Ericsson
Ericsson reports 10% drop in 1st quarter sales; targets network growth
Executive Summary:
Ericsson reported mixed first-quarter 2026 results, characterized by continued resilience in its Networks segment despite regional demand variability and emerging supply-side cost pressures. The Swedish company recorded 7% year-over-year organic growth in its Networks business, supported by sustained network modernization programs and ongoing 5G deployments across Europe, the Middle East, and Africa (EMEA), as well as increased delivery volumes in India and Japan. This growth offset a decline in North American sales, which followed a period of elevated operator investment in 2025 and reflects a near-term reallocation of capital expenditure by key customers. However, Ericsson reported a 10% total sales drop to 49.33 billion kronor in the first quarter, with EBITA falling to 1.44 billion kronor.
Ericsson reiterated its expectation of a broadly flat global RAN market in 2026 but expressed confidence in its ability to outperform the overall sector. The Networks segment maintained a robust adjusted gross margin of 50.4%, within its guided 49–51% range, with similar margin performance anticipated in the second quarter. Sequential revenue growth is projected to align with typical seasonal trends, approximating a 4% increase.
Despite these operational strengths, Ericsson highlighted increasing uncertainty in the macroeconomic and geopolitical environment. Of particular concern is the rising cost of components—especially semiconductors—driven in part by global AI-related demand. The company indicated that while semiconductors represent a relatively limited portion of its total cost base, sustained price increases are expected to create headwinds.
To mitigate these pressures, Ericsson is pursuing a combination of supply chain optimization, product substitution strategies, operational efficiencies, and selective cost-sharing mechanisms with customers. The company emphasized that its prior investments in supply chain diversification have enhanced resilience, although it acknowledged that it remains exposed to broader market disruptions affecting pricing and component availability.
Geopolitical factors have also introduced operational challenges. Ongoing conflict in the Middle East has necessitated adjustments to logistics and transportation routes, resulting in incremental costs. Ericsson noted that its regional distribution infrastructure has been impacted but that supply continuity has been maintained through flexible supply chain management.
From a financial perspective, Ericsson reported first-quarter EBIT of SEK 1.44 billion, a significant decline from SEK 5.93 billion in the prior year, reflecting restructuring charges and adverse currency movements. Group revenue decreased 10% year-over-year to SEK 49.33 billion, below market expectations, while gross margin contracted to 47.2% from 48.2%.
Image Credit: lars schroder/Agence France-Presse/Getty Images
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Börje Ekholm, Ericsson President and CEO, said:
“Our Q1 results demonstrate continued resilience in a dynamic environment, with organic sales growth of 6%. Our healthy gross margins and strong cash flow reflect the progress we have made in recent years, reducing reliance on geographic mix and strengthening our foundations globally. Our multi-year investments in building a resilient, diversified, supply chain have enabled us to deliver consistently for customers amidst geopolitical and macroeconomic uncertainties. We are facing increasing input costs, especially in semiconductors, caused in part by AI demand. Our ambition is to offset these challenges, by working closely with customers and suppliers, and through product substitution and efficiency actions. Looking ahead, while we continue to expect a flattish RAN market, our focused strategy, leading portfolio, and strengthened positions in mission critical and Enterprise give us confidence in our ability to grow faster than the mobile networks market and drive long-term success.”
Overall, the results underscore a transitional phase for Ericsson, with strong execution in global 5G and modernization programs partially offset by cyclical demand softness in North America and emerging cost inflation in critical technology inputs. The company recorded 7% year-over-year organic growth in its Networks business, supported by sustained network modernization programs and ongoing 5G deployments across Europe, the Middle East, and Africa (EMEA), as well as increased delivery volumes in India and Japan. This growth offset a decline in North American sales, which followed a period of elevated operator investment in 2025 and reflects a near-term reallocation of capital expenditure by key customers.
Ericsson’s quarter reinforces a broader industry pattern: the global RAN market is stabilizing after the 5G deployment peak, but not re-entering a meaningful growth phase. Until 6G capex begins to scale later in the decade, vendor performance will depend more on regional share gains, modernization cycles, and margin discipline than on total market expansion. After the 5G buildout peak, network operators are largely shifting from coverage expansion to optimization, monetization, and cost efficiency, which limits near-term revenue upside for vendors even when unit shipments remain healthy.
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RAN Market dynamics:
The key issue is that RAN demand is no longer being driven by broad-based new macro rollouts. Instead, spending is being concentrated on targeted modernization, selective capacity adds, and feature upgrades, while legacy LTE revenue continues to decline and offsets much of the remaining 5G activity.
That helps explain why vendors can still post pockets of growth in regions like EMEA, India, and Japan while North America softens after a prior wave of heavy investment. In other words, regional growth is becoming more cyclical and more dependent on operators’ capex timing than on a sustained global upgrade super-cycle.
Why RAN growth stays muted:
The structural problem is that RAN is maturing into a low-growth infrastructure market. Dell’Oro’s latest forecast points to only about 1% CAGR over the next five years, with the broader market remaining largely flat until 6G-related capex begins to ramp late in the decade.
That means the industry is effectively living through a long gap between the end of the 5G peak and the start of the 6G investment cycle. During that gap, vendors compete less on market expansion and more on mix, efficiency, software attach, and share gains, which is why financial performance can diverge from headline market growth.
What this means for Ericsson:
For Ericsson, the implication is that beating the market may matter more than the market itself. If the underlying RAN market is flat to low-single-digit growth, then Ericsson’s ability to sustain margin through supply-chain discipline, pricing, and product mix becomes more important than chasing top-line expansion alone.
This is also why component inflation matters now. When market growth is weak, cost pressure from semiconductors, logistics, and geopolitics has a larger effect on earnings quality, because vendors have fewer natural volume tailwinds to absorb it.
6G/IMT 2030 timing risk:
The big strategic uncertainty is timing. If meaningful telco 6G capex does not begin until around 2030–2031 (which seems highly likely), then the wireless telecom industry faces several years of subdued RAN revenue. That creates pressure on vendors to extract value from 5G Advanced, automation, private networks, and software-led differentiation before the next technology cycle arrives.
This is why “no real growth till 6G in 2031” is a reasonable framing. It captures the reality that the market can remain technically active while still being economically stagnant, with limited aggregate revenue growth even as networks become more capable and more software-defined.
From Sebastian Barros:
“Ericsson’s Q1 results are a masterclass in structural paradox. Pulling a 6% organic growth rate in a dead-flat global RAN market is a massive operational flex for a 150-year-old heavyweight. But look under the hood. Reported sales took a 10% hit due to brutal FX headwinds, and their supply chain is under intense pressure as global AI data centers hoard 3nm semiconductor capacity. Their historic dominance in custom ASIC silicon and radio frequency is exactly what makes them structurally vulnerable today. Being functionally addicted to a $35 billion RAN market that accounts for over 60% of their portfolio is a massive liability, as that profit pool is being actively dismantled by x86/GPU disaggregation, open architectures, and geopolitical hardware wars…”
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References:
Ericsson and Forschungszentrum Jülich MoU for neuromorphic computing use in 5G and 6G
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
China’s telecom industry rapid growth in 2025 eludes Nokia and Ericsson as sales collapse
SoftBank and Ericsson-Japan achieve 24% 5G throughput improvement using AI-optimized Massive MIMO
Huawei, Qualcomm, Samsung, and Ericsson Leading Patent Race in $15 Billion 5G Licensing Market
Ericsson announces capability for 5G Advanced location based services in Q1-2026
Highlights of Ericsson’s Mobility Report – November 2025
Ericsson’s revenue drops, profits soar; deal with Vodafone and partnership with Export Development Canada look promising
Huawei FY2025: 2.2% YoY revenue increase; strategic pivot to AI and intelligent automotive solutions
Overview:
Huawei has released its 2025 audited financial results, reporting total revenue of CNY 880.9bn ($127.6bn) — a 2.2% YoY increase. The report highlights a significant expansion in profitability, with operating profit surging 22.1% to CNY 96.9bn ($14bn). That translated to an operating profit margin of 11%, up 180 bps from the 9.2% recorded in 2024.
