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Automation

Network automation replaces manual configuration and operations with software-driven processes, ranging from scripted tasks to fully autonomous, self-optimizing networks. Driven by the complexity of 5G standalone, cloud-native architectures, and multi-vendor environments, operators are pursuing higher autonomy — often framed against the TM Forum’s autonomous networks levels — to cut costs, speed service delivery, and improve reliability. Automation increasingly relies on AI and closed-loop systems that sense, decide, and act with diminishing human intervention. For operators and enterprises, the practical path runs through incremental autonomy: automating well-defined tasks before attempting end-to-end self-operation. This channel covers network automation across RAN, core, and operations — including orchestration, closed-loop control, and the AI techniques behind autonomous networks — with analysis of where automation is delivering measurable results and how operators are progressing toward higher levels of autonomy.

SoftBank and NVIDIA AI-RAN: 16-Layer MU-MIMO on GPUs

SoftBank AI-RAN Realizes - Software-only Massive MIMO on GPU
SoftBank and NVIDIA have validated a fully software-defined, GPU-accelerated AI-RAN that delivers 16-layer massive MU-MIMO outdoors—an inflection point for vRAN performance, Open RAN scalability, and AI-native RAN design. SoftBank’s AI-RAN product, AITRAS, executed the entire 5G physical layer on NVIDIA GPUs at the Distributed Unit and demonstrated stable 16-layer multi-user MIMO downlink in an outdoor trial at NVIDIA’s Santa Clara campus. The system connected to O-RAN-compliant radios via Split 7.2x and achieved roughly three times the spectral efficiency and throughput of a conventional 4-layer setup while maintaining per-user rates under high load. The field results show that software-only massive MIMO on GPUs can meet macro-radio conditions without bespoke silicon.

Ericsson and SAR bring 5G FRMCS to Saudi Rail

Ericsson and SAR bring 5G FRMCS to Saudi Rail
The partnership targets two fronts: mission-critical rail communications for operations and high-speed broadband for passengers. The scope includes deploying advanced 5G infrastructure, testing FRMCS-based use cases, and running a real-world trial on an existing SAR line to validate performance, integration, and safety requirements. An innovation and test lab will be established to accelerate solution validation, and SAR teams will be trained on FRMCS/5G rail technologies to build in-house capability. The partners will explore 5G Standalone capabilities for operational communications, including quality-of-service guarantees, redundancy, and resilience needed for rail. FRMCS-aligned services such as mission-critical push-to-talk/data/video (MCX), Railway Emergency Call, and secure staff communications will be validated for integration with signaling and control systems.

Verizon partners with Eaton Fiber for FTTP expansion

Verizon partners with Eaton Fiber for FTTP expansion
Verizon signed a commercial agreement with Eaton Fiber, an affiliate of Tillman Global Holdings, to extend fiber-to-the-premises service well beyond its current Fios footprint and the locations it expects to add through its planned Frontier deal. The structure is straightforward. Eaton Fiber will fund, build, and operate the local access network. Verizon will handle sales, marketing, and customer care and gain full residential retail exclusivity on the new builds during deployment and for a subsequent period. Fiber is the control point for converged services.

Vodafone GenAI RIC Accelerates 5G Open RAN rApps

Vodafone GenAI RIC Accelerates 5G Open RAN rApps
Vodafone is partnering with Irish firm Zinkworks on Rapid RIC, a central platform that blends secure data analytics, a visual low-code interface, and code-generating AI to create and operate RAN applications, or rApps. The goal is ambitious but specific: cut time-to-market from months to weeks, scale deployments across markets, and improve service quality, capacity, and energy use. The platform is slated for early 2026 availability and will run primarily on Vodafone’s private Google Cloud Platform environment. Rapid RIC uses GenAI to generate production-grade code from visual designs, enabling radio engineers to turn domain knowledge directly into software without deep AI or ML skills.

Nokia Profit Beat as AI Demand Lifts Optical Networks

Nokia Shifts Focus to AI and Network Infrastructure
Nokia delivered a stronger-than-expected third quarter, with comparable operating profit reaching €435 million against consensus of about €342 million. Group net sales rose 12% to €4.83 billion, above forecasts, driven by Optical Networks and cloud-related demand tied to AI data centers. The stock jumped double digits intraday and added billions in market value, reflecting newfound confidence after a challenging first half. The recovery now is concentrated in network infrastructure rather than mobile RAN, underscoring where customers are actually spending to handle AI-era traffic patterns. Nokia nudged its full-year operating profit outlook to €1.7–2.2 billion, with a reporting change related to scaling down passive venture investments partly in play.

