Author: Alan Weissberger
Analysis: Nokia’s new AI-RAN platform and Standalone AI-RAN node with Nvidia GPUs
Nokia today announced what it describes as the first commercial AI-RAN platform, signaling a potentially significant inflection point in radio access network architecture. As AI workloads increasingly influence network design and operations, service providers are under pressure to deliver higher capacity, improved cost efficiency, and faster service innovation without depending on traditional hardware refresh cycles. Nokia’s AI-RAN platform is positioned to address these requirements by extracting greater uplink and downlink performance from existing spectrum and radio assets, while enabling a transition toward AI-native network architectures through software-centric evolution. Key take-aways:
- Nokia launches the industry’s first commercial AI-RAN platform, turning AI-RAN from vision into reality and providing a practical path to AI-native networks.
- Built on Nokia’s AI-native anyRAN software and NVIDIA’s Aerial AI-RAN platform, it will deliver more than 100% spectral efficiency gains by 2028, doubling the capacity of existing spectrum assets.
- Nokia’s anyRAN software will support three new accelerated computing baseband platforms. In addition, its existing portfolio will be fully ORAN compliant so operators can modernize at their own pace.
“AI-RAN is the biggest innovation in radio in decades. AI-RAN makes the network intelligent, extends AI into the physical world, and allows telcos to get more from their existing infrastructure, including a software upgrade path to 6G. Nokia’s anyRAN software, powered by NVIDIA’s Aerial AI-RAN platform, unlocks greater performance from the spectrum operators already have and can be deployed with existing Nokia or ORAN-compliant radio units. For operators, that means more performance, better returns and faster delivery of new services,” said Justin Hotard, President and Chief Executive Officer at Nokia.
The platform is built on Nokia’s AI-native network architecture and leverages NVIDIA’s accelerated computing stack to enable AI-driven radio optimization. Initial results indicate spectral efficiency gains exceeding 20% through AI-enhanced radio resource management and signal processing techniques. Nokia’s roadmap targets up to 50% gains by 2027 and greater than 100% by 2028, with the objective of increasing capacity in dense deployments while reducing cost per bit and improving user experience.
“Telecommunications is entering the AI era — the radio access network is the next AI infrastructure,” said Jensen Huang, CEO and founder of NVIDIA. “Together with Nokia, we are bringing NVIDIA CUDA and AI into the baseband, transforming RAN into a planet-scale AI computer. This is a generational shift for operators — unlocking more capacity and efficiency from today’s spectrum while creating the foundation for new AI services and the 6G era.”
Image credit: Nokia
A key element of the offering is a software subscription model that enables operators to access ongoing AI-driven enhancements, feature updates, and performance improvements independent of hardware upgrade cycles. Nokia expects pilot deployments to begin by the end of the year, with broader commercial availability targeted for 2027. The roadmap incorporates NVIDIA’s programmable merchant silicon to support continued performance scaling and feature evolution.
“Nokia’s AI-RAN launch represents an important step in bringing AI-RAN from industry vision to commercial reality. The addition of the new AI-RAN node alongside the AirScale capacity plug-in unit and cloud-native deployment options gives operators practical choices for adopting AI-native networks based on their existing infrastructure and transformation goals. By combining AI-accelerated computing with a software-defined architecture and a clear product roadmap, Nokia is helping operators unlock greater capacity, improve network economics and accelerate the transition toward AI-native RAN,” commented Rémy Pascal, Practice Leader, Mobile Infrastructure at Omdia.
Light Reading’s Iain Morris wrote:
Nokia’s vision, outlined during an exclusive interview with Light Reading, is that the Nvidia-based hardware products announced today and available from next year will potentially last customers deep into the 6G era. That cannot be said of the latest Nokia hardware in commercial use, based on custom silicon provided by Marvell Technology, according to Atkinson. The same would be true of the latest hardware from rival Ericsson, he believes: “It will get them into 6G, but it won’t see them long into 6G.”
The Nokia pivot to GPUs sets up a riveting clash between the two Nordic companies. Ericsson remains firmly attached to its own custom silicon, and its top executives think writing code for CUDA, Nvidia’s software platform, could make Nokia a victim of “vendor lock-in,” imprisoned by a single vendor’s hardware.
For telcos interested in general-purpose processors and the greater freedom they promise, Ericsson instead offers a set of “virtual” RAN products. While based today on Intel’s central processing units (CPUs), the same software can run on CPUs from other chipmakers after minimal tweaks, according to Ericsson. Only a small amount of code remains hardware-dependent, it says.
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Nokia’s AI-RAN platform is based on a common, software-defined architecture integrating its anyRAN software with NVIDIA’s accelerated computing. The platform supports 4G, 5G, and forward evolution toward 6G, while maintaining compliance with Open RAN specifications to enable multi-vendor interoperability. Operators can select from multiple deployment options aligned with their installed base and transformation strategy, while leveraging a unified software roadmap.
For network operators with existing Nokia AirScale deployments, the introduction of a GPU-accelerated capacity plug-in unit provides a relatively low-friction upgrade path. This approach integrates accelerated computing into the current RAN footprint, enabling step-function capacity improvements while preserving prior infrastructure investments. Nokia also highlights support for AI-optimized merchant silicon, including contributions from partners such as Marvell, reflecting a broader ecosystem strategy for AI-RAN evolution.
Nokia is also introducing a GPU-based standalone AI-RAN node designed for flexible deployment across diverse network environments. The platform supports 4G, 5G, and future 6G workloads and can be deployed as a discrete node, in clustered configurations, or integrated with existing AirScale systems as a logical baseband. This enables scalable deployment of AI-native capacity while maintaining architectural flexibility.
For network operators pursuing cloud-native RAN strategies, Nokia’s AI-RAN capabilities extend to GPU-enabled COTS server platforms delivered through ecosystem partners. This approach supports deployment on standardized, accelerated infrastructure while aligning with open and secure supply chain principles. It combines cloud-native operational models with the performance requirements of AI-intensive RAN workloads.
Nokia’s AI-RAN introduces a shift from hardware-centric lifecycle management to a software-driven innovation model. Through its subscription-based framework, operators gain continuous access to evolving AI algorithms, spectral efficiency enhancements, and network optimization capabilities. This model enables ongoing improvements in performance, efficiency, security, and resilience without requiring discrete hardware upgrades, thereby improving total cost of ownership and extending asset lifecycles.
By combining AI-accelerated computing, software-defined RAN architecture, and an open ecosystem approach, Nokia’s AI-RAN platform is intended to provide operators with a scalable pathway to increased capacity, improved economic performance, and continuous innovation as networks evolve toward the 6G era.
References:
https://www.nokia.com/radio-access/ai-ran/
https://www.lightreading.com/6g/nokia-says-long-term-6g-is-not-doable-without-nvidia
European Consortium 5G NTN transmission paves the way for standards based direct to device (D2D) connectivity
Executive Summary:
Satellite connectivity advanced meaningfully this past week as the European Trantor consortium reported the first 5G NTN transmission over a Hispasat satellite on July 8th. This is an important step because it moves NTN from proof-of-concept demonstrations toward a standards-based implementation path aligned with 3GPP’s non-terrestrial network work. In telecom terms, interoperability is the real gating factor: NTN only becomes architecturally relevant if it can integrate cleanly with 3GPP-defined access, mobility, and service procedures rather than remaining a proprietary satellite overlay.
From a technical perspective, the signal here is that NTN is evolving beyond its initial role as satellite backhaul for remote coverage and into direct-to-device (D2D) access using standard cellular devices and network functions. That transition brings a new set of engineering challenges: synchronization and timing, mobility management, spectrum coordination, terminal power efficiency, and seamless handover between terrestrial and non-terrestrial domains. The “pre-6G” label is appropriate because these developments point to a converged terrestrial-plus-space access architecture, not a standalone satellite niche.
Sanford Bernstein’s warning that direct-to-device satellite can increase competitive pressure on terrestrial network operators is credible because it erodes one of the incumbents’ traditional advantages: exclusive control over wide-area coverage. If NTN systems can support messaging, emergency connectivity, and eventually broader mobile services, then operators face substitution pressure in segments where they historically monetized coverage gaps, roaming resilience, and service continuity. This does not displace terrestrial networks, but it does reduce the ability of carriers to price certain coverage and resilience attributes as premium differentiators.
The most likely industry response is partnership rather than confrontation. Mobile operators will probably position NTN as a complementary resilience layer for coverage extension, disaster recovery, IoT continuity, and premium service tiers, rather than as a replacement for terrestrial RAN investment. At the same time, vendors and standards bodies will continue pushing multi-orbit, multi-band, and multi-vendor interoperability as the condition for commercial viability. For editorial purposes, the key question is whether NTN matures as an operator-integrated extension of the mobile network or as an adjacent service layer that partially bypasses terrestrial incumbents.
