many players. In 2025, it looks like some of the major gen AI players are building at least some of this connection capacity, but largely on their own or with companies that are specialists in long-haul fiber.
Telcos may want to think about how they can continue to be a relevant player in the part of the connectivity space, rather than just ceding it to the gen AI behemoths. For context, it is estimated that big tech players will spend over US$100 billion on network capex between 2024 and 2030, representing 5% to 10% of their total capex in that period, up from only about 4% to 5% of capex for a network historically.
Where the opportunities could be greater are for connecting billions of consumers and enterprises. Telcos already serve these large markets, and as consumers and businesses start sending larger amounts of data over wireline and wireless networks, that growth might translate to higher revenues. A recent research report suggests that direct gen AI data traffic could be in exabyte by 2033.24
The immediate challenge is that many gen AI use cases for both consumer and enterprise markets are not exactly bandwidth hogs: In 2025, they tend to be text-based (so small file sizes) and users may expect answers in seconds rather than milliseconds,25 which can limit how telcos can monetize the traffic. Users will likely pay a premium for ultra-low latency, but if latency isn’t an issue, they are unlikely to pay a premium.
Telcos may want to think about how they can continue to be a relevant player in the part of the connectivity space, rather than just ceding it to the gen AI behemoths.
A longer-term challenge is on-device edge computing. Even if users start doing a lot more with creating, consuming, and sharing gen AI video in real time (requiring much larger file transmission and lower latency), the majority of devices (smartphones, PCs, wearables, or Internet of Things (IoT) devices in factories and ports) are expected to soon have onboard gen AI processing chips.26 These gen accelerators, combined with emerging smaller language AI models, may mean that network connectivity is less of an issue. Instead of a consumer recording a video, sending the raw image to the cloud for AI processing, then the cloud sending it back, the image could be enhanced or altered locally, with less need for high-speed or low-latency connectivity.
Of course, small models might not work well. The chips on consumer and enterprise edge devices might not be powerful enough or might be too power inefficient with unacceptably short battery life. In which case, telcos may be lifted by a wave of gen AI usage. But that’s unlikely to be in 2025, or even 2026.
Another potential source of gen AI monetization is what’s being called AI Radio Access Network (RAN). At the top of every cell tower are a bunch of radios and antennas. There is also a powerful processor or processors for controlling those radios and antennas. In 2024, a consortium (the AI-RAN Alliance) was formed to look at the idea of adding the same kind of generative AI chips found in data centers or enterprise edge servers (a mix of GPUs and CPUs) to every tower.The idea would be that they could run the RAN, help make it more open, flexible, and responsive, dynamically configure the network in real time, and be able to perform gen AI inference or training as service with any extra capacity left over, generating incremental revenues. At this time, a number of original equipment manufacturers (OEMs, including ones who currently account for over 95% of RAN sales), telcos, and chip companies are part of the alliance. Some expect AI RAN to be a logical successor to Open RAN and be built on top of it, and may even be what 6G turns out to be.
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The TM Forum has three broad “AI initiatives,” which are part of their overarching “Industry Missions.” These missions aim to change the future of global connectivity, with AI being a critical component.
The three broad “AI initiatives” (or “Industry Missions” where AI plays a central role) are:
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AI and Data Innovation: This mission focuses on the safe and widespread adoption of AI and data at scale within the telecommunications industry. It aims to help telcos accelerate, de-risk, and reduce the costs of applying AI technologies to cut operational expenses and drive revenue growth. This includes developing best practices, standards, data architectures, ontologies, and APIs.
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Autonomous Networks: This initiative is about unlocking the power of seamless end-to-end autonomous operations in telecommunications networks. AI is a fundamental technology for achieving higher levels of network automation, moving towards zero-touch, zero-wait, and zero-trouble operations.
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Composable IT and Ecosystems: While not solely an “AI initiative,” this mission focuses on simpler IT operations and partnering via AI-ready composable software. AI plays a significant role in enabling more agile and efficient IT systems that can adapt and integrate within dynamic ecosystems. It’s based on the TM Forum’s Open Digital Architecture (ODA). Eighteen big telcos are now running on ODA while the same number of vendors are described by the TM Forum as “ready” to adopt it.
