International 5G News Stories

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General

GSMA presses for European reform

The GSMA argued critical reform of European laws are necessary to position the region as a technology frontrunner by 2030, a rank it argued had been lost due to various barriers to digitalisation.

In a manifesto released today (20 March), the GSMA asserted barriers to the growth and competitiveness of the telecoms sector in the European Union (EU) have hindered the progress of the digital economy.

It highlighted a need to address “systemic challenges” around market fragmentation, regulation and investment, and called on EU politicians to “embrace a new digital infrastructure framework” which promotes financial input and market harmony.

Laszlo Toth, head of Europe and CIS at the industry association, said “urgent action to secure the continent’s digital future has never been more imperative”, as the region faces “significant geopolitical, economic and societal shifts”.

“This manifesto represents a commitment to partnership and progress, laying the groundwork for Europe to reclaim its position as a global leader in digital technology and innovation”.

The GSMA argued an overhaul is required to deliver universal 5G coverage in Europe by 2030, highlighting the achievements of mobile sector initiatives covering factories, farming and city management in terms of fuelling “economic growth” and sustainability.

It recommended EU politicians take action around spectrum costs and availability, levelling the regulatory playing field and “updating historically based rules to reflect current realities”.

General

Private 5G lands starring role among broadcasters

North American video networking company Haivision declared mobile as in vogue among broadcasters, with regular research showing strong interest in 5G along with compatible private networks.

A survey of 800 broadcast executives conducted between October and December 2023 showed mobile technologies are the second-most popular means of network transport after the internet.

The company stated 60 per cent of broadcasters are already using 3G, 4G or 5G “for live video contribution”, compared with 80 per cent employing internet connectivity.

Haivision also found high interest in 5G, reporting 74 per cent said they already use or plan to use the technology for broadcast contribution, a term used to describe the delivery of offsite live streams to a studio.

It added 46 per cent “anticipate” using private 5G for this purpose.

Markus Schioler, VP of marketing, stated Haivision’s 2024 Broadcast Transformation Report showed use of private 5G and AI are emerging trends.

The company found 60 per cent expect AI to be “the technology that will have the biggest impact on the broadcast industry in the next five years”, with 49 per cent planning or already employing it “in their workflows”.

A large proportion (84 per cent) also “use at least some cloud-based technology”, though Haivision noted “only 22 per cent” employ it in “more than half of their current workflow elements”.

Schioler described the use of cloud and mobile networks as an evolving trend, along with high efficiency video coding (HEVC), used by 67 per cent in broadcast contribution workflows.

General

Industry Voices: What can AI do for mobile operators?

Every year, the mobile industry has a new buzzword. This year, it’s “AI.” That’s artificial intelligence (AI), not an abbreviation for “Albert” or A1 steak sauce. Companies are enamored with this term because Nvidia has soared past a $2 trillion valuation.  

But let’s look more closely at what AI will really do in the mobile telecom market:

First off, let’s address the use of generative AI (GenAI) models to “chat” with customers or network techs and provide a human-language interface to the network. This might be useful as a way to replace human beings in the Customer Service call center, or in field operations, but my point of view is that this is a peripheral change, unlikely to work better than humans, and probably won’t realize as much business impact as people hope. 

The human-language aspect of AI is a nearly infinite problem: It requires huge databases and truly massive computing power. For that reason, I wonder whether the business benefit will be worth the investment.

Second, I’m more interested in the use of AI to enhance capacity in the network. In contrast with the boundless problem of human interaction, this is a finite problem with clear boundary conditions and clear metrics. Applying AI to the RAN, for example, can improve the way that users are grouped, the way that beams are steered and coordinated and the way that modulation and coding settings are used. This directed use of technology is aimed at the heart of the business case for a mobile operator: They need more capacity from each dollar of investment.

AI for capacity enhancements

Over the past year, I’ve had access to some field trials that are very promising for AI-directed capacity enhancements. The results are very encouraging, with a 20% – 40% increase in RAN capacity through the application of AI.

Each network vendor has a different way to view AI implementation and the best approach to capacity enhancement, but in general they choose 20-30 variables or “settings” in the network and remove the human algorithms previously used to choose the settings. In this case, “AI” means that the optimization engine can create its own algorithm to determine the best settings and achieve the highest possible capacity.

Recently, I saw a GPU workshop advertising a discussion about how to use AI to stretch 6G beyond Shannon’s limit. Hogwash! AI is not magic. It simply helps to bring the communication channel back to its ideal state when the network setup is not ideal.

In 5G, we have brought OFDMA to a level where we are approaching Shannon’s limit, meaning that we’re reaching a point where we are transmitting the maximum possible amount of information within a given bandwidth.

Well-optimized 5G networks are near this limit today in test networks under ideal lab conditions. In commercial networks in the field, nobody achieves the same results as ideal lab conditions because there are obstacles and interference and misalignments in the network. The real-world spectral and spatial efficiency is often half of the results achieved in a lab.

Half! Some of that loss cannot be recovered, because path losses (like the loss in penetrating a building) force the 5G link to a less efficient mode. But a great deal of inefficiency is built into today’s networks based on the constraints of human algorithms used to set up the network.   

Here’s an example: Setting up a 5G network, the technicians point the antennas in a given direction with some overlap between sectors. They set various frequency channels and turn on the network. Users will pop up with assignments to resource blocks and beams that are relatively random based on what’s available at the time. To improve the performance for an individual user, the network can reassign the user to a new band or massive MIMO beam, but there is not much coordination with other sites to ensure low cross-talk between beams.