Image Credit: Imago/Alamy Stock Photo
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“In 2025, Huawei’s overall performance remained steady,” said Sabrina Meng, Huawei’s Rotating Chairwoman. “I would like to thank our customers for your ongoing trust and support. Thanks also to consumers for choosing Huawei, as well as suppliers, partners, and developers around the world for working with us. “Of course, we couldn’t do any of this without the support of every Huawei employee. Thank you for your hard work, and also your families for their steadfast support.”
In 2025, Huawei’s connectivity business weathered the impact of industry investment cycles, while its computing business continued to seize opportunities in AI. The consumer business worked to overcome formidable challenges, driving the HarmonyOS ecosystem to cross a new threshold in user experience. Huawei’s digital power business continued to place quality before all else. Huawei Cloud honed its competitiveness with a focus on core services, and the company’s intelligent automotive solutions grew rapidly.
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Pivot to Intelligent Automotive Solutions:
Huawei is aggressively diversifying and placing a massive strategic bet on the automotive sector to drive future growth. Its Intelligent Automotive Solutions business is experiencing explosive growth, with revenue increasing by over 400% in 2024 to 26.35 billion yuan ($3.62bn).
In 2025, the unit surged another 72% to CNY 45 billion (approx. $6.2bn). Huawei does not manufacture its own cars directly but operates as a top-tier supplier and technology partner (similar to “Bosch”) via its Harmony Intelligent Mobility Alliance (HIMA). Huawei continues to invest heavily in its “future-oriented” auto and AI businesses.
Revenue Breakdown by Segment & Geography:
- Infrastructure & Solutions: Remains the primary anchor, contributing 42.6% of total revenue (up 2.6% YoY).
- Consumer Business: Accounted for 39.1% of revenue, maintaining a steady recovery with 1.6% YoY growth.
- Intelligent Automotive Solutions (IAS): The high-growth outlier, with revenues spiking 72.1% YoY to CNY 45bn, now representing 5.1% of the total portfolio.
- Geographic Mix: Domestic China operations generated ~70% of revenue. International footprints were led by EMEA (18.3%), followed by Asia-Pacific (5.7%) and the Americas (4.2%).
R&D Intensity and Ecosystem Strategy:
Huawei continues to maintain one of the industry’s highest reinvestment rates, allocating CNY 192.3bn ($27.9bn) to R&D—a massive 21.8% of annual revenue. Huawei’s R&D expenditure rose 7% last year to an impressive RMB 192.3 billion (approximately $28 billion), representing nearly 22% of annual revenue.
In sharp contrast, Ericsson—whose portfolio remains heavily centered on 5G—reduced its R&D outlay by 9% to SEK 48.9 billion (about $5.2 billion). At 21% of sales, Ericsson’s R&D intensity was largely in line with Huawei’s. Nokia, meanwhile, outpaced both rivals in relative terms, allocating 23% of revenue—roughly €4.6 billion ($5.3 billion)—to R&D, up 7% year over year. Most of that increase stemmed from the February 2025 acquisition of optical systems vendor Infinera, which expanded Nokia’s technology base and R&D footprint.
The huge divergence lies in workforce trends. As reported by Light Reading, Ericsson and Nokia have collectively shed nearly 28,000 positions since 2022, equivalent to about 15% of their combined headcount that year. While growing automation and AI integration have arguably improved operational efficiency, the scale of these reductions also reflects a cooling investment climate among operators. With telco spending on 5G deployments tapering off, Europe’s two large network equipment vendors are continuing layoffs.
In contrast, Huawei’s workforce has continued to increase as it has pushed into new industrial sectors. Since 2021, when Huawei suffered its worst-ever sales decline, the Chinese behemoth has added about 18,000 employees to its payroll, according to annual reports. Around 5,000 of them were recruited last year, including 1,000 in R&D alone. That resulted in 213,000 employees Huawei employees in 2025.
The increased hiring boosted overall operating costs, including R&D expenditure, by 7.2%, to about RMB334 billion ($48.5 billion).
Source & Graph Credit: Light Reading
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Moving forward, China’s largest IT vendor’s roadmap prioritizes:
- Full-Stack AI Integration: Embedding AI and carrier-grade security across the entire product lifecycle and network architecture.
- Strategic Domain Expansion: Increasing CapEx and R&D in connectivity, cloud, and autonomous driving.
- Ecosystem Sovereignty: Scaling the Ascend (AI), Kunpeng (Computing), and HarmonyOS ecosystems to drive vendor-agnostic collaboration and industry-wide adoption
Meng stressed, “We are moving toward a future that is full of uncertainty, so we have to remain true to our strategy and maintain strategic focus. We will translate strategy to execution, keep cultivating the developer ecosystem, and pursue high-quality development.”
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References:
https://www.huawei.com/en/news/2026/3/annual-report-2025
https://www.lightreading.com/5g/huawei-sales-growth-plummeted-in-2025-as-it-gained-5-000-workers
Huawei unveils AI Centric Network roadmap, U6 GHz products, 5G Advanced strategy and SuperPoD cluster computing platforms
Huawei, Qualcomm, Samsung, and Ericsson Leading Patent Race in $15 Billion 5G Licensing Market
Huawei Cloud Review and Global Sales Partner Policies for 2026
Huawei’s Electric Vehicle Charging Technology & Top 10 Charging Trends
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
Omdia on resurgence of Huawei: #1 RAN vendor in 3 out of 5 regions; RAN market has bottomed
Huawei launches CloudMatrix 384 AI System to rival Nvidia’s most advanced AI system
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Introduction:
The narrative surrounding “AI-RAN” — a term thrust into the spotlight by Nvidia — may have left many believing that boatloads of GPUs are already powering baseband compute in RAN equipment across the world’s seven million mobile sites. In truth, the reality is far more nascent.
Among major RAN vendors, Nokia stands alone in adapting baseband software for GPU acceleration. Yet even Nokia does not anticipate commercial readiness until late 2026, as confirmed by its Chief Technology Officer, Pallavi Mahajan, during the company’s MWC press conference earlier this year. For now, no operator has announced a commercial deployment — despite the buzz around trials.
Early Movers, Limited Momentum:
Much of the current AI-RAN activity centers on two operators: T-Mobile US and Japan’s SoftBank. At MWC, T-Mobile’s Executive Vice President of Innovation and ex-CTO, John Saw, acknowledged the limited availability of deployable solutions, quipping that he hoped Nokia would deliver an AI-RAN product within the year. Nokia CEO Justin Hotard quickly assured him that such a milestone was indeed on track.
Still, the debut of a GPU-based RAN stack does not imply an imminent large-scale rollout. Without tangible network performance or cost advantages over existing virtualized or disaggregated RAN approaches, operators are unlikely to move past controlled trials.
SoftBank, while often positioned as an AI-RAN pioneer, remains cautious. As Ryuji Wakikawa, Vice President of its Advanced Technology Division, outlined last year, the operator aims to deploy only a handful of AI-RAN sites over the next fiscal cycle. Transitioning from testing to carrying live commercial traffic, he emphasized, demands a significant maturity leap in quality and feature completeness.