3GPP Evolution to 6G:
Image Credit: Ericsson
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Key Technology Takeaways:
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The first 5G NTN transmission over a Hispasat satellite marks a meaningful step from lab validation to standards-aligned deployment.
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3GPP NTN work in Release 19 is the key enabler because interoperability, not just link feasibility, will determine commercial viability.
- ITU-R SWG 4B1 – Satellites in Next Generation Access Technologies will likely rubber stamp 3GPP NTN specifications which will then become ITU-R recommendations.
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NTN is evolving from satellite backhaul for remote coverage into direct device access for standard cellular endpoints.
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The hardest technical problems are shifting toward timing, mobility, spectrum coordination, device power efficiency, and seamless terrestrial/non-terrestrial handover.
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“Pre-6G” is the right framing because NTN is becoming part of a hybrid terrestrial-plus-space access architecture.
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Direct-to-device satellite services can pressure terrestrial operators by reducing their exclusive control over last-mile coverage and resilience.
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The most likely carrier strategy is partnership and bundling, using NTN for coverage extension, disaster recovery, and IoT continuity rather than full substitution.
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Multi-orbit, multi-band, and multi-vendor interoperability will be essential if NTN is to become a durable commercial platform.
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References:
https://www.3gpp.org/technologies/ntn-overview
https://www.ericsson.com/en/blog/2024/10/ntn-payload-architecture
AT&T/Ericsson Demonstrate 5G-Based ISAC for Drone Detection at World Cup Stadium
AT&T and Ericsson recently demonstrated the potential of 5G as a platform for integrated sensing and communications (ISAC) [1.] in support of critical infrastructure protection and public safety. The demonstration, conducted at a World Cup stadium near Dallas, TX highlights how cellular networks can evolve into dual-function systems that provide both connectivity and environmental sensing.
Note 1. Integrated Sensing and Communication (ISAC) is a flagship 6G/IMT 2030 capability that unifies mobile communication and environmental sensing into a single network. By using the same infrastructure, spectrum, and waveforms, 6G systems will act as spatially aware platforms. It allows networks to detect, track, and image objects while transmitting data. ITU-R officially designated ISAC as one of the six core usage scenarios in the IMT-2030 (6G) framework.
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In this trial, AT&T and Ericsson (its primary RAN equipment supplier) employed 5G-based network sensing to detect and track unmanned aerial vehicles (UAVs) operating at altitudes between 300 and 400 feet in authorized airspace as they approached AT&T Stadium in Arlington, Texas. The approach reflects a joint communication and sensing (JCAS) paradigm, in which existing radio access network (RAN) infrastructure is leveraged for situational awareness without the need for dedicated radar or sensing overlays.
Ericsson deployed Massive MIMO radios across multiple sites to establish a “multi-static sensing configuration,” enabling spatial diversity and improved detection performance. By combining “sensing-enabled radio transmissions with advanced signal processing and AI-enabled sensing algorithms,” the system detected, localized, and tracked drones in real time. This capability exploits the propagation characteristics of RF signals used for communication, enabling object detection and tracking within the coverage footprint of the network.
Although no match was scheduled during the demonstration, AT&T Stadium has been a primary venue during the tournament and will host the semi-final between France and Spain later this week, providing a representative high-density and security-sensitive deployment context.
Cellular Sensing for Drone Mitigation:
Image Credit: AT&T
Unauthorized UAV activity has posed ongoing challenges for public safety authorities during the tournament. On match days, drone operations are prohibited within a one-nautical-mile radius of stadiums and up to 1,000 feet above ground level, according to the Federal Aviation Administration.
Reporting from the tech demo in Arlington, NBC 5 DFW indicated that U.S. authorities have detected approximately 1,500 drones and confiscated more than 700 across World Cup venues, including 53 in the vicinity of AT&T Stadium.
AT&T and Ericsson position cellular-based sensing as a complementary capability to existing counter-UAV systems deployed by law enforcement. While the Arlington Police Department indicated it was not directly involved in the demonstration, it acknowledged ongoing evaluation of emerging technologies that could enhance future operational capabilities, according to NBC 5 DFW.
Quotes:
Ildefonso de la Cruz, senior principal analyst at Omdia (owned by Informa-UK), characterized the demonstration as strategically timed and situated, noting its alignment with global attention on the World Cup and upcoming large-scale events such as the 2028 Summer Olympic Games in Los Angeles. “This demonstration shows that robust cellular infrastructure is the foundation to build reliable next-generation critical services for public safety and other critical infrastructure verticals,” he stated.
“As networks evolve, the opportunity is not just to prepare for 6G someday, but to begin introducing important building blocks now,” said Dyon Agnew, SVP and Head of Customer Unit AT&T, Ericsson Americas. “This demonstration with AT&T shows a product roadmap in action: using advanced 5G capabilities today to explore how sensing and connectivity can work together, then evolving those capabilities over time as the path to 6G becomes clearer.”
“Integrated sensing is an important part of the road to 6G, and this work helps show how we can start bringing that future to life right now,” said Yigal Elbaz, SVP and Network CTO, AT&T. “By working with Ericsson, we are exploring how advanced wireless networks can add sensing capabilities to connectivity in ways that could support safer operations, smarter venues, and stronger customer experiences, while creating a path to evolve these capabilities responsibly over time.”
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Technical Takeaways:
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Demonstrates early-stage ISAC/JCAS capabilities using commercial 5G Massive MIMO infrastructure, with implications for 6G-native sensing architectures.
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Validates multi-static sensing configurations in cellular deployments, improving detection accuracy through spatial diversity.
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Highlights the role of AI-driven signal processing in extracting sensing information from communication waveforms.
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Suggests a pathway to cost-efficient sensing by reusing existing RAN assets, avoiding dedicated radar infrastructure.
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Reinforces the potential for cellular networks to support public safety and critical infrastructure monitoring as a value-added service layer.
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“Integrated sensing is an important part of the road to 6G, and this work helps show how we can start bringing that future to life right now,” said Yigal Elbaz, SVP and Network CTO, AT&T. “By working with Ericsson, we are exploring how advanced wireless networks can add sensing capabilities to connectivity in ways that could support safer operations, smarter venues, and stronger customer experiences, while creating a path to evolve these capabilities responsibly over time.”
What this roadmap will enable over time:
- Help event and facility teams improve planning and staffing by providing broader visibility into how vehicles move through large environments.
- Enhance coordination around temporary event infrastructure and logistics by adding network-based environmental awareness alongside connectivity.
- Support a wide-area drone awareness system for public-sector stakeholders, improving visibility into low-altitude drone activity as the low-altitude economy develops across cities and regions.
- Inform the evolution of future 5G and 6G capabilities as sensing and communications mature together for large venues, enterprises, governments, and public-sector environments.
Conclusions:
AT&T and Ericsson will continue exploring how sensing capabilities can be introduced pragmatically using existing network foundations, then advanced over time as standards, ecosystems, and market needs develop.
The goal is to help shape a practical path where future 6G/IMT 2030 capabilities are not treated as a distant leap, but as an evolution that can begin delivering value well before full 6G/IMT 2030 commercialization.
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References:
https://about.att.com/story/2026/att-ericsson-drone-detection.html
https://www.lightreading.com/5g/att-and-ericsson-demo-5g-sensing-for-drone-detection
https://www.linkedin.com/feed/update/urn:li:activity:7481460850159943680/ by Yigal Elbaz, of AT&T
https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030/pages/default.aspx
Analysis: Cohere’s $28M U.S. DoD FutureG ISAC contract; OTFS vs OFDM; 6G-NR/IMT 2030 RIT standards outlook
Analysis & Implications of the Communications Cybersecurity Information Sharing and Analysis Center (C2 ISAC)
Network X Americas: AT&T and Comcast reveal huge AI impact on network operations
Analysis: AT&T’s $250B network investment to advance U.S. connectivity
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Meta’s “Iris” AI Chip for MTIA: Implications for Telecom-Grade Optical Networking, DCI and High Capacity Ethernet Fabrics
Executive Summary:
According to Reuters, Meta Platforms (previously known as Facebook) plans to start manufacturing an artificial intelligence (AI) chip in September as part of its plan to boost overall computing power to 14 gigawatts in 2027. The social media firm’s data center chip, code-named “Iris,” is part of a four-generation project for Meta Training and Inference Accelerators (MTIA) that it will design in-house. The plan is to use custom-built silicon to improve the AI that powers its Facebook and Instagram social media platforms.