With AI optimization, the beam selection, frequency channel selection, carrier aggregation settings and other technical parameters can be set holistically, instead of sequentially. That is how the AI engine can find improvements: by breaking users out of conditions that are sub-optimal.

In fact, what AI will do is to bring the average performance of the RAN closer to Shannon’s limit, not to make any user exceed Shannon’s limit.

The mobile industry is starting a new era, where capacity gain will not come from large blocks of spectrum or from a new “G” with better peak efficiency. Instead, the industry needs to learn how to squeeze more capacity out of existing spectrum. This will become one of the biggest differentiators in the RAN market three years from now.

General

Nvidia’s radio ambitions: Do AI RANs dream of 6G?

Artificial intelligence (AI) darling Nvidia laid out some of future radio access network (RAN) plans this week, unveiling a 6G Research Cloud platform at the GTC conference.

Nvidia’s aim partly appears to center around making its high-performance graphics processing units (GPUs) silicon as crucial to 6G RAN — and even 5G wireless — as the chips have become for many AI systems. Nvidia hasn’t yet become as big a player in RAN systems as it would evidently like to be.

The platform consists of a digital twin to enable users to simulate accurate radio environments for 5G and eventually 6G systems; a software-defined, full-RAN stack that allows researchers to customize, program and test 6G networks in real time; and a neural radio framework that uses Nvidia GPUs to train AI and machine learning models at scale.

“Really there’s nothing that limits its use in 5G,” said senior vice president of Telecom at Nvidia, Ronnie Vasishta. Noting that 5G spectrum is being used presently.

Vasishta said the digital twin will create “a much faster feedback loop” than currently available when testing specific environments that are physically accurate. “It works from one tower to city scale,” he said, noting that the twin can create an RF environment which models grass, concrete and buildings.

“You can now test your algorithms directly into that simulated environment,” he said. “Over time, you’ll be able to do this in real time.”

No word yet on what a simulated RF environment in NYC would look like or if they can model pizza rat!

Vasishta said that the research platform is already available. “It’s in limited availability right now,” he said. The Aerial digital twin will be available in April.

Analysts chip in

Analysts had a lot to say about the cloud platform, as it combined two of the hottest buzz words in the industry: AI and 6G.

“A lot of its work to date has been to sketch out potential 6G architectures and what this might mean for how you implement a future RAN system,” Gabriel Brown, senior principal analyst, mobile networks at Omdia, said of the Nvidia platform. “There is a play in 5G and 5G Advanced, but more as a testing ground.”

Roy Chua, principal at AvidThink, told Fierce, “The current work that they describe is based around O-RAN architecture, and…it could be used to improve today’s 5G RAN – so you can leverage it to explore improving xApps and rApps, and improve the performance of RAN today,” he said. “They are trying to help entrench GPUs into the 6G ecosystem, particularly around the RAN, and getting it going as university and vendor research teams are exploring ways to improve massive MIMO, L1 and L2 improvements for 6G.”

Nvidia clearly wants to make GPU chips the silicon of choice for 6G RAN, in a way they haven’t been for 5G systems.

“They’ve had limited traction in the RAN vendor community so far, but with the increased interest in O-RAN, plus spillover hype in AI/ML from GenAI, they likely see this as an opportune time to push into the telco market – they look like they have support from the RAN vendors this time,” Chua said.

“Taking the 6G research angle allows them to drive use of GPUs in 6G research as algorithms are being designed and then hopefully become entrenched into production platforms later,” he said.

“As we always say, the RAN pie is fixed over time but it is not fixed in opportunities,” Stefan Pongratz, VP at Dell’Oro Group said of Nvidia’s 6G play. “In contrast to other hyped technologies, there is a growing realization that AI, vRAN and automation are here to stay,” he concluded.

General

Sequans receives EUR 11 mln grant in France for 5G RedCap chip for IoT applications

Sequans Communications said that it has secured a EUR 10.9 million public grant in France to create a new chip for massive IoT applications that supports 5G NR eRedCap (5G new radio enhanced reduced capability). This product will meet the requirements of the 3GPP standard defined in Release 18, said the company, noting that it will also be backward compatible with existing 4G networks. The funding for this R&D project comes from the ‘France 2030’ investment programme operated by public investment bank Bpifrance.

The new funding follows the release earlier this month of Sequans’ preliminary financial results for 2023. The company said that it expects to achieve sequential growth in revenues in all four quarters of the current year. 

The company reported a turnover of USD 4.8 million for the three months to December, down 70 percent from USD 15.9 million in Q4 2022. It is now targeting revenues of over USD 7 million and a gross margin rate greater than 60 percent for Q1 2024, after reporting a rate of 12.2 percent in Q4 2023 and 78.5 percent in Q1 2023.

For the whole of 2023, revenues nearly halved to USD 33.6 million, from USD 60.6 million in 2022. Over the same period, the operating loss widened to USD 30 million from a comparable loss of USD 3.8 million.

Sequans CEO Georges Karam mentioned that the company’s board was actively engaging with multiple partners to explore various alternatives on the strategic front. This follows the collapse of the planned takeover by Renesas, which abandoned the acquisition of Sequans at the end of February after an adverse tax ruling.

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