Beyond Hype: Limited Commercial Engagement:
Elsewhere, Indonesia’s Indosat Ooredoo Hutchison (IOH) was heralded in 2025 as the first operator in Southeast Asia pursuing AI-RAN. More than a year later, authoritative sources indicate IOH’s work remains confined to its research facility in Surabaya, with no near-term plans for GPU investment at cell sites until measurable value is demonstrated.
The challenge for Nokia — and for GPU-backed AI-RAN broadly — is convincing operators that general-purpose accelerators offer sufficient performance or efficiency gains for most RAN workloads. T-Mobile and SoftBank continue evaluating both Nokia and Ericsson, whose AI-RAN philosophies diverge sharply. Nokia is developing GPU-based baseband software, while Ericsson maintains its focus on custom silicon and CPU architectures.
Divergent Architectures and Use Cases:
Ericsson contends that no core RAN performance enhancements intrinsically require GPUs. Its ongoing collaboration with Nvidia leverages the latter’s Grace CPU technology rather than its GPU portfolio, reserving GPU acceleration only for compute-intensive functions like forward error correction (FEC).
If Ericsson’s premise holds, GPUs in the RAN become justifiable only when supporting AI inference workloads. Even then, inference at every radio site remains improbable. A more incremental strategy — deploying GPUs selectively at edge locations where AI workloads justify their cost — may prove more practical.
This modular approach aligns with existing virtual RAN deployments based on Intel CPUs, which already include native FEC acceleration. “It is an off-the-shelf card that you can slide right into an HPE or Dell or Supermicro server,” said Alok Shah, the vice president of network strategy for Samsung Networks. “That gets you the edge functionality you are looking for.”
Rethinking the Economic Case for AI RAN:
Initially, Nvidia positioned GPUs for AI-RAN as viable only if broadly utilized for AI inference across the RAN. Following its strategic alignment with Nokia, however, the company has softened its stance — now suggesting that appropriately sized, power-efficient GPUs could make sense even when dedicated solely to baseband computation.
For now, the global RAN landscape remains far from GPU-saturated. AI-RAN remains an exploratory frontier — one testing not only the technical feasibility of GPUs at the edge, but also the economic/business case rationale for re-architecting a trillion-dollar telecom infrastructure around them.
The AI models suitable for RAN environments must be compact and efficient, far slimmer than those that drive data center-scale AI. There’s no room for the massive, parameter-heavy neural networks that justify a GPU’s cost or energy appetite. In that light, a GPU looks less like a breakthrough and more like a mismatch — a chainsaw brought to a task better handled with a sharp pair of scissors.
Evaluating the Case for AI-RAN Acceleration:
The central question is whether GPUs can deliver meaningful benefits over custom silicon or conventional CPUs for RAN compute. Ericsson’s engineers argue that AI models deployed at the RAN must remain relatively lightweight, with far fewer parameters than those used in large-scale data centers. Excessive model complexity could introduce signaling delays unacceptable in real-time radio environments. In this context, deploying a GPU for such workloads might seem disproportionate — a high-powered tool for a low-demand task.
The most compelling defense of GPU-based RAN acceleration came from Ronnie Vasishta, Nvidia’s Senior Vice President for Telecom, who told Light Reading last summer, “The world is developing on Nvidia.” His point underscores a shift in semiconductor economics: the cost and risk of building dedicated silicon for a mature and shrinking RAN market make general-purpose processors — supported by large-volume ecosystems — increasingly attractive alternatives.
Intel’s difficulties further illustrate this dynamic. Despite $53 billion in revenue during 2025, the former microprocessor king barely broke even despite $53 billion in revenue, following a $19 billion loss the previous year. A major restructuring cut its headcount by nearly 24,000, and its planned spinoff of the Network and Edge division — serving telecom infrastructure customers — was ultimately abandoned in December. Nvidia, the world’s most valuable company, may be eager to step into that space — but the economic logic seems upside down. Wireless network operators are looking to reduce costs, not import data center economics into the RAN.
Ecosystem or Echo Chamber?
Nvidia’s Aerial platform and CUDA-based software ecosystem do present a compelling story: open infrastructure, modular APIs, and space for smaller developers to innovate alongside giants like Nokia. On paper, it’s an alluring image of democratized RAN software. In practice, it ties the industry even more tightly to a vertically integrated, proprietary ecosystem.
Nokia appears comfortable with that trade-off. Nokia CTO Pallavi Mahajan’s recent blog post framed AI-RAN as a vehicle for “software speed and innovation.” He added, “Nokia’s AI-RAN initiative begins with a simple observation: AI is changing not only how networks are operated, but also the nature of the traffic they carry. AI workloads have already reached massive scale, with mobile devices accounting for more than half of AI interactions. Large language model interactions introduce richer uplink flows and burstier patterns as devices continuously send context to models.”
Indeed, that me be true someday. But for now, most wireless network operators need stable, cost-efficient networks, not AI-driven complexity or GPU-level power draw.
Image Credit: Nokia
Conclusions:
The uncomfortable truth is that AI-RAN feels more like a vendor-driven experiment than an operator-driven demand. Until someone proves that GPUs in the RAN deliver a measurable payoff — in performance, cost, or operational simplicity — the whole concept risks joining the long list of telecom “game-changers” that never made it past the trial stage. The hype cycle is predictable; the economics are not. Unless that equation changes, the real intelligence may be knowing when not to deploy AI RAN.
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In a Substack post today, Sebastian Barros writes: What Does AI-RAN Even Mean?
Despite the crazy hype, there is no definition for AI-RAN. Today it is at best a vibe, a dangerous reality for an industry that moves on strict standards that are currently completely absent.
The AI RAN hype is crazy right now. But despite the endless talk and vendor announcements, there is no actual technical definition of what it even means. As wild as it sounds for an industry built on strict 3GPP and O-RAN standards (those are specs- not standards), AI RAN is currently just a vendor interpretation designed to move hardware. Moreover, telecom has been using AI in the RAN before it was even cool. In fact, we were among the first industries to use neural networks in signal processing back in the 80s.
The problem is that treating AI-RAN as a marketing narrative rather than a rigid standard actively stalls progress. When the definition of AI-RAN is as different as night and day depending on which OEM you ask, it becomes impossible for any Telco to accurately model TCO or make solid CAPEX decisions.
Editor Notes:
- ITU-R’s IMT-2030 framework (ITU-R Recommendation M.2160-0 for IMT-2030) calls for an AI-native new air interface and AI-enhanced radio networks, but does not mention Nokia’s AI RAN.
- 3GPP Release 18 and later have study/work items on AI/ML for RAN functions such as energy saving, load balancing, mobility optimization, and AI/ML on the RAN air interface, but again no specifics have been discussed let alone agreed upon.
- 3GPP Release 19 continues and expands this work, with reporting that it builds on Release 18’s normative work and adds new AI/ML-based use cases for NG-RAN. In other words, 3GPP does have AI-RAN-related specs in progress and some normative features, but they are distributed across multiple RAN work items rather than packaged as one standalone “AI RAN” specification.
- AI RAN Alliance “is dedicated to driving the enhancement of RAN performance and capability with AI.” However, they’ve not yet produced any implementable specifications for AI RAN. Yet there are only four carriers that are “executive members“: Vodafone, T-Mobile, and SK Telecom, and Softbank (which is a conglomerate).
In Japan, NTT Docomo holds the largest cellular market share, with KDDI second, followed by SoftBank and the rapidly expanding Rakuten Mobile.