This move by Meta marks a pivotal moment in hyperscaler AI infrastructure strategy. This vertical integration play, executed through a multi-vendor supply chain (Broadcom design, TSMC manufacturing, Samsung RAM, SanDisk storage, Sumitomo fiber-optic equipment), has profound implications for telecom-grade optical networking, data center interconnect (DCI), and high-capacity Ethernet fabrics.
For IEEE Techblog readers focused on network architecture, standards, and infrastructure economics, the Meta MTIA story illuminates three critical trends:
- Hyperscaler silicon sovereignty as a cost and performance lever.
- Scaling challenge of 14 GW AI compute for optical transport and DCI.
- The emerging “Network Supercycle” driven by agentic AI workloads as per Cisco.
Image Credit: Meta Platforms
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The MTIA “Iris” Roadmap: Accelerating AI Silicon Cadence:
Meta’s Meta Training and Inference Accelerator (MTIA) program—now in its third generation with “Iris”—is pursuing an aggressive development cadence: a new chip every six months through 2027. This contrasts sharply with the industry-standard 12–18 month cadence for AI accelerators from NVIDIA, AMD, and even hyperscaler custom silicon programs (Google TPU, AWS Trainium)
Key MTIA milestones:
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MTIA v1 (2024): First-generation training/inference chip, proof-of-concept for Meta’s internal AI workloads
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MTIA v2 (early 2026): Performance and efficiency improvements, scaled deployment for Llama model training
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MTIA v3 “Iris” (September 2026): Production ramp, targeting higher throughput and lower power per inference
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MTIA v4 (2027): Next-generation architecture, expected to integrate advanced packaging, higher-bandwidth memory, and improved interconnect topologies
This cadence is not merely a technical achievement—it’s a strategic signal. Meta is betting that in-house silicon, even if initially less performant than NVIDIA’s H100/B100 or AMD’s MI300X, can deliver better total cost of ownership (TCO) when optimized for Meta’s specific workloads (Llama LLMs, recommendation systems, ad targeting).
Broadcom + TSMC: A Multi-Vendor Supply Chain Play:
Meta’s MTIA program is not a pure in-house design effort. The company is partnering with Broadcom for chip design and TSMC for advanced-node manufacturing (likely 5nm or 3nm process). This hybrid approach—hyperscaler architectural control with foundry and design partner execution—is becoming the dominant model for AI silicon:techcrunch
Why this matters: The multi-vendor AI chip supply chain is becoming a critical dependency for telecom-grade infrastructure. Broadcom’s involvement in both Meta’s MTIA and Apple’s $30B RF/FBAR deal (announced July 7–8, 2026) positions the company as a central player in both AI compute and 5G/6G RF ecosystems. For network architects, this means tracking Broadcom’s packaging, interconnect, and I/O roadmaps—not just NVIDIA’s.
14 GW Computing Target: The Optical and DCI Challenge:
Meta’s internal memo, reported by Reuters on July 9, 2026, outlines a target of 14 GW of computing capacity by 2027. To put this in perspective:
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14 GW ≈ 14 large nuclear power plants (each ~1 GW)
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Current hyperscaler data center power draw: ~50–100 GW total across all hyperscalers (2025 estimate)
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Meta’s 2025 data center power: ~10–12 GW (estimated)
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Growth rate: ~15–20% CAGR in hyperscaler power draw, but Meta is targeting a step-function increase
This is not just a compute scaling story—it’s an optical transport and DCI scaling story. Each GW of AI compute requires:
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High-bandwidth optical interconnect within data centers (400G/800G/1.6T Ethernet, optical circuit switching)
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Long-haul DCI between data center campuses (coherent 800G/1.6T, subsea cable systems)
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Power and cooling infrastructure (liquid cooling, direct-to-chip, immersion)
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Fiber-optic cabling and fiber-optic equipment (Sumitomo, Corning, Prysmian)
Optical Transport Implications:
Meta’s 14 GW target implies a massive buildout of optical infrastructure. Key considerations for IEEE ComSoc readers:
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Intra-DC Optical Fabrics: AI clusters (e.g., 10K–100K GPU/TPU/MTIA nodes) require non-blocking, low-latency optical fabrics. Meta’s 2025–2026 data center designs likely use:
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800G/1.6T optical transceivers (OSFP, QSFP-DD)
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Optical circuit switching (OCS) for dynamic bandwidth allocation (e.g., Oriole Networks PRISM, Google Apollo)
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Co-packaged optics (CPO) and near-packaged optics (NPO) for power efficiency
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Inter-DC DCI: Meta operates multiple data center campuses globally (U.S., Europe, Asia). Connecting these for AI workload distribution requires:
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Coherent 800G/1.6T DCI (400ZR/ZR+, OpenROADM)
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Subsea cable systems (e.g., Meta’s 2024–2026 investments in transatlantic and transpacific cables)
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Terragraph-inspired metro fiber for regional campus interconnects
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Fiber-Optic Equipment: Meta’s supply chain includes Sumitomo Electric for fiber-optic equipment, per the July 2026 memo. Sumitomo is a key supplier of:reuters
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Optical amplifiers (EDFA)
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Optical switches and ROADMs
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Fiber-optic cables and connectors
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Standards relevance: IEEE 802.3 (Ethernet), IEEE 802.1 (Time-Sensitive Networking), and ITU-T G.709 (OTN) are all directly impacted by Meta’s custom AI chip development program.
Cost Reduction vs. NVIDIA/AMD: The Vertical Integration Calculus & Why Hyperscalers Are Building Their Own AI Chips:
Meta’s MTIA program is part of a broader hyperscaler trend: vertical integration in AI silicon. The economic rationale is straightforward:
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NVIDIA H100/B100 pricing: $30K–$40K per GPU (2025–2026 list prices)
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AMD MI300X pricing: $20K–$30K per accelerator (2025–2026)
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Hyperscaler custom silicon TCO: 30–50% lower than NVIDIA/AMD at scale, despite lower peak performance
Meta’s internal analysis (per their July 2026 internal memo) likely shows that MTIA v3 “Iris” can deliver comparable inference throughput per dollar to NVIDIA H100 for Llama workloads, even if peak FLOPS are lower. This is because:
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Workload-specific optimization: MTIA is tuned for Meta’s LLM architectures (Llama 2/3/4), recommendation systems, and ad targeting—not general-purpose AI training.
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Supply chain control: Meta can negotiate better TSMC wafer pricing, avoid NVIDIA’s 20–30% gross margin, and reduce dependency on a single vendor.
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Software stack integration: Meta can optimize PyTorch, Llama inference libraries, and Meta’s internal AI frameworks for MTIA, reducing software overhead.
NVIDIA’s AI Chip “tax” vs. Hyperscaler Pushback:
NVIDIA’s dominance in AI accelerators (80–90% market share in 2025) has created what hyperscalers call the “NVIDIA tax”: premium pricing, limited supply, and software lock-in (CUDA ecosystem). Meta’s MTIA, Google’s TPU, Amazon’s Trainium, and Microsoft’s Maia are all attempts to reduce this dependency.
This is analogous to the telecom industry’s historical pushback against Cisco/Juniper proprietary switching ASICs. Open networking (Barefoot Tofino, Broadcom StrataXGS, P4 programmability) and disaggregated hardware (white-box switches, SONiC NOS) emerged as responses. AI silicon is following a similar path: disaggregation, open software stacks, and multi-vendor supply chains.
Full AI Infrastructure Stack Diversification: Samsung, SanDisk, Sumitomo:
Meta’s July 2026 memo outlines a fully diversified AI infrastructure stack:
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AI accelerators: Meta MTIA (Broadcom design, TSMC fab)
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DRAM: Samsung (HBM3/HBM3e for high-bandwidth memory)
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Storage: SanDisk (NVMe SSDs, QLC/TLC NAND for model checkpoints and data lakes)
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Fiber-optic equipment: Sumitomo (optical amplifiers, switches, cables)
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Networking: Broadcom (Ethernet switches, NICs), potentially NVIDIA (Spectrum-X, Quantum InfiniBand for some clusters)
This diversification is not just about cost—it’s about supply chain resilience. The 2020–2023 chip shortage, U.S.-China trade tensions, and Taiwan geopolitics have made hyperscalers acutely aware of single-vendor risk.
Telecom relevance: This mirrors the telecom industry’s shift from Cisco/Juniper monolithic routers to disaggregated white-box switches, open optical line systems, and multi-vendor RAN (O-RAN, vRAN). The AI infrastructure stack is undergoing a similar transformation.
The “Network Supercycle” Narrative: AI Compute as a WAN Traffic Driver:
Cisco executives have framed agentic AI workloads as driving a new infrastructure investment wave, with AI inference projected to account for ~25% of total WAN traffic by 2035. Meta’s 14 GW target is a concrete manifestation of this thesis.