Palantir and Lumen $200M Enterprise AI Partnership at the Edge

Palantir and Lumen $200M Enterprise AI Partnership at the Edge
A new partnership between Palantir and Lumen Technologies signals a shift from internal AI pilots to packaged enterprise services delivered over a telecom-grade edge and network footprint. Palantir will provide its Foundry and Artificial Intelligence Platform (AIP) as the data and decisioning layer for Lumen’s enterprise AI offerings, which Lumen plans to deliver on top of its edge computing nodes, broadband infrastructure, and managed digital services. The companies position this as a multi-year, strategic collaboration focused on operational AI use cases, not just experimentation. While exact terms were not disclosed, multiple reports indicate Lumen’s total spend could exceed $200 million over several years.

Amazon AI smart glasses for delivery drivers

Amazon AI smart glasses for delivery drivers
Amazon is piloting AI-enabled smart glasses for delivery associates to streamline last‑mile workflows, adding a hands‑free heads‑up display that blends navigation, scanning, and proof‑of‑delivery into the driver’s field of view. The company is testing delivery‑specific smart glasses that use on‑device computer vision and AI to identify packages, surface hazards, and guide walking routes from the vehicle to the doorstep without requiring a phone in hand. When a van is parked, the device activates and shows the next task: find the right parcel in the vehicle, traverse complex environments like multi‑unit buildings, and confirm delivery with visual capture.

Ubiik Earns Anterix 900 MHz Certification for Private LTE

Ubiik Earns Anterix 900 MHz Certification for Private LTE
Ubiik has secured Anterix certifications for its router and base station, signaling readiness for private LTE deployments on Anterix’s 900 MHz Band 106 spectrum. Anterix awarded Anterix Active badges to Ubiik’s Pyxis 5G LPWA RA810 router and its goRAN+ base station, confirming they meet Anterix operating criteria for 900 MHz private LTE. In addition, the high-power Pyxis RA320X variant received an Anterix Capable badge, validating a 28 dBm transmit option that extends reach compared to standard 23 dBm LTE modules. Together, the router and RAN designations give utilities and critical infrastructure providers a tested, end-to-end path to deploy pLTE on B106. Band 106 is licensed 900 MHz spectrum aligned with 3GPP LTE that Anterix has aggregated across the U.S., Puerto Rico, Alaska, and Hawaii.

2024 U.S. Broadband Capex: $89.6B Investment

2024 U.S. Broadband Capex: $89.6B Investment
Industry capex remained exceptionally strong in 2024, underscoring broadband’s status as critical infrastructure for the digital and AI economy. Broadband providers invested an estimated $89.6 billion in U.S. communications infrastructure last year, pushing cumulative investment since 1996 to more than $2.2 trillion and keeping the 2020–2024 average above $90 billion annually. Spend concentrated on fiber deepening, rural reach, wireless capacity, and overall network scale for AI, cloud, and streaming workloads. While 2024 trailed 2023’s higher tally, it still signals a sustained, competitive race to modernize fixed and mobile networks.

Google Cloud Study: 52% Deploy AI Agents, Driving ROI

Google Cloud Study: 52% Deploy AI Agents, Driving ROI
Google Cloud’s 2025 ROI of AI study signals a step-change: AI agents are now in production at scale and delivering measurable business outcomes. The study, fielded with National Research Group across 24 countries, finds 52% of executives report their organizations already use AI agents—specialized models that can plan, reason, and take actions. Momentum is material: 39% say their company has launched more than ten agents. Executives also report faster delivery cycles, with over half moving use cases from idea to production within three to six months, up from last year. Generative AI investment continues to climb as technology costs fall.

Arm and Meta Boost AI Efficiency with Neoverse

Arm and Meta Boost AI Efficiency with Neoverse
Arm and Meta have inked a multi-year partnership to scale AI efficiency from hyperscale data centers to on-device inference, aligning Arm’s performance-per-watt strengths with Meta’s AI software and infrastructure stack. Meta plans to run its ranking and recommendation workloads on Arm Neoverse-based data center platforms as part of an ongoing infrastructure expansion. The companies are co-optimizing AI software components—spanning compilers, libraries, and frameworks like PyTorch, FBGEMM, vLLM, and the ExecuTorch runtime—so models can execute more efficiently on Arm CPUs in the cloud and on Arm-based devices at the edge. The work includes leveraging Arm’s KleidiAI optimizations to improve inference throughput and energy efficiency, with code contributions flowing back to open source.

AWS US-EAST-1 Outage: What Went Wrong and How to Mitigate

AWS US-EAST-1 Outage: What Went Wrong and How to Mitigate
AWS experienced a major outage centered on its US-EAST-1 region in Northern Virginia, triggering cascading failures across dozens of cloud services and dependent applications worldwide. The incident began in the early hours of Monday and was initially mitigated within a few hours, though residual errors and recovery backlogs persisted through the morning in US-EAST-