References:
https://www.lightreading.com/5g/ai-ran-lots-of-talk-little-action-no-guarantees
https://www.nokia.com/blog/ai-ran-bringing-software-speed-innovation-into-the-radio-network/
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
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: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
Overview:
AT&T and Ericsson have completed a milestone Cloud RAN test by successfully demonstrating Ericsson’s AI-native Link Adaptation [1.] on a Cloud RAN stack powered by Intel Xeon 6 SoC. The test showed how artificial intelligence (AI) can improve spectral efficiency and network responsiveness in real-world conditions. Conducted over AT&T’s licensed frequency bands, the experiment was the first to use portable Ericsson RAN software running on Intel’s new Xeon 6 system-on-chip (SoC) platform—an architecture designed for high-performance, cloud-native processing of RAN workloads. Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.
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Note 1. AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.
Other Notes:
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vRAN: A radio access network (RAN) in which the baseband processing functions run as software on general-purpose processors (mostly from Intel) instead of on dedicated hardware at the cell site. In vRAN, the functional split defines how baseband processing is divided between centralized processors and the radio unit at the site, and that split drives fronthaul bandwidth, latency, and cost.
- Cloud RAN: An evolution of vRAN where those same RAN functions are re-architected as cloud‑native microservices/containers with CI/CD (Continuous Integration and either Continuous Delivery or Continuous Deployment), automation, and orchestrators, optimized for elastic scaling across distributed cloud infrastructure.
- Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.
- According to Dell’Oro Group, Cloud RAN (often encompassing vRAN) accounted for approximately 5% to 10% of the total global Radio Access Network (RAN) market revenues in 2025. In early 2026, Dell’Oro revised Cloud RAN projections downward. While virtualization remains a “key pillar” for the long term, short-term adoption is being slowed by performance, power, and cost-parity challenges when compared to purpose-built hardware.
- The total RAN market stabilized in late 2025 after losing approximately 20% of its value between 2022 and 2024. Market concentration reached a 10-year high in 2025, with the top five vendors (Huawei, Ericsson, Nokia, ZTE, and Samsung) capturing 96% of the revenue.
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Image Credit: Ericsson
In this proof-of-concept setup, Ericsson’s disaggregated and containerized RAN software operated within AT&T’s target Cloud RAN configuration, built on open, commercial off-the-shelf hardware. The test advanced from basic call functionality to validation of feature-rich network behavior in a cloud computing environment. Ericsson’s AI-native Link Adaptation is a learning algorithm that continuously assesses channel state and interference to determine the optimal modulation and coding scheme for each transmission interval. By generating real-time predictions of link quality, the AI model dynamically adjusts data rates to maximize throughput and spectral efficiency.
Early results were promising. Throughput gains reached up to 20% compared with conventional rule-based link adaptation approaches, alongside measurable improvements in spectral efficiency. Ericsson and Intel also used the trial to benchmark various AI inference models, demonstrating performance scalability and energy efficiency on general-purpose compute nodes rather than proprietary hardware accelerators. This suggests a more pragmatic path for deploying AI workloads across distributed RAN architectures.
AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.
Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.
Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.
Beyond the immediate performance improvements, the trial illustrates how open RAN architectures can accelerate innovation. By decoupling RAN software from vendor-specific hardware, AT&T can integrate AI capabilities and update network functions more quickly, avoiding the constraints of lock-in. The portability demonstrated here—running production-grade Ericsson RAN software on Intel Xeon 6 silicon—marks an industry first.
For AT&T, the achievement represents more than a lab milestone. It provides a technical template for scaling AI-native RAN functions into its cloud infrastructure, pointing to a future where machine learning operates natively within radio environments to fine-tune performance in real time. As operators continue balancing cost, flexibility, and efficiency, AI-optimized Cloud RAN deployments could become the next competitive frontier in 5G—and eventually, 6G—network evolution.
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Quotes:
Rob Soni, Vice President, RAN Technology at AT&T, says: “AT&T is leading the charge toward an open, intelligent, and scalable network future by advancing Open RAN and Cloud RAN with AI-native capabilities at their core. This demo highlights how AI capabilities, powered by our next-generation Cloud RAN platform, can be deployed seamlessly to drive innovation and deliver superior customer experiences.”
Mårten Lerner, Head of Networks Strategy and Product Management, Business Area Networks at Ericsson, says: “Together with AT&T and Intel, Ericsson is demonstrating how our domain expertise combined with AI-native RAN software can drive transformative advancements in both Cloud RAN and purpose-built deployments. Our industry-leading AI-native Link Adaptation serves as the first proof point on this journey. With a hardware-agnostic RAN software stack, Ericsson is committed to offering maximum flexibility and enabling all our customers to benefit from future innovations – regardless of their chosen underlying hardware. This milestone underscores Ericsson’s commitment to helping operators advance their networks by deploying AI functionality across the RAN stack.”
Cristina Rodriguez, VP and GM, Network and Edge at Intel, says: “This successful collaboration with AT&T and Ericsson showcases the power of Intel Xeon 6 SoC to enable and accelerate AI workloads in Cloud RAN environments. Xeon 6 SoC is architected to handle the demanding compute requirements of AI-native network functions, delivering the performance and efficiency operators need to unlock the full potential of intelligent networks. By providing a flexible, standards-based platform, Intel Xeon 6 enables service providers like AT&T to deploy innovative AI capabilities while maintaining the openness and choice that drive industry innovation.”
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AI-Native Link Adaptation vs. Traditional Methods:
Traditional link adaptation in RAN relies on deterministic, rule-based algorithms that select the Modulation and Coding Scheme (MCS) from predefined lookup tables. These methods primarily use instantaneous Channel Quality Indicator (CQI) reports or estimated Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, often adjusted via Outer Loop Link Adaptation (OLLA) based on ACK/NACK feedback from the UE. This reactive approach applies conservative margins to account for channel estimation errors, prediction lag, and varying interference, which can lead to suboptimal throughput—either underutilizing the link with low MCS or triggering excess HARQ retransmissions with overly aggressive selections.
AI-native Link Adaptation shifts to a predictive, model-driven paradigm using machine learning (typically lightweight neural networks or time-series models) trained on historical channel data. Rather than static thresholds, the AI processes sequences of CQI, beam metrics, mobility patterns, and interference traces to forecast the probable channel state for the next transmission time interval (TTI). This enables precise MCS selection that hugs the Shannon capacity limit more closely, minimizing BLER while maximizing spectral efficiency in dynamic scenarios like high-mobility NLOS or bursty interference.
Key differences include:
| Aspect | Traditional (Rule-Based) | AI-Native (ML-Based) |
|---|---|---|
| Decision Mechanism | Lookup tables, SINR thresholds, OLLA offsets | Real-time inference from ML models |
| Channel Handling | Reactive (past CQI/SINR) | Predictive (time-series forecasting) |
| Adaptation Speed | Step-wise, with feedback lag | Continuous, sub-TTI granularity |
| Performance Gains | Baseline (0% reference) | Up to 20% throughput, 10% spectral efficiency |
| Compute Needs | Low (fixed arithmetic) | Moderate (edge inference on COTS like Xeon 6) |
| Limitations | Struggles with non-stationary channels | Requires training data, retraining overhead |
Analysis: Rakuten Mobile and Intel partnership to embed AI directly into vRAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
vRAN market disappoints – just like OpenRAN and mobile 5G
Nokia and Eolo deploy 5G SA mmWave “Cloud RAN” network
Ericsson and Google Cloud expand partnership with Cloud RAN solution
Ericsson and O2 Telefónica demo Europe’s 1st Cloud RAN 5G mmWave FWA use case
Cloud RAN with Google Distributed Cloud Edge; Strategy: host network functions of other vendors on Google Cloud
vRAN market disappoints – just like OpenRAN and mobile 5G
Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026
Ericsson and Intel at MWC 2026:
Building on milestones in Cloud RAN, 5G Core, and open network innovation, Ericsson and Intel are showcasing joint technology advancements at the Mobile World Congress (MWC) 2026 in Barcelona this week. Demonstrations can be experienced at the Ericsson Pavilion (Hall 2), Intel Booth (Hall 3, Stand 3E31), and across partner event spaces, highlighting the companies’ shared progress in enabling the next era of AI-driven networks.