Key implications for WAN and DCI:
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Bursty, Low-Latency Uplink Traffic: Agentic AI (e.g., autonomous coding agents, multi-agent collaboration) requires high uplink capacity, low latency, and guaranteed connectivity—exactly the traffic patterns Ookla’s July 2026 report highlighted as stressors for 5G networks.
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East-West DCI Traffic: AI training and inference workloads require massive data movement between storage, compute, and memory across data center campuses. This drives demand for:
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Coherent 800G/1.6T DCI
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Optical circuit switching for dynamic bandwidth allocation
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Subsea cable systems for intercontinental AI workload distribution
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Token/Byte Monetization: Huawei’s July 2026 AI-centric network vision includes “token/byte” monetization strategies for AI-driven services in the upper-6 GHz band. Meta’s AI infrastructure buildout is the supply-side enabler for this demand-side monetization.techblog.comsoc+1
Nokia’s “Physical AI” Warning:
Nokia’s “Physical AI” study (covered in earlier Techblog posts) warns that high-volume, low-latency uplink traffic from physical AI applications (e.g., robotics, autonomous systems) may require a fundamental RAN redesign. Meta’s 14 GW target is a parallel data center-side manifestation of this trend: AI workloads are reshaping both RAN and DCI/optical architectures.techblog.comsoc+1
Standards and Interoperability:
Meta’s MTIA “Iris” and 14 GW target have direct implications for several IEEE and standards activities:
IEEE 802.3 (Ethernet):
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800G/1.6T Ethernet: IEEE 802.3df (800G/1.6T) and IEEE 802.3dj (1.6T/3.2T) are critical for AI cluster fabrics.
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Power over Ethernet (PoE) for AI racks: Higher-power PoE standards may be needed for AI accelerator racks and liquid-cooled systems.
IEEE 802.1 (Time-Sensitive Networking):
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Deterministic Ethernet for AI: Low-latency, jitter-free traffic for AI inference may require TSN profiles or new deterministic Ethernet extensions.
IEEE 802.15 (Wireless Personal Area Networks):
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AI-native wireless for edge inference: Meta’s MTIA may eventually extend to edge inference (e.g., AR/VR, metaverse), requiring low-power, high-bandwidth wireless standards.
ITU-T and OIF:
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Coherent DCI: ITU-T G.709 (OTN), G.709.x (coherent OTN), and OIF 400ZR/ZR+ are critical for inter-DCI.
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Open optical line systems: OpenROADM, OpenCable, and disaggregated optical line systems are relevant for hyperscaler DCI builds.
O-RAN and AI-RAN Alliance:
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AI-for-RAN vs. AI-on-RAN: Meta’s AI infrastructure could eventually support AI-on-RAN workloads (running AI inference on RAN/edge infrastructure), aligning with the AI-RAN Alliance’s vision.
Competitive Landscape – How Meta’s MTIA Compares:
Key takeaway: Meta’s MTIA is not the most performant AI accelerator, but it’s part of a broader hyperscaler strategy to reduce NVIDIA dependency, control TCO, and optimize for specific workloads.
Conclusions – The AI Infrastructure Stack as a Telecom-Grade Opportunity:
Meta’s MTIA “Iris” chip and 14 GW computing target are not just hyperscaler news—they are telecom-grade infrastructure news. For IEEE ComSoc readers, the implications are clear:
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Optical transport and DCI will scale dramatically to support 14 GW of AI compute, creating demand for 800G/1.6T coherent optics, optical circuit switching, and subsea cable systems.
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Hyperscaler silicon sovereignty is reshaping the AI accelerator market, with direct implications for Broadcom, TSMC, and the broader semiconductor supply chain.
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The “Network Supercycle” is real, driven by agentic AI workloads that require high uplink capacity, low latency, and guaranteed connectivity.
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Standards bodies (IEEE, ITU-T, OIF, O-RAN) must track AI infrastructure trends to ensure interoperability, performance, and cost efficiency.
For telecom network architects, optical engineers, and standards professionals, the Meta MTIA story is a call to action: AI infrastructure is the next frontier for telecom-grade networking. The question is not whether telecom and AI will converge—it’s how quickly and effectively the industry can adapt.
References:
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
Ookla: AI workloads will force changes in 5G mobile network infrastructure
Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic
Ookla: AI platform reliability decreases as outages surge
Huawei’s AI-Centric Network Vision: Six Imperatives for the Next Decade; Critical Questions for IEEE Techblog Community
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
Ookla: AI workloads will force changes in 5G mobile network infrastructure
Introduction:
Ookla’s latest research study examines how AI use cases will stress 5G mobile networks, relative to standard internet traffic. The report, based on Speedtest Intelligence® data across 22 markets, evaluates metrics like upload capacity, latency under load, and cloud infrastructure pathways (see graphs below). Using Speedtest 5G data from 2025 across 22 markets and 86 operators in North America, Europe, Asia Pacific, the Middle East, and Latin America, it measures upload capacity, latency under load, and the quality of the path to the cloud. It also shows where current 5G falls short of what AI actually demands.
Analysis:
Ookla’s report argues that 5G network evaluation is entering a new phase: raw download speed is no longer enough to describe user experience or network capability in an AI-driven era. The more relevant indicators are upload performance, latency, consistency, and resilience, because AI-heavy applications tend to be interactive, symmetric, and sensitive to delay. The report’s timing is important because it reframes 5G from a consumer mobile broadband service into an infrastructure question for AI workloads. That shift matters for network operators, because uplink and latency have historically received less attention than headline download rates in market rankings and public messaging.
Here’s the lead-in (emphasis added):
“AI has changed what a good mobile network looks like, and the metric the industry has marketed for two decades — peak download speed — no longer predicts it. The networks that top the download charts are often not the ones best prepared for AI traffic. Whether an AI application feels instant or breaks depends in large part on how much a network can upload, how it holds up under load, and how consistently it reaches the cloud, and on those measures, different networks come out on top. This report rebuilds the industry’s download-led scorecard around what AI actually asks of a network, and shows where today’s 5G mobile networks are ready and where they fall short. AI traffic is not one thing. Text chat, conversational voice, multimodal and AR vision, generated video, and agentic activity each load the network differently, and most of them lean on parts of the network that download speed never tested. The change AI brings is less about raw capacity, which operators have expanded for years, than about the shape of the traffic — heavier on upload, always on, and bursty, rather than download-led and session-based.”
A few high-level takeaways for the U.S. market include:
- Although the United States ranks among the strongest on overall network performance, it sits at 5.1% for the proportion of network capacity allocated to the uplink, which is the lowest in the dataset.
- The U.S. upload share has contracted, declining from 8.0% to 5.1% between 2023 and 2025.
- The U.S. market top network operators fall short of the 20 Mbps upload target required for AR and multimodal AI.
- For baseline network responsiveness, the U.S. records a multi-server latency of 50.5 ms, missing the target of less than 50 ms for text-based large language models (LLMs).
Technical Implications:
Ookla’s framing implicitly favors 5G SA, 5G Advanced, and edge-assisted architectures, since these are the network generations most likely to improve latency determinism and support more efficient uplink behavior. It also suggests that future benchmarking should include workload-aware tests, not just conventional speed tests, because AI applications stress networks differently from video streaming or web browsing. The report has immediate relevance for markets where 5G download speeds look strong but uplink and latency remain weaker, because those networks may appear healthy under older metrics while still underperforming for AI use cases. That is a useful lens for comparing operators, especially where regulators and carriers are beginning to discuss AI readiness as part of national digital infrastructure strategy.
Conclusions:
With the rise of AI workloads, mobile network measurement is becoming application-specific. The central question is no longer just “How fast is 5G?” but “How well does the network support AI-era traffic patterns, especially interactive and uplink-heavy traffic?” In this new context, metrics such as upload capacity, latency consistency, and service resilience are becoming just as important as peak downlink speed. For operators, this implies that competitive advantage will increasingly depend on how well the network supports real-time, bidirectional, and latency-sensitive applications, rather than how well it performs on legacy consumer benchmarks.
Traditional speed tests still matter, but they are increasingly insufficient as a proxy for user experience in an AI-native environment. In practice, the networks that win will be those that can deliver symmetry, resilience, and predictable latency across real workloads, not merely impressive headline throughput.