These initiatives are supported by various programs, tools, and resources, including:
- AI Operations (AIOps): Focusing on deploying and managing AI at scale, re-engineering operational processes to support AI, and governing AI operations.
- Responsible AI: Addressing ethical considerations, risk management, and governance frameworks for AI.
- Generative AI Maturity Interactive Tool (GAMIT): To help organizations assess their readiness to exploit the power of GenAI.
- AI Readiness Check (AIRC): An online tool for members to identify gaps in their AI adoption journey across key business dimensions.
- AI for Everyone (AI4X): A pillar focused on democratizing AI across all business functions within an organization.
Under the leadership of CEO Nik Willetts, a rejuvenated, AI-wielding TM Forum now underpins what many telcos do in business and operational support systems, the essential IT plumbing. The TM Forum rates automation using the same five-level system as the car industry, where 0 means completely manual and 5 heralds the end of human intervention. Many telcos are on track for Level 4 in specific areas this year, said Willetts. China Mobile has already realized an 80% reduction in major faults, saving 3,000 person years of effort and 4,000 kilowatt hours of energy each year, thanks to automation.
Outside of China, telcos and telco vendors are leaning heavily on technologies mainly developed by just a few U.S. companies to implement AI. A person remains in the loop for critical decision-making, but the justifications for taking any decision are increasingly provided by systems built on the core underlying technologies from those same few companies. As IEEE Techblog has noted, AI is still hallucinating – throwing up nonsense or falsehoods – just as domain-specific experts are being threatened by it.
Agentic AI substitutes interacting software programs for junior technicians, the future decision-makers. If AI Level 4 renders them superfluous, where do the future decision-makers come from?
Caroline Chappell, an independent consultant with years of expertise in the telecom industry, says there is now talk of what the AI pundits call “learning world models,” more sophisticated AI that grows to understand its environment much as a baby does. When mature, it could come up with completely different approaches to the design of telecom networks and technologies. At this stage, it may be impossible for almost anyone to understand what AI is doing, she said.
References:
Sources: AI is Getting Smarter, but Hallucinations Are Getting Worse
McKinsey: AI infrastructure opportunity for telcos? AI developments in the telecom sector
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Dell’Oro Group just completed its 1Q-2025 Radio Access Network (RAN) report. Initial findings suggest that after two years of steep declines, market conditions improved in the quarter. Preliminary estimates show that worldwide RAN revenue, excluding services, stabilized year-over-year, resulting in the first growth quarter since 1Q-2023. Author Stefan Pongratz attributes the improved conditions to favorable regional mix and easy comparisons (investments were very low same quarter lasts year), rather than a change to the fundamentals that shape the RAN market.
Pongratz believes the long-term trajectory has not changed. “While it is exciting that RAN came in as expected and the full year outlook remains on track, the message we have communicated for some time now has not changed. The RAN market is still growth-challenged as regional 5G coverage imbalances, slower data traffic growth, and monetization challenges continue to weigh on the broader growth prospects,” he added.
Vendor rankings haven’t changed much in several years, as per this table:
Additional highlights from the 1Q 2025 RAN report:
– Strong growth in North America was enough to offset declines in CALA, China, and MEA.
– The picture is less favorable outside of North America. RAN, excluding North America, recorded a fifth consecutive quarter of declines.
– Revenue rankings did not change in 1Q 2025. The top 5 RAN suppliers (4-Quarter Trailing) based on worldwide revenues are Huawei, Ericsson, Nokia, ZTE, and Samsung.
– The top 5 RAN (4-Quarter Trailing) suppliers based on revenues outside of China are Ericsson, Nokia, Huawei, Samsung, and ZTE.
– The short-term outlook is mostly unchanged, with total RAN expected to remain stable in 2025 and RAN outside of China growing at a modest pace.