The two companies are strengthening their long-standing technology partnership to accelerate ecosystem readiness for AI-native 6G networks and use cases. The expanded collaboration spans next-generation mobile connectivity, cloud infrastructure, and compute acceleration — with a focus on AI-driven RAN and packet core evolution, platform-level security, and scalable cloud-native architectures designed to shorten time-to-market for advanced network solutions.
“6G is not merely an iteration of mobile technology; it will serve as the foundational infrastructure distributing AI across devices, the edge, and the cloud,” said Börje Ekholm, President and CEO of Ericsson. “With our deep history in network innovation and global-scale operator deployments, Ericsson is uniquely positioned to drive practical 6G integration from research to commercialization.”
Lip-Bu Tan, CEO of Intel, added: “Intel’s vision is to lead the industry in unifying RAN, Core, and edge AI to enable seamless deployment of AI-native 6G environments. Together with Ericsson, we are proving that next-generation connectivity can be open, energy-efficient, secure, and intelligent. With future Ericsson Silicon built on Intel’s most advanced process technologies, coupled with Intel Xeon-powered AI-RAN ready Cloud RAN and collaborative multi-year research efforts, we are delivering the performance, efficiency, and supply assurance demanded by leading operators worldwide.”
As 6G transitions from research to commercialization, the industry must align around a mature, standards-based ecosystem. The Ericsson–Intel collaboration aims to accelerate development of high-performance, energy-efficient compute architectures optimized for both AI for Networks and Networks for AI.
AI-native 6G will fuse intelligent, programmable network functions with distributed compute and real-time sensing, bringing processing power closer to the network edge and enabling ultra-responsive, adaptive services. This convergence will enhance network efficiency, agility, and service intelligence across future deployments.
About Ericsson:
Ericsson‘s high-performing networks provide connectivity for billions of people every day. For 150 years, we’ve been pioneers in creating technology for communication. We offer mobile communication and connectivity solutions for service providers and enterprises. Together with our customers and partners, we make the digital world of tomorrow a reality.
About Intel:
Intel is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better.
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Related AI-Native 6G Announcements at MWC 2026:
In addition to the Ericsson-Intel collaboration, several vendors and operators announced AI-native 6G advancements or related demos at MWC Barcelona 2026. These initiatives emphasize AI-RAN integration, software-defined architectures, and ecosystem partnerships to bridge 5G-A to 6G.
NVIDIA Multi-Partner Commitment: NVIDIA rallied operators and vendors including Booz Allen, BT Group, Cisco, Deutsche Telekom, Ericsson, Nokia, SK Telecom, SoftBank, and T-Mobile to build open, secure AI-native 6G platforms. The focus is on software-defined wireless with AI embedded in RAN, edge, and core for integrated sensing, communications, and interoperability.
Nokia AI-RAN: Nokia highlighted new partnerships with Dell, Quanta, Red Hat, SuperMicro, NVIDIA, and operators like T-Mobile, Indosat Ooredoo Hutchison, BT, Elisa, NTT DOCOMO, and Vodafone for AI-RAN trials paving the way to cognitive 6G networks. Live demos at Nokia’s Hall 3 Booth 3B20 included Southeast Asia’s first AI-RAN Layer 3 5G call on shared GPU infrastructure and vision AI for immersive services.
T-Mobile & Deutsche Telekom Hub: T-Mobile US and (major shareholder) Deutsche Telekom launched a joint 6G Innovation Hub targeting AI-native autonomous networks, secure sensing/positioning, and connectivity-compute convergence for Physical AI. It builds on agentic AI proofs like network-integrated translation, emphasizing “kinetic tokens” for real-time physical world control.
ZTE GigaMIMO 6G Prototype: ZTE unveiled the world’s first 6G prototype with 2000+ U6G-band antenna elements (GigaMIMO), powered by AI algorithms for 10x capacity over 5G-A, 30% spectral efficiency gains, and AI-driven immersive services. Booth 3F30 demos integrate AI across connectivity, computing, and devices for “AI serves AI” networks.
Qualcomm Agentic AI RAN: Qualcomm announced AI-native RAN management services in its Dragonwing suite for autonomous 6G-grade networks, plus new Open RAN AI features for performance optimization. CEO Cristiano Amon’s keynote focused on “Architecting 6G for the AI Era,” with device-to-data-center transformations.
Huawei U6GHz for 6G Path:
Huawei released all-scenario U6GHz products (macro/micro sites, microwave) with AI-centric solutions for 5G-A capacity (100 Gbps downlink) and low-latency AI apps, enabling smooth 6G evolution. Emphasizes hyper-resolution MU-MIMO and multi-band coordination for indoor/outdoor AI experiences.
Summary Chart:
| Vendor/Operator | Key Focus | Partners/Demos | Booth/Location |
|---|---|---|---|
| NVIDIA | Open AI-native platforms | Multiple operators/vendors | MWC general |
| Nokia | AI-RAN trials & cognitive networks | NVIDIA, T-Mobile, IOH et al. | Hall 3, 3B20 |
| T-Mobile/DT | Physical AI hub | Joint R&D | Announced pre-MWC |
| ZTE | GigaMIMO 6G prototype | China Mobile, Qualcomm | Hall 3, 3F30 |
| Qualcomm | Agentic RAN automation | Open RAN ecosystem | Keynote & demos |
| Huawei | U6GHz AI-centric evolution | Carrier-focused | MWC showcase |
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References:
NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU
Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)
SKT 6G ATHENA White Paper: a mid-to-long term network evolution strategy for the AI era
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Nokia and Rohde & Schwarz collaborate on AI-powered 6G receiver years before IMT 2030 RIT submissions to ITU-R WP5D
SK Telecom, DOCOMO, NTT and Nokia develop 6G AI-native air interface
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
Executive Summary:
Today, Nokia announced a strategic collaboration with Amazon (AWS), Du, and Orange to debut an industry-first agentic AI-driven network slicing [1.] capability on a 5G SA core network. Du and Orange will deploy this new technology which uses Nokia’s 5G AirScale base stations, MantaRay SMO and Agentic AI modules in tandem with Amazon’s Bedrock Artificial Intelligence platform. Autonomous AI agents are used to ingest and process real-time telemetry—including geospatial data, event triggers, and traffic patterns—the framework enables adaptive network slicing. This architecture allows communications service providers (CSPs) to dynamically orchestrate resources in response to fluctuating demand, such as prioritizing mission-critical throughput for first responders during emergency incidents.
Note 1. There are no ITU standards for network slicing or the 5G SA Core network required to implement that capability. 3GPP specifications define end-to-end network slicing architecture, covering slice management (TS 28.552, TS 28.554), service requirements, and security (NSSAA – Network Slice Specific Authentication and Authorization). The NSA and CISA have released specific, recognized guidance on designing, deploying, and maintaining secure 5G standalone (SA) network slices. ETSI publishes and adopts 3GPP technical specifications (specifically the 28-series) as European standards for network slicing management, including 5G RAN, core network, and NFV-MANO architecture. ETSI, as a 3GPP partner, ensures these specifications cover the lifecycle of network slices.
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- Data Ingestion & Inference: Agentic AI modules, hosted on Amazon Bedrock, ingest real-world contextual data (e.g., emergency alerts, traffic sensors, weather) alongside live network KPIs.