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Ookla Charts:
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References:
https://www.ookla.com/articles/benchmarking-5g-ai-workloads-2026
https://www.ookla.com/s/media/2026/07/Ookla_Research_AI_network_readiness_07262.pdf
Ookla: AI platform reliability decreases as outages surge
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap
Ericsson’s June 2026 Mobility Report Highlights + AI impact on network traffic
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Ookla on the Global D2D Market
Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area
Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
Dell’Oro: Mobile Core Networks +15% in 2025; Ookla: Global Reality Check on 5G SA and 5G Advanced in 2026
Ookla: FWA Speed Test Results for big 3 U.S. Carriers & Wireless Connectivity Performance at Busy Airports
Huawei’s AI-Centric Network Vision: Six Imperatives for the Next Decade; Critical Questions for IEEE Techblog Community
The Case for AI-Native Networks:
At MWC Shanghai 2026 [1.], David Wang, Deputy Chairman of the Board and Rotating Chairman of Huawei, outlined a strategic roadmap for AI-native mobile networks, positioning artificial intelligence as the cornerstone of industry growth over the next decade.
Note 1. MWC Shanghai 2026 was held June 24–26, 2026 at the Shanghai New International Expo Center (SNIEC), with Huawei showcasing products and solutions in Hall N1.
Over the past 40 years, innovation in mobile technology from each generation to the next has been key to the industry’s success. “With each generation, we have pushed the limits of spectral efficiency and performance,” said Wang. “Network architecture has gradually flattened, with new application scenarios and services emerging left and right. This has consistently expanded the boundaries of communications, helping carriers translate network capabilities into commercial value,” he added.
Huawei argues that traditional telecom infrastructure built around data traffic is no longer sufficient. As the global digital ecosystem transitions toward real-time interactions with AI applications and intelligent agents, mobile and transport networks must be completely redesigned to support both communication and computing. According to Huawei, an AI-native architecture transforms networks from simple communication utilities into revenue-generating engines while helping operators transition to Level-4 and Level-5 network autonomy
Huawei’s Six Strategic Imperatives:
Wang identified six imperatives to guide the industry through the age of intelligence:
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Developing new services and capabilities for future mobile communications systems
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Integrating AI with mobile communications to build three distinct layers of intelligence
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Building network architecture for integrated satellite-ground communications
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Advocating for sustainable and future-oriented spectrum planning and allocation
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Clearly defining the specifications of AI-native core networks
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Exploring new business models and application scenarios for mobile services
Photo Credit: Huawei
Innovations Unveiled: Byte and Token Monetization:
Huawei released a portfolio of innovations targeting both services and infrastructure. On the services side, in collaboration with China’s three major carriers, the company announced advances in 5G-Advanced (5G-A) high-uplink and experience monetization, AI-powered business upgrades, and token monetization.
For infrastructure, Huawei launched the AI-centric target network, designed to enhance carrier competitiveness in byte and token monetization. This architecture comprises three layers:
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Basic Communications Network: A shift from traffic-centric to real-time interaction networking, offering guaranteed connectivity with high uplink and downlink capabilities alongside advanced QoS mechanisms.
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Computing Network: A transition from traffic transport to network-wide compute scheduling and supply, where “connecting to the network is equivalent to accessing compute.”
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AI Computing Infrastructure: High-performance, efficient compute with support for open-source and open ecosystems.
5G-Advanced: 100 Million Users and Beyond:
The global 5G-A (based on 3GPP Release 18) user base has surpassed 100 million. Huawei is now working with network operators worldwide to advance 5G-A experience monetization and integrate it into installed base operations, targeting mid-range and high-end user retention, ARPU growth, and sustainable revenue expansion.
High Uplink Speed: The New Frontier for AI Applications:
High uplink capacity is critical for token monetization. Emerging AI applications—such as multimodal AI glasses for real-time translation and augmented exhibitions—demand uplink speeds of 20 Mbps or higher. In 2026, leading carriers globally are piloting commercial high-uplink services with guaranteed peak speeds, latency, and universal uplink performance.
Upper-6 GHz: The Next Golden Band:
The proliferation of AI agents is expected to drive rapid growth in token services, requiring ultra-broadband networks with high uplink, high reliability, and low latency. Upper-6 GHz (U6 GHz) is positioned as the next-generation golden frequency band for this purpose.
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More than 20 countries and regions have designated U6 GHz for IMT, covering nearly 80% of the global population.
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2026 marks the commercial debut of U6 GHz, with the Middle East expected to deploy the world’s first commercial 5G-A network on U6 GHz.
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Select carriers in Hong Kong and Macao will also initiate commercial U6 GHz deployment.
AI-Native B2C and B2B Services:
Huawei plans to collaborate with carriers in Guangdong, Shanghai, Hebei, and other regions in 2026 to reengineer B2C and B2H services with AI, targeting consumer applications such as smart home assistants, personal communication assistants, and integrated consumer-home services. In the B2B segment, the focus is on AI computing services centered on compute-network integration, unlocking new business growth avenues.
Path to Level-4 Autonomous Networks:
Huawei is advancing AI-native technologies toward Level-4 autonomous networks by developing domain-specific intelligence. In 2026, the company will work with carriers to deploy domain-specific intelligence across wireless and transmission network domains in key regions. This will enable cross-domain synergy in maintenance, optimization, energy efficiency, and user experience, enhancing network quality and enabling differentiated products for high-speed rail, event venues, and campuses.
Critical Questions:
Huawei’s AI-centric network vision positions AI not as an incremental improvement to mobile networks but as a foundational network architecture. That vision raises several critical questions for the IEEE community and IEEE Techblog readers:
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Interoperability: How does Huawei’s AI-centric target network align—or conflict—with AI-RAN Alliance initiatives and O-RAN specifications?
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Vendor Comparison: How does Huawei’s AI-centric target network compare with Ericsson’s cloud RAN strategy and Nokia’s Altiplano/Corteca agentic AI platforms in terms of technical architecture and commercial viability?
- Specifications and Standards: What role will 3GPP and ITU-R play in standardizing AI-native core network specifications, particularly for token monetization and compute-network integration?
- Autonomous Networks: How do Huawei’s domain-specific intelligence approaches compare with vendor-neutral SMO/rApp ecosystems, and what are the implications for multi-vendor interoperability?
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Are carriers adequately prepared for the operational and cultural shifts required to transition from traffic monetization to token monetization?
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How will U.S./European regulatory frameworks (e.g. spectrum policy, AI governance, data sovereignty) shape the deployment of AI-native networks compared to China’s more centralized approach?
- Spectrum Policy: With Upper 6 GHz emerging as a key enabler for AI-driven token services, what are the regulatory and coexistence challenges, particularly in regions yet to designate Upper 6 GHz for IMT 2030? What will WRC 2027 decide?
Conclusions:
Huawei’s roadmap underscores the ICT industry’s rapid shift towards AI token monetization, positioning 5G-Advanced high-uplink, AI-native networks, and Upper 6 GHz spectrum as the foundational pillars for the next decade of growth. The success of this vision depends not only on technological feasibility but also on standards alignment, regulatory support, and carrier willingness to reinvent business models—a complex challenge that warrants close scrutiny from the IEEE technical community.
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References:
https://www.huawei.com/en/news/2026/6/mwcs-ai-byte-token
https://carrier.huawei.com/minisite/mwcs2026/en/
https://www.huawei.com/en/news/2026/6/mwcs-gsma-asac-5g-advanced
Huawei unveils AI Centric Network roadmap, U6 GHz products, 5G Advanced strategy and SuperPoD cluster computing platforms
Huawei FY2025: 2.2% YoY revenue increase; strategic pivot to AI and intelligent automotive solutions
Huawei, Qualcomm, Samsung, and Ericsson Leading Patent Race in $15 Billion 5G Licensing Market
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
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
Huawei Cloud Review and Global Sales Partner Policies for 2026
Huawei’s “FOUR NEW strategy” for carriers to be successful in AI era
Huawei to revolutionize network operations and maintenance
Huawei’s Electric Vehicle Charging Technology & Top 10 Charging Trends
Analysis: Cohere’s $28M U.S. DoD FutureG ISAC contract; OTFS vs OFDM; 6G-NR/IMT 2030 RIT standards outlook
Executive Summary:
Cohere Technologies has won a $28 million U.S. government contract funded by the FutureG Office within the U.S. Department of War (previously called the Defense Department or DoD) to develop a multi-waveform RAN prototype for integrated sensing and communications (ISAC), with Cohere’s Zak-OTFS as a core waveform alongside conventional OFDM [1]. The DoD award expands on a National Science Foundation VINES Phase 2 project. It will fund the development of a sovereign, mission-first ISAC capability that leverages existing and future commercial 5G/6G infrastructure to provide persistent aerial and ground surveillance while remaining indistinguishable from ordinary cellular traffic.
Mission: The contract is intended to turn commercial cellular infrastructure into a sensing layer for detection, tracking, and response applications, especially drone defense. Cohere says the prototype will support a multi-waveform software stack, a mobile test platform, and a layered inference sensing system that converts delay-Doppler data into real-time 3D tracks with classification and confidence scoring.