Dell’Oro Group’s RAN Quarterly Report offers a complete overview of the RAN industry, with tables covering manufacturers’ and market revenue for multiple RAN segments including 5G NR Sub-7 GHz, 5G NR mmWave, LTE, macro base stations and radios, small cells, Massive MIMO, Open RAN, and vRAN. The report also tracks the RAN market by region and includes a four-quarter outlook. To purchase this report, please contact us by email at [email protected]
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Separately, Pongrantz says “there is great skepticism about AI’s ability to reverse the flat revenue trajectory that has defined network operators throughout the 4G and 5G cycles.”
The 3GPP AI/ML activities and roadmap are mostly aligned with the broader efficiency aspects of the AI RAN vision, primarily focused on automation, management data analytics (MDA), SON/MDT, and over-the-air (OTA) related work (CSI, beam management, mobility, and positioning).
Current AI/ML activities align well with the AI-RAN Alliance’s vision to elevate the RAN’s potential with more automation, improved efficiencies, and new monetization opportunities. The AI-RAN Alliance envisions three key development areas: 1) AI and RAN – improving asset utilization by using a common shared infrastructure for both RAN and AI workloads, 2) AI on RAN – enabling AI applications on the RAN, 3) AI for RAN – optimizing and enhancing RAN performance. Or from an operator standpoint, AI offers the potential to boost revenue or reduce capex and opex.
While operators generally don’t consider AI the end destination, they believe more openness, virtualization, and intelligence will play essential roles in the broader RAN automation journey.
Operators are not revising their topline growth or mobile data traffic projections upward as a result of AI growing in and around the RAN. Disappointing 4G/5G returns and the failure to reverse the flattish carrier revenue trajectory is helping to explain the increased focus on what can be controlled — AI RAN is currently all about improving the performance/efficiency and reducing opex.
Since the typical gains demonstrated so far are in the 10% to 30% range for specific features, the AI RAN business case will hinge crucially on the cost and power envelope—the risk appetite for growing capex/opex is limited.
The AI-RAN business case using new hardware is difficult to justify for single-purpose tenancy. However, if the operators can use the resources for both RAN and non-RAN workloads and/or the accelerated computing cost comes down (NVIDIA recently announced ARC-Compact, an AI-RAN solution designed for D-RAN), the TAM could expand. For now, the AI service provider vision, where carriers sell unused capacity at scale, remains somewhat far-fetched, and as a result, multi-purpose tenancy is expected to account for a small share of the broader AI RAN market over the near term.
In short, improving something already done by 10% to 30% is not overly exciting. However, suppose AI embedded in the radio signal processing can realize more significant gains or help unlock new revenue opportunities by improving site utilization and providing telcos with an opportunity to sell unused RAN capacity. In that case, there are reasons to be excited. But since the latter is a lower-likelihood play, the base case expectation is that AI RAN will produce tangible value-add, and the excitement level is moderate — or as the Swedes would say, it is lagom.
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Editor’s Note:
ITU-R WP 5D is working on aspects related to AI in the Radio Access Network (RAN) as part of its IMT-2030 (6G) recommendations. IMT-2030 is expected to consider an appropriate AI-native new air interface that uses to the extent practicable, and proved demonstrated actionable AI to enhance the performance of radio interface functions such as symbol detection/decoding, channel estimation etc. An appropriate AI-native radio network would enable automated and intelligent networking services such as intelligent data perception, supply of on-demand capability etc. Radio networks that support applicable AI services would be fundamental to the design of IMT technologies to serve various AI applications, and the proposed directions include on-demand uplink/sidelink-centric, deep edge, and distributed machine learning.
In summary:
- ITU-R WP5D recognizes AI as one of the key technology trends for IMT-2030 (6G).
- This includes “native AI,” which encompasses both AI-enabled air interface design and radio network for AI services.
- AI is expected to play a crucial role in enhancing the capabilities and performance of 6G networks.
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References:
Dell’Oro: Private RAN revenue declines slightly, but still doing relatively better than public RAN and WLAN markets
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2160-0-202311-I!!PDF-E.pdf