- Intent-Based Policy Generation: The AI agents analyze this telemetry to determine the optimal network configuration required to meet specific Service Level Agreements (SLAs) or emergency “intents'”
- NEF & SMO Integration: These high-level intents are translated into actionable policies and pushed to Nokia’s MantaRay SMO (Service Management and Orchestration).
- Dynamic RAN/Core Adjustment: The Network Exposure Function (NEF) acts as the secure gateway, allowing the AI agents to interface with the 5G Core. It exposes network capabilities so the agents can dynamically adjust RAN policies and resource allocation across the 5G AirScale base stations.
- Autonomous Feedback Loop: The system operates in an autonomous mode where agents continuously monitor the results of their adjustments, performing forensic analysis to refine slicing parameters in real-time.
Nokia will host live technical demonstrations of this AI network slicing capability at its 2026 Mobile World Congress (MWC) Barcelona exhibit.
Quotes:
“This innovation marks a major milestone in the evolution of AI-native networks,” said Pallavi Mahajan, Chief Technology and AI Officer at Nokia. “By combining Nokia’s advanced network slicing capabilities with agentic AI, we are enabling operators to deliver premium, intent-based services that adapt dynamically to real-world conditions. Nokia is advancing connectivity by unlocking new value streams for telecommunication providers and supporting next-generation applications and differentiated services for enterprises, industries and consumers.”
Amir Rao, Global Director, GTM & Telco Solutions at AWS added: “Network slicing has long promised to unlock new revenue streams for operators, but manual configuration and static policies have prevented end customers from accessing on-demand provisioning. By integrating agentic AI capabilities through Amazon Bedrock with Nokia’s application, operators can now deliver intelligent, context-aware network slicing that responds dynamically to real-world conditions from traffic surges to emergency situations. This transforms network slicing from a technical capability into a true business enabler, allowing operators to monetize their 5G investments through differentiated, premium services that adapt automatically to customer needs. Agentic Network Slicing is the beginning of an era that will enable telecommunications providers to enable real-time intent-based service provisioning for end customers.”
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Competitive Network Slicing Solution:
Rival wireless equipment vendor Ericsson yesterday gave a preview of a network slicing related offering which it will be demonstrating at the 2026 MWC. Together with Ookla it has developed a specialized test version of its Speedtest app designed to measure and validate 5G network slicing performance. The tool enables the Speedtest app to identify and test specific network slices, which apparently demonstrates how Service Level Agreements (SLAs) for differentiated services can be verified in real-time by consumers and service providers.
Ericsson reported in its latest Mobility report that there were 65 commercial network slicing services worldwide providing so-called “differentiated connectivity” offerings. That’s out of a total of 118 network slicing cases discovered by Ericsson’s researchers. Yet in the UK, none of the three mobile network operators have launched a commercial 5G network slicing capability yet. According to Ofcom’s latest Connected Nations report, 5G SA is available across 83% of outside areas in the country and 5G SA accounts for nearly one-third of 5G traffic. However, 4G accounts for 72% of total monthly data traffic.
“Network slicing is no longer a future concept; it is a commercial reality. However, you cannot manage what you cannot measure,” said Tibor Rathonyi, Senior Advisor at Ookla. “Our work with Ericsson is a pivotal first step in providing the transparency needed to prove the value of these premium 5G services to both consumers and enterprises.”
Philipp Bichsel, Executive Vice President Mobile Network & Services at Swisscom, said: “Swisscom has retained the title as the country’s best-performing mobile network over many years by truly prioritizing the delivery of the best possible customer experience. This has meant embarking on a journey to fully exploit automation to enhance reliability and efficiency without compromising the service quality our customers expect. As we advance towards self-learning, autonomous networks, enabling Swisscom to build smarter and more adaptive network operations, we are leveraging the SMO framework as the foundation for this evolution. Within this framework, partner solutions such as Ericsson’s Intelligent Automation Platform and its ecosystem of rApps play an important role in helping us explore the potential of AI driven automation.”
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References:
https://www.telecoms.com/5g-6g/nokia-and-aws-show-off-agentic-ai-powered-5g-advanced-network-slicing
https://www.telecoms.com/5g-6g/ericsson-and-ookla-launch-network-slicing-measurement-tool
https://www.lightreading.com/5g/eurobites-network-slicing-enjoying-a-moment-finds-ericsson-report
https://www.ericsson.com/en/reports-and-papers/mobility-report/reports/november-2025
https://www.lightreading.com/5g/5g-network-slicing-not-ready-for-prime-time-in-uk
https://www.awardsolutions.com/portal/resources/network-slicing
ABI Research: 5G network slicing market to hit $67.52 billion in 2030 with Asia Pacific in the lead
5G network slicing progress report with a look ahead to 2025
FCC Draft Net Neutrality Order reclassifies broadband access; leaves 5G network slicing unresolved
Telstra achieves 340 Mbps uplink over 5G SA; Deploys dynamic network slicing from Ericsson
ABI Research: 5G Network Slicing Market Slows; T-Mobile says “it’s time to unleash Network Slicing”
Ericsson, Intel and Microsoft demo 5G network slicing on a Windows laptop in Sweden
Ericsson and Nokia demonstrate 5G Network Slicing on Google Pixel 6 Pro phones running Android 13 mobile OS
Nokia and Safaricom complete Africa’s first Fixed Wireless Access (FWA) 5G network slicing trial
Is 5G network slicing dead before arrival? Replaced by private 5G?
5G Network Slicing Tutorial + Ericsson releases 5G RAN slicing software
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Ahead of MWC Barcelona 2026, Ericsson unveiled its initial suite of AI-RAN products at a pre-event briefing in London, emphasizing a strategy anchored in proprietary, purpose-built silicon to enhance radio access network (RAN) performance. While the wireless industry is finally moving to virtualized/cloud RAN utilizing general-purpose processors from Intel, Ericsson is defending its continued investment in custom silicon for specialized, high-performance tasks.
Concurrently, the company is demonstrating a strong push toward software-defined flexibility, ensuring its proprietary RAN algorithms and AI-native software are portable across diverse, open silicon platforms. Ericsson was exploring the use of Nvidia’s Arm-based Grace CPU, rather than the Hopper-branded GPU, but has opted for custom silicon (ASICs) instead.
Ericsson’s RAN portfolio currently diverges into two primary architectures. The majority of its footprint relies on ASICs—developed through internal design and external partnerships with Intel. The alternative is Cloud RAN, which pairs Ericsson’s software stack with Intel Xeon processors. Despite the industry’s promise that virtualization would decouple hardware from software, Intel remains Ericsson’s sole silicon partner for production-grade deployments.
This hardware lock-in was underscored during Ericsson’s recent London event, where documentation confirmed “commercial support” exclusively for Intel, while AMD, Arm, and NVIDIA remain relegated to “prototype support.” Despite years of industry rhetoric regarding silicon diversity in the vRAN ecosystem, tangible progress remains stalled. Furthermore, the integration of AI into RAN software introduces new layers of complexity that may further entrench hardware dependencies.
Industry observers remain skeptical of Ericsson’s ambition for a “unified software stack” across heterogeneous hardware platforms. While hardware-software disaggregation is achievable in the higher layers (L2/L3), Layer 1 (L1)—the most compute-intensive portion of the stack—remains heavily optimized for specific silicon. Ericsson’s initial strategy relied on the portability of L1 code across x86 architectures (via AMD) or the adoption of Arm’s SVE2 (Scalable Vector Extension) to match Intel’s AVX-512 capabilities. However, achieving high-performance parity across these platforms without significant refactoring remains a significant engineering hurdle.