Cohere is positioning OTFS [2.] via its Pulsone/Zak-OTFS technology, as the waveform that better fits high-Doppler sensing and communications than plain OFDM. The company argues that OTFS carries information in the delay-Doppler domain, which is useful when the same signal must communicate and sense moving targets such as drones. If successful, this DoD funded ISAC demo could give OTFS a stronger credibility boost with standards bodies, equipment vendors, and defense customers, even if it does not immediately make OTFS a mainstream 3GPP waveform.
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Definitions and Comparison: OFDM vs. OTFS:
Note 1. OFDM (orthogonal frequency division multiplexing) is the 1D time-frequency workhorse that dominates WiFi, 4G and 5G because it is simpler, mature, and standardized by IEEE 802.11, ITU-R, and ETSI. OFDM maps data onto orthogonal subcarriers in the frequency domain, with symbols arranged over time and frequency; it is the basis of 4G LTE and is also used in 5G NR. OTFS maps data in the delay-Doppler domain and then spreads each symbol across the time-frequency plane, so the receiver sees a more invariant coupling to the channel under high Doppler and multipath.
Note 2. OTFS (orthogonal time frequency space) modulation is best thought of as a 2D, delay-Doppler-native waveform that Cohere has championed for highly mobile and doubly selective channels. It’s main advantage over OFDM is that it can make the channel look more stable to each symbol in fast-varying, high-Doppler environments, whereas OFDM excels when channels are relatively well-behaved and implementation efficiency matters most.
| Aspect | OTFS | OFDM |
|---|---|---|
| Best channel condition | High mobility, high Doppler, strong time variation | Quasi-stationary or modestly varying channels |
| Channel view | Delay-Doppler domain, more invariant symbol coupling | Time-frequency domain, channel varies per subcarrier/time slot |
| Equalization burden | Potentially easier in challenging channels, especially with mobility | Well understood and efficient in mainstream deployments |
| Standardization | Emerging, not yet the default cellular waveform comsoc+1 | Fully embedded in 4G/5G ecosystems |
| Maturity | Less mature, more research/prototype-driven | Very mature, widely deployed |
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Quotes and Capabilities:
“ISAC is a mission-first priority for the U.S. Department of War to defend against drone swarms. Due to guidance from leadership to execute rapidly, we required a partner with the right technology ready today,” said Tom Rondeau, Principal Director for FutureG, OUSW(R&E). “As a proven innovator with a demonstrated ability to build multi-waveform platforms, Cohere Technologies offered a clear path that we could move on immediately. Their OTFS modulation carries information directly in the sensing domain, delivering massive communications and sensing performance advantages in high-Doppler environments. This solution rapidly delivers critical ISAC capabilities while building on our ‘innovate-first’ posture, demonstrating the tremendous opportunity for innovation brought by the FutureG Open Centralized Unit Distributed Unit (OCUDU) platform.”
The multi-waveform system prototype is designed to provide detection, classification, tracking, and defeat-cueing of drone threats while operating in commercial spectrum bands, making it difficult for adversaries to distinguish sensing activity from normal cellular communications. In addition to core defense applications-including battlefield awareness, border security, and critical infrastructure protection-the program will identify parallel commercial use cases such as Advanced Air Mobility, smart city traffic management, and public safety. Work under the contract will be executed in close collaboration with government technical authorities and program partners.
“This ISAC contract from DoW represents a major milestone for Cohere and for the future of dual-use wireless technology,” said Ray Dolan, Chairman and CEO of Cohere Technologies. “By combining our Pulsone Technology with conventional Orthogonal Frequency-Division Multiplexing (OFDM) in a flexible, software-defined architecture, we can deliver high-performance sensing that is affordable, scalable, and operationally invisible-exactly what is needed to counter the growing threat of sophisticated drone and Unmanned Aerial Systems (UAS).”
Key Capabilities to Be Developed Under the Program:
- A Multi-Waveform physical layer running on an open, extensible software stack that supports both traditional 4G and 5G OFDM and Pulsone Technology using the Zak-OTFS waveform.
- A Mobile Test Platform enabling bi-static and multi-static sensing configurations.
- A Layered Inference Sensing system that converts raw Delay-Doppler data into real-time 3D tracks with classification and confidence scoring.
- Realistic outdoor test environments supporting mono-static, bi-static, and multi-static sensing.
- Compliance with the FutureG OCUDU platform and Zero Trust security requirements.
“This ISAC project award validates Cohere’s long-term vision of building sovereign, future-proof wireless infrastructure that serves both national security and commercial markets,” Dolan added. “We are proud to work alongside the FutureG Office and partners to deliver technology that strengthens our nation’s ability to sense and respond in contested environments.”
Cohere and the FutureG Office are considering commercial applications like Advanced Air Mobility traffic management, smart-city applications, and public safety are all named as potential adjacent markets, continuing the dual-use framing the Pentagon has increasingly favored for next-gen wireless R&D.
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Caveats and 6G NR/IMT 2030 RIT Standards Outlook:
The public information so far is largely company-announced, so the contract details, schedule, and technical requirements should be treated as initial disclosures rather than a full program specification. Also, the award appears to fund a prototype and operational demonstration, not a guaranteed path to standards adoption or mass deployment. While it is certainly possible for OTFS to be accepted as an IMT 2030 RIT (Radio Interface Technology), 3GPPs submission (via ATIS) to ITU-R WP5D will almost surely be OFDM based version of 6G NR.
ITU-R WP 5D has published the IMT-2030 roadmap and invited RIT-candidate submissions in the 02/2027 to 02/2029 window, so the process is still open to proposals. WP 5D’s role is to define the overall radio system aspects for IMT, but it does not itself guarantee adoption of any one waveform; candidates must survive technical performance requirements, evaluation criteria, and national/industry consensus. That means OTFS can still be proposed, but it would need to show clear benefits under the evaluation framework and broad support from proponents.
To get adopted, OTFS would need to prove more than attractive simulation results. It would need implementable receiver complexity, backward-compatible deployment pathways, acceptable PAPR and synchronization behavior, and a compelling story for mass-market devices, not just high-mobility or ISAC use cases. The literature and industry commentary generally position OTFS as strongest where Doppler and sensing matter most, which helps its case but may also narrow its scope.
The most realistic 6G/IMT 2030 standards scenario is:
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3GPP keeps OFDM-family waveforms as the baseline for 6G NR.
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OTFS remains active in research, patents, and trial implementations.
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OTFS is considered for targeted IMT-2030 RIT use cases such as high mobility, NTN, or integrated sensing and communications, rather than universal deployment.
Conclusions:
The U.S. government is backing Cohere’s OTFS-centered ISAC concept with real funding, and the strategic aim is to fuse communications and sensing in a way that is harder for drones or other threats to detect. For OTFS, that is a meaningful validation event, but still a prototype-stage win rather than proof of broad cellular standardization.
Cohere’s OTFS is not “better OFDM”; it is a different design point optimized for a harder channel model. OFDM remains the incumbent because it is standardized and efficient, but OTFS has a credible technical case where Doppler and channel variation are the real bottlenecks.
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About Cohere Technologies:
Cohere is the innovator of Universal Spectrum Multiplier (USM) software for 4G, 5G, and Multi-G. USM significantly improves mobile networks in any FDD and TDD spectrum band – and Pulsone™ Technology which is based on the Zak-OTFS waveform for ISAC and NTN. Pulsone is a trademark of Cohere Technologies. Cohere is headquartered in San Jose, Calif. (USA). www.cohere-tech.com
About the FutureG Office:
The FutureG Office within the Office of the Under Secretary of War for Research and Engineering is responsible for the strategic assessment and research and development of FutureG technologies to confer long-term economic, military and security advantages to the United States of America and its allies. By strengthening and developing relationships with private industry, academia, interagency and international allies and partners, the FutureG Office promotes the use of common, commercial standards for DoW operations, encourages adoption of open and interoperable technologies, and advances critical next-generation wireless network capabilities.
About the OCUDU Ecosystem Foundation:
The OCUDU Ecosystem Foundation, hosted by the Linux Foundation, is a global public-private initiative dedicated to building a commercial and research ecosystem around a production-ready, open source CU/DU stack. By fostering collaboration across the entire RAN lifecycle, from R&D to end-to-end integration, the OCUDU Ecosystem Foundation provides the reference architectures, conformance tooling, and “super blueprints” required to scale Open RAN from pilot projects to global production.
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References:
https://www.lightreading.com/6g/us-defense-dept-backs-6g-rival-to-tech-used-by-ericsson-and-nokia
Cohere Technologies bags $28M DoW deal to turn cell sites into drone-spotting sensors
https://rt.cto.mil/ddre-rt/science-and-technology-futures/futureg-home/
Multi-G Initiative to drive Open RAN Software Interfaces and increase innovation
Bloomberg: Meta to sell AI compute in a new cloud services offering
Disclaimer: Perplexity.ai was used for research resulting in this article.
Executive Summary:
According to Bloomberg, Meta Platforms is advancing plans to commercialize its internal AI infrastructure through a new cloud services offering, signaling a strategic expansion beyond its traditional hyperscale consumer platforms into the competitive AI infrastructure market. This initiative would position Meta alongside established cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, while also overlapping with emerging GPU-centric “neocloud” providers. Meta’s move represents a significant evolution in the AI infrastructure landscape, with potential ripple effects across data center architecture, optical transport networks, and the broader telecom ecosystem.
At the core of this strategy is the monetization of Meta’s rapidly expanding AI compute footprint. The company has aggressively invested in large-scale data center infrastructure—reportedly including multi-hundred-billion-dollar campus developments—to support training and inference for its proprietary large language models (LLMs) and recommendation systems. As these deployments scale, Meta appears to be seeking to externalize surplus capacity, transforming a cost center into a revenue-generating platform.
The proposed service portfolio is expected to span two primary layers. First, Meta may expose access to hosted AI models via APIs, analogous to AWS Bedrock or Azure AI Services, enabling enterprises to integrate generative AI and foundation model capabilities without managing underlying infrastructure. Second, Meta is exploring the provision of raw compute capacity—primarily GPU-accelerated workloads—mirroring the infrastructure-as-a-service (IaaS) model offered by neocloud providers such as CoreWeave. This dual-layer approach would allow Meta to compete both in higher-margin AI platform services and in lower-level compute provisioning.
Telecom & Networking Implications:
From a telecom and network infrastructure perspective, this development has several implications. Hyperscale AI workloads are increasingly bandwidth-intensive, requiring high-capacity, low-latency interconnects within and between data centers. Meta’s investments are therefore likely to drive demand for advanced optical networking technologies, including coherent pluggable optics (e.g., 400ZR/800ZR), data center interconnect (DCI) architectures, and AI-optimized fabric designs leveraging Ethernet-based scale-out topologies. In addition, the geographic placement of these data centers—often in power-abundant, rural locations—introduces new requirements for long-haul fiber connectivity and edge aggregation.
The initiative, internally referred to as “Meta Compute,” reflects a broader industry shift toward vertically integrated AI infrastructure stacks, where hyperscalers tightly couple compute, networking, and software frameworks. For telecom operators and infrastructure vendors, this trend underscores the growing convergence between cloud, AI, and network domains, particularly as AI-driven workloads begin to influence traffic patterns, peering strategies, and edge deployment models.
Strategically, Meta’s entry into the AI cloud market raises competitive pressure across multiple fronts. Unlike traditional cloud providers, Meta brings extensive experience in hyperscale distributed systems and open-source AI frameworks (e.g., PyTorch), but lacks a mature enterprise cloud ecosystem. Its success will likely depend on its ability to translate internal infrastructure efficiencies into externally consumable services, while addressing enterprise requirements for reliability, security, and service-level agreements.
Meta’s cloud push is best viewed as a network-and-infrastructure strategy as much as a software business, because monetizing AI capacity depends on how well it can expose compute, move data, and preserve performance at hyperscale. The telecom significance is that Meta is turning internal AI infrastructure into a market-facing platform, which increases the importance of optical transport, data-center interconnect, and low-latency backbone engineering.
From a telecom perspective, the key issue is not simply that Meta may sell AI models or GPU capacity; it is that the company is building a service layer on top of a very large, power- and bandwidth-intensive distributed system. Reuters reported that Meta is considering both hosted model access and raw compute sales, with the former resembling an AI platform service and the latter looking more like neocloud infrastructure.That means the network becomes part of Meta’s product offering. Large AI inference and training environments require high-bisection fabrics inside the data center, plus dense east-west traffic handling, which pushes demand for faster Ethernet switching, advanced optical modules, and carefully engineered rack-to-rack and site-to-site interconnects. Meta’s AI cloud ambitions reinforce a broader shift: hyperscalers are no longer treating networking as a background utility, but as a primary constraint on scale.
Network World’s coverage of Meta Compute notes that Meta has unified data center and network oversight and is planning multi-gigawatt AI buildouts, underscoring how tightly power, fiber, switching, and facility design are now linked.
For network operators and vendors, that translates into stronger demand for long-haul fiber, DCI platforms, low-latency transport, and high-radix switching. It also raises the strategic value of metro and regional interconnect corridors that can support AI clusters, especially when capacity must be spread across multiple sites for power, land, or resiliency reasons.
Meta’s potential move into raw compute sales is especially relevant to telecom because it resembles the economics of infrastructure-heavy cloud and colocation models. In practice, the service quality will depend on how efficiently Meta can provision GPU clusters, maintain deterministic performance, and avoid congestion across the transport layer connecting those clusters. That implies growing importance for:
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Coherent optical transport and scalable DCI.
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High-capacity Ethernet fabrics for AI clusters.
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Open-rack and disaggregated infrastructure designs.
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Network automation that can track workload placement and traffic hotspots.
These are not just cloud concerns; they are telecom-grade capacity-planning problems. As AI clusters become larger and more distributed, network planning starts to look more like core network engineering than conventional enterprise hosting.
Image Credits: Gabby Jones/Bloomberg / Getty Images
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Conclusions:
Meta’s entry would not only compete with AWS, Azure, and Google Cloud, but could also pressure specialized neocloud providers more directly. Reuters noted that Meta’s spare capacity could matter more to neo-cloud vendors than to the largest hyperscalers, because those providers rely on access to external GPU supply and managed infrastructure growth. For telecom analysts, that suggests the competitive battleground is shifting from “who has the best model” to “who can deliver the most resilient compute-network-power stack.” The winners will likely be those that can couple AI accelerators with fiber-rich sites, robust interconnect, and energy-secure data center footprints.
Meta’s move reflects the convergence of cloud, AI, and transport networks. The story is less about Meta becoming a generic cloud vendor and more about hyperscale AI infrastructure evolving into a new class of network-dependent utility. Indeed, Meta’s cloud initiative highlights a broader industry reality — in the AI era, compute is valuable, but connectivity, optical scale, and power-aware architecture increasingly determine whether compute can be monetized at all.
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References:
Meta, like SpaceX, looks to turn excess AI compute into cash
https://www.cnbc.com/2026/05/27/mark-zuckerberg-says-meta-starting-cloud-business-on-the-table.html
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
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Is AI the driving force behind the metaverse?
Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained
According to a new AI RAN Advanced Research Report published by Dell’Oro Group, cumulative AI RAN revenue is projected to reach $35 B over the next five years (2026-2030). However, AI RAN is not expected to expand the overall RAN market.
“Our market assessment and long-term AI RAN position remain unchanged,” said Stefan Pongratz, Vice President at Dell’Oro Group. “AI RAN is already happening and will scale ahead of 6G. At the same time, these tools will enhance the RAN, but they are unlikely to expand the overall RAN market. Even as suppliers introduce new software-based subscription models, we expect AI RAN to generate little, if any, incremental RAN revenue the end of the forecast period,” continued Pongratz.
Additional highlights from the June 2026 AI RAN Advanced Research Report:
- The base-case forecast assumes that AI RAN will not expand the RAN market. Nevertheless, AI RAN is expected to become an important technology enabler as operators incorporate greater virtualization, intelligence, automation, and O-RAN capabilities into their RAN roadmaps.
- GPU RAN projections have been revised upward—GPU RAN is now expected to be a $1 B+ market by the end of the forecast period.
- In the near term, the AI RAN market will remain centered on AI-for-RAN, single-purpose deployments, non-GPU architectures, D-RAN, and 5G.
- Incumbent RAN radio and baseband suppliers are well-positioned in the initial AI RAN phase, driven primarily by AI-for-RAN upgrades leveraging existing hardware. Per Dell’Oro Group’s regular RAN coverage, the top five RAN suppliers contributed approximately 96 percent of 2025 RAN revenue. See charts below.
Dell’Oro Group’s AI RAN Advanced Research Report includes a 5-year forecast for AI RAN by location, tenancy, technology, and region. To purchase this report, please contact us at [email protected].
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Total & Wireless Telecom Equipment Revenue- top 4 and top 3:
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Analysis (Source: Perplexity.ai AND Google Gemini):
There are three versions of AI-RAN which are not mutually exclusive:
- AI for RAN: Embeds AI into the base software stack to automatically manage radio waves, optimize spectrum efficiency, enhance beamforming, and reduce energy consumption in real time. Nokia, Ericsson, NVIDIA.