A critical bottleneck in PHY Layer (L1) processing is Forward Error Correction (FEC), which traditionally necessitates dedicated hardware acceleration. Ericsson initially addressed this using a lookaside acceleration model, offloading FEC tasks to discrete PCIe-based Intel accelerators. In recent iterations, Intel has moved toward a more integrated System-on-Chip (SoC) approach, embedding the accelerator directly onto the CPU die (e.g., vRAN Boost).
The primary challenge for Ericsson lies in achieving silicon parity across the AMD and NVIDIA ecosystems. While AMD’s FPGA-based accelerators have faced scrutiny regarding power efficiency, NVIDIA’s GPU-based offloading was previously viewed as cost-prohibitive for standard FEC. However, the rise of AI-RAN has recalibrated these economic models, as telcos explore the dual-use potential of GPUs for both RAN and AI workloads. Emerging platforms, such as Google’s Tensor Processing Units (TPUs), have also been identified by Ericsson leadership as viable long-term options.
Despite ambitions for a unified “single software track,” Ericsson’s technical roadmap suggests a more nuanced reality. While L2 and higher layers aim for a universal codebase across hardware platforms, L1 necessitates concurrent feature development and platform-specific tailoring. As CTO Erik Ekudden noted, maximizing the efficiency of advanced accelerators requires a degree of software customization that challenges the ideal of total hardware-software disaggregation.
Ericsson executives are keen to avoid what Executive VP Per Narvinger describes as a “native implementation,” which would create silicon vendor lock-in. To prevent that the company is prioritizing Hardware Abstraction Layers (HALs). Key initiatives include the adoption of the BBDev (Baseband Device) interface to decouple RAN software from underlying silicon. Furthermore, potential integration with NVIDIA’s CUDA platform is being evaluated to provide the necessary abstraction for heterogeneous compute environments, though this remains contingent on broader industry standardization.
Ericsson’s AI strategy mirrors this modular approach. By leveraging AI as a functional abstraction layer, the company aims to simplify software portability across diverse platforms while maintaining AI control loops for real-time network management. Unlike competitors tethered to high-TDP GPUs, Ericsson maintains that AI-RAN is commercially viable on general-purpose and purpose-built silicon. Recent London showcases highlighted AI-driven gains in spectral efficiency, channel estimation, and beamforming without external acceleration. A production-ready AI-native link adaptation model recently delivered a 10% spectral efficiency improvement in field tests and is now integrated into the latest baseband portfolio.
As for radios—a domain less susceptible to full virtualization—Ericsson is embedding Neural Network Accelerators (NNA) directly into its radio-unit ASICs. These programmable matrix cores are optimized for Massive MIMO inference, enabling sub-millisecond beamforming and channel estimation while adhering to strict site power envelopes. New AI‑ready radios, feature Ericsson custom silicon with neural network accelerators. They are said to boost on‑site AI inference capabilities in Massive MIMO radios, enabling real‑time optimization and full stack, fully distributed AI.
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References:
https://www.lightreading.com/5g/ericsson-does-ai-ran-minus-nvidia-in-push-for-5g-silicon-freedom
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Analysis: Nokia and Marvell partnership to develop 5G RAN silicon technology + other Nokia moves
China gaining on U.S. in AI technology arms race- silicon, models and research
Marvell shrinking share of the RAN custom silicon market & acquisition of XConn Technologies for AI data center connectivity
Intel FlexRAN™ gets boost from AT&T; faces competition from Marvel, Qualcomm, and EdgeQ for Open RAN silicon
China’s telecom industry rapid growth in 2025 eludes Nokia and Ericsson as sales collapse
According to a Chinese government update, “Telecommunications business volume and revenue grew steadily, mobile internet access traffic maintained rapid growth, and the construction of network infrastructure such as 5G, gigabit optical networks, and the Internet of Things was further promoted.”
Figure 1. Cumulative growth rate of telecommunications service revenue and total telecommunications service volume
There were 4.83 million 5G base stations in service in China at the end of November 2025, an increase of 579,000 since late 2024 and 37.4% of the total number of mobile base stations in China. In one year, China claims to have added more 5G base stations than Europe has installed since the 5G technology was first put into service.
The total number of mobile phone users of the top four Chinese telcos (China Mobile, China Telecom, China Unicom, China Broadcasting Network) reached 1.828 billion, a net increase of 38.54 million from the end of last year. Among them, 5G mobile phone users reached 1.193 billion, a net increase of 179 million from the end of last year, accounting for 65.3% of all mobile phone users.
Meanwhile, the total number of fixed broadband internet access users of the three state owned telecom operators (China Mobile, China Telecom and China Unicom) reached 697 million, a net increase of 27.12 million from the end of last year. Among them, fixed broadband internet access users with access speeds of 100Mbps and above reached 664 million, accounting for 95.2% of the total users; fixed broadband internet access users with access speeds of 1000Mbps and above reached 239 million, a net increase of 32.52 million from the end of last year, accounting for 34.3% of the total users, an increase of 3.4 percentage points from the end of last year.
The construction of gigabit fiber optic broadband networks continues to advance. As of the end of November, the number of broadband internet access ports nationwide reached 1.25 billion, a net increase of 48.11 million compared to the end of last year. Among them, fiber optic access (FTTH/O) ports reached 1.21 billion, a net increase of 49.42 million compared to the end of last year, accounting for 96.8% of all broadband internet access ports. As of the end of November, the number of 10G PON ports with gigabit network service capabilities reached 31.34 million, a net increase of 3.133 million compared to the end of last year.
The penetration rate of gigabit and 5G users continued to increase across all regions. As of the end of November, the penetration rates of fixed broadband access users with speeds of 1000Mbps and above in the eastern, central, western, and northeastern regions were 34.6%, 33.8%, 35.8%, and 28.5%, respectively, representing increases of 3.4, 2.6, 4.1, and 4.9 percentage points compared to the end of last year; the penetration rates of 5G mobile phone users were 64.9%, 65.9%, 65.1%, and 65.9%, respectively, representing increases of 8.2, 8.7, 8.8, and 9.6 percentage points compared to the end of last year.
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Separately, Light Reading reports that Ericsson and Nokia sales of networking equipment to China have collapsed.
Ericsson recently published earnings release for the final quarter of 2025 puts China revenues at just 3% of total sales last year. This would equate to revenues of 7.1 billion Swedish kronor (US$798 million). Based on a rounding range of 2.5% to 3.4%, it works out to be between SEK5.92 billion ($665 million) and SEK8.05 billion ($905 million) – down sharply compared with the SEK10.2 billion ($1.15 billion) Ericsson made in 2024, according to that year’s Ericsson annual report.
Nokia does not break out details of revenues from mainland China, instead lumping them together with the sales it generates in neighboring Hong Kong and Taiwan. But this “Greater China” business is in decline. Total annual revenues – which include Nokia’s sales of fixed, Internet Protocol and optical network products, as well as 5G – slumped from almost €2.2 billion ($2.6 billion) in 2019 to around €1.5 billion ($1.8 billion) in 2020, before creeping back up to nearly €1.6 billion ($1.9 billion) by 2022. Two years later, they had fallen to about €1.1 billion ($1.3 billion).
Bar Chart Credit: Light Reading
Nokia has recently indicated the complete disappearance of its China business. “Western suppliers, which is only us and Ericsson, have 3% market share now in China and it’s been coming down, and we are going to be excluded from China for national security reasons,” said Tommi Uitto, the former president of Nokia’s mobile networks business group, at a September press conference in Finland also attended by Justin Hotard, Nokia’s CEO. It implies China’s government is now treating the Nordic vendors in the same way Europe and the U.S. are banning Huawei and ZTE networking equipment.