- AI on RAN: Uses cell towers and base stations as decentralized computing nodes. This allows telecom networks to host AI workloads locally rather than sending all data to distant cloud servers, providing ultra-low latency for applications like robotics, AR/VR, and autonomous vehicles. Nokia, NVIDIA, and operator trial partners like T-Mobile, Indosat, and SoftBank.
- AI and RAN: Combines the two to support “Networks for AI,” where distributed telecom networks act as an active, intelligent backbone to serve end-user AI traffic. AI-RAN Alliance plus Nokia and NVIDIA as the most visible industry champions.
References:
AI RAN to Reach $35 B Over Next Five Years, According to Dell’Oro Group
NVIDIA AI RAN video: youtube.com/watch?v=hwLLBfzoSko&t=26
AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap
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
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
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: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Analysis of AWS-3 Spectrum Results: Verizon Wins Big; Urban Capacity vs. Propagation
AWS-3 Auction Results:
Of the $3.57 billion total spent in the recent AWS-3 auction, $3.2 billion of it was accounted for by Verizon alone (bidding as Cellco Partnership). That bought it 82 of the 200 licenses on offer, with the price premium explained by a bias towards high value urban licenses. The remaining AWS-3 spectrum allocation was primarily secured by T-Mobile and, to a lesser extent, AT&T. SpaceX bid conservatively, indicating its recent spectrum acquisitions from Dish Networks will likely serve as supplementary capacity rather than signaling a shift to build a comprehensive, standalone terrestrial mobile network.
T-Mobile took home more raw licenses than Verizon, but spent a fraction of the capital. This architectural split is dictated by existing network layouts. T-Mobile used its capital to snap up cheap, fragmented regional licenses to patch coverage holes in its massive 2.5 GHz (Band 41) rural backbone. Conversely, Verizon spent heavily because its existing grid configuration requires deeper, cleaner mid-band spectrum to keep up with urban data density without triggering catastrophic inter-cell interference.
Here’s a breakdown of winning bids and assigned spectrum:
| Carrier | Licenses Won | Total Spend | Strategic Context |
|---|---|---|---|
| Verizon | 82 | $3.20 billion | Dominant bidder to acquire significant mid-band capacity. |
| T-Mobile | 102 | $278 million | Acquired the largest volume of licenses for rural and edge-market coverage. |
| AT&T | 10 | $121 million | Targeted smaller holdings to bolster localized network capacity. |
| SpaceX | 2 | $8.5 million | Acquired two regional licenses to complement supplemental coverage from space (SCS) initiatives. |
Here are the full results, published by the FCC:
“After years on the sidelines, FCC auctions are finally back,” said Chairman Brendan Carr. “Today’s successful auction generated billions of dollars in competitive bids to put spectrum to effective commercial use, and it bolsters competition in the wireless marketplace. We will carry this momentum forward as we prepare for the Upper C-Band auction in the year ahead.”
“Up to $3.3B of the auction’s proceeds will be used to cover amounts borrowed to support the FCC’s “rip and replace” program and other Commerce Department programs,” said the FCC press release. “The auction made available 200 spectrum licenses in the 1695-1710 MHz, 1755-1780 MHz, and 2155-2180 MHz bands which were subject to bid defaults or bid withdrawals in the 2014 auction and thus have remained unused in the FCC’s inventory since then.”
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Let’s examine how cellular network operators are navigating the fundamental physics of RF propagation:
As 5G networks transition from initial deployment to hyper-dense optimization, Auction 113 highlights a widening divergence in network architecture strategies: Verizon’s aggressive pursuit of premium urban capacity versus T-Mobile’s tactical rural densification.
The Physics of the Premium: Why Verizon Paid Up for AWS-3
To the casual observer, Verizon’s multi-billion-dollar bet on AWS-3 (operating in the 1.7 GHz uplink / 2.1 GHz downlink bands) seems redundant given their massive 2021 C-band (3.7 GHz) holdings. However, looking at the link budget reveals that all mid-band spectrum is not created equal.
[1.7 GHz / 2.1 GHz (AWS-3)] ---> Lower Path Loss, Better Indoor Penetration
[3.7 GHz (C-Band)] ---> Higher Path Loss, Requires High Node Density
By securing AWS-3 blocks in high-density markets like New York, Boston, and Chicago, Verizon is solving a specific structural challenge in urban network topology:
- Free-Space Path Loss (FSPL): Operating at 1.7/2.1 GHz provides a significant propagation advantage over 3.7 GHz. According to the Friis transmission equation, signal attenuation increases with the square of the frequency. Moving from 3.7 GHz down to 1.7 GHz yields a theoretical path loss improvement of nearly 6 to 7 dB, drastically extending the effective cell radius.
- Building Penetration Indices: Higher-frequency C-band signals suffer from severe attenuation when interacting with concrete, low-E glass, and brick. AWS-3 signals possess longer wavelengths that penetrate urban building envelopes far more effectively, reducing the reliance on costly indoor small-cell deployments.
- Offloading the Core: Rather than burning valuable C-band capacity on edge-case indoor users with degraded Signal-to-Interference-plus-Noise Ratios (SINR), Verizon can utilize the AWS-3 layer to maintain robust, high-throughput indoor links, preserving the 3.7 GHz layer for line-of-sight macro capacity.
For RF engineers tracking the convergence of terrestrial and non-terrestrial networks (NTN), the most intriguing data point from Auction 113 was SpaceX’s calculated acquisition of two specific licenses—including the Gulf of Mexico footprint—for $8.5 million. This move offers critical clues regarding SpaceX’s Direct-to-Cell (D2C) Starlink framework. Fresh off its multi-billion-dollar spectrum onboarding from EchoStar, SpaceX is systematically hunting for terrestrial frequencies that can act as a safety valve. Winning the Gulf of Mexico AWS-3 block allows SpaceX to establish a seamless, interference-free maritime D2C testing ground. This block can bridge terrestrial terrestrial networks and satellite-to-phone links without violating the strict aggregate interference power-flux-density (PFD) limits imposed near land borders.
Engineering the Transition: Funding “Rip and Replace”:
Beyond network topology, Auction 113 serves a vital national security engineering mandate. Up to $3.3 billion of the auction’s proceeds are legally earmarked to fill the funding shortfall for the FCC’s Secure and Trusted Communications Networks Reimbursement Program.
For hundreds of regional and rural operators, this influx of capital directly funds the complex hardware migration away from legacy, non-compliant Huawei and ZTE cellular access networks. Engineers are replacing proprietary, single-vendor base stations with modern, Open RAN-ready or fully compliant Ericsson, Nokia, and Samsung network infrastructure—effectively rewriting the physical layer of rural American telecom.
Conclusions:
Auction 113 proves that even in an era dominated by software-defined networking and cloud-native cores, physical layer mechanics dictate market value. Verizon’s $3.16 billion investment confirms that superior propagation characteristics and favorable link budgets still command a premium. As carriers race to deliver uniform 5G performance indoors and out, AWS-3 remains an elite tier of wireless real estate where engineering reality justifies the corporate price tag.
References:
https://www.telecoms.com/spectrum/verizon-was-the-big-spender-at-the-aws-3-spectrum-auction
https://www.fierce-network.com/wireless/verizon-emerges-biggest-winner-aws-3-auction
Federal Communications Commission, “Auction of Advanced Wireless Services (AWS-3) Licenses Closes; Winning Bidders Announced for Auction 113,” FCC Public Notice (DA-26-633), Jun. 26, 2026. Available: FCC Official Document Announcement
M. Alleven, “Verizon emerges as biggest winner in AWS-3 auction,” Fierce Network, Jun. 29, 2026. Available: Fierce Network Article
F. Rayal, “Big Carriers Get Selective: Lessons from the $3.57 Billion AWS-3 Auction 113,” Frank Rayal Telecom Insights, Jun. 28, 2026. Available: Frank Rayal Strategic Analysis
G. Winslow, “FCC Raises $3.5 Billion in AWS-3 Wireless Auction,” TV Tech, Jun. 24, 2026. Available: TV Technology Regulatory Report
Reuters, “U.S. spectrum auction raises $3.5 billion, will fund replacing Chinese telecom equipment,” Yahoo Finance, Jun. 23, 2026. Available: Yahoo Finance / Reuters Coverage
SatNews Publishers, “$3.57 Billion Milestone: FCC Advanced Wireless Services (AWS-3) Spectrum Auction Concludes,” SatNews Space & Satellite Media, Jun. 24, 2026. Available: SatNews Auction Summary
Morningstar Equity Research, “US Telecom: Verizon Shells Out $3 Billion for Spectrum as SpaceX Treads Lightly,” Morningstar Investor, Jun. 29, 2026.