Nokia revealed in its latest earnings update that Greater China revenues for 2025 had fallen by another 19%, to €913 million ($1.08 billion) – just 42% of what Nokia earned in the region seven years earlier. In the last few years, moreover, Nokia has cut more jobs in Greater China than in any other single region. While figures are not yet available for 2025, the Greater China headcount numbered 8,700 employees in 2024, down from 15,700 in 2019.
Ericsson has significantly reduced its China operations following greatly reduced 5G market share. In September 2021, the company consolidated three operator-specific customer units into a unified structure, impacting several hundred sales and delivery roles within its ~10,000-person local workforce. This followed the divestment of a Nanjing-based R&D center (approx. 650 employees), aligning with strategic pivots away from legacy 2G-4G technologies. The company’s total workforce in Northeast Asia plummeted from about 14,000 in mid-2021 to roughly 9,500 at the end of last year, according to Ericsson’s financial statements.
Exclusion from China would leave Ericsson and Nokia on the outside of the world’s most promising 6G market in 2030. That would intensify concern about a bifurcation of 6G into Western and Chinese variants of IMT 20230 RIT/SRIT standard and the 3GPP specified 6G core network.
References:
https://www.miit.gov.cn/gxsj/tjfx/txy/art/2025/art_7514154ec01c42ecbcb76057464652e4.html
https://www.lightreading.com/5g/ericsson-and-nokia-see-their-sales-in-china-fall-off-a-cliff
China’s open source AI models to capture a larger share of 2026 global AI market
Goldman Sachs: Big 3 China telecom operators are the biggest beneficiaries of China’s AI boom via DeepSeek models; China Mobile’s ‘AI+NETWORK’ strategy
China Telecom’s 2025 priorities: cloud based AI smartphones (?), 5G new calling (GSMA), and satellite-to-phone services
China ITU filing to put ~200K satellites in low earth orbit while FCC authorizes 7.5K additional Starlink LEO satellites
China gaining on U.S. in AI technology arms race- silicon, models and research
SoftBank and Ericsson-Japan achieve 24% 5G throughput improvement using AI-optimized Massive MIMO
SoftBank Corp. and Ericsson Japan K.K. have announced a successful demonstration and deployment of an AI-powered, externally controlled optimization system for Massive MIMO, resulting in a 24% improvement in 5G downlink throughput, increasing speeds from 76.9 Mbps to 95.5 Mbps during periods of high traffic fluctuation.
- Dynamic Beam Patterns: The system automatically adjusts horizontal and vertical beam patterns every minute based on real-time user distribution.
- Packet Stalling Mitigation: By reacting to sudden traffic surges (e.g., during fireworks or concerts), the AI helps prevent “packet stalling,” where data transmission typically freezes due to congestion.
- Commercial Deployment: Following the successful trials at Expo 2025, SoftBank and Ericsson have begun deploying this AI-based system at other large-scale event venues, including major arenas and dome-type facilities in the Tokyo metropolitan area, to manage heavily fluctuating traffic patterns.
Overview of the System:
- An external control device (server) uses user distribution data collected from base stations at one-minute intervals to automatically determine event occurrence using AI
- Dynamically and automatically optimizes the horizontal and vertical coverage patterns of Massive MIMO base stations
Overview of demonstration at Expo 2025:
An AI model was constructed using performance results obtained when multiple coverage patterns were changed in advance as training data. Based on user distribution-related data such as Massive MIMO beam estimation information*2 acquired by an external control device from base stations at one-minute intervals, the system automatically determined event occurrence status and switched base station coverage patterns to optimal configurations.
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ABOUT SOFTBANK:
Guided by the SoftBank Group’s corporate philosophy, “Information Revolution – Happiness for everyone,” SoftBank Corp. (TOKYO: 9434) operates telecommunications and IT businesses in Japan and globally. Building on its strong business foundation, SoftBank Corp. is expanding into non-telecom fields in line with its “Beyond Carrier” growth strategy while further growing its telecom business by harnessing the power of 5G/6G, IoT, Digital Twin and Non-Terrestrial Network (NTN) solutions, including High Altitude Platform Station (HAPS)-based stratospheric telecommunications. While constructing AI data centers and developing homegrown LLMs specialized for the Japanese language, SoftBank is integrating AI with radio access networks (AI-RAN), with the aim of becoming a provider of next-generation social infrastructure. To learn more, please visit https://www.softbank.jp/en/corp/
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References:
SoftBank’s Transformer AI model boosts 5G AI-RAN uplink throughput by 30%, compared to a baseline model without AI
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Ericsson’s 5G Advanced location based services (LBS) offering is a comprehensive suite of innovations designed to redefine location-based services across commercial 5G Standalone (SA) networks. Set for release in Q1 2026, it makes Ericsson the leader in 5G positioning technology, offering a scalable and fully integrated solution on top of Ericsson’s dual-mode 5G Core network.
By embedding positioning as a core 5G SA network capability, Ericsson 5G Advanced location services enables Communications Service Providers (CSPs) to monetize precise location services and expand beyond traditional mobile offerings into verticals such as manufacturing, healthcare, public safety, automotive, drones, and more.
Key benefits:
- High Accuracy: Down to sub-meter for indoor and sub-10 cm for outdoor positioning, enabling precise tracking
- Scalability: Scalable, precise positioning for outdoor applications (automotive, agriculture, drones)
- Seamless Indoor/Outdoor Coverage: Unified 5G positioning technology for both environments.
- Developer & Device Friendliness: No need for device-side apps; improved battery life compared to satellite-based solutions
- Support for Large-Scale Use Cases: Enables massive geofencing, population density analysis, and tracking use cases.
Monica Zethzon, Head of Core Networks, Ericsson, says: “With the launch of 5G Advanced Location Services we are evolving the value of 5G Standalone networks. This innovation gives CSPs the precision and scalability to create differentiated services based on location capabilities.”
Caroline Gabriel, Partner at Analysys Mason, says: “Ericsson’s integrated approach to indoor and outdoor positioning sets a new benchmark in the industry. It addresses critical pain points for operators and enterprises, particularly in sectors where location accuracy is mission-critical.”
The global market for 5G positioning is in its early stages but poised for rapid growth, driven by demand for enhanced precision in diverse sectors. Ericsson’s solution responds to this demand with scalable, developer-friendly capabilities that improve device battery life compared to legacy systems.
This launch further strengthens Ericsson’s location solutions based on Real-Time Kinematics technology, with related devices from Ericsson planned for Q1 2026.
Photo Credit: Ericsson
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- Integrated Positioning: Positioning is built into the 5G Standalone (SA) architecture, moving beyond traditional GPS reliance.
- High Accuracy & Efficiency: New techniques improve accuracy (e.g., bandwidth aggregation, carrier-phase measurements) and reduce power consumption for devices.
- AI/ML Integration: Artificial Intelligence/Machine Learning is applied to enhance positioning accuracy, especially for challenging scenarios like beyond-visual-line-of-sight (BVLOS).
- Support for New Devices/Apps: Enables precise tracking for wearables, industrial sensors (RedCap), augmented reality (AR), drone control, and smart grids.
- Beyond-Line-of-Sight (BVLOS): Focus on reliable positioning for industrial and public safety applications where line-of-sight isn’t guaranteed.
- Reduced Power: Solutions target lower power usage, crucial for IoT devices.
- Release 18 (5G Advanced Start): Finalized mid-2024, introduced major LBS enhancements, including RedCap positioning, bandwidth aggregation, and carrier-phase support.
- Release 19 (Ongoing): Continues the evolution, extending LTM (L1/L2-triggered Mobility) and further exploring AI/ML for mobility and positioning.
- Release 20 & Beyond: Will build on these foundations, further evolving towards 6G capabilities.
References:
https://www.ericsson.com/en/press-releases/2026/1/5g-advanced-location-services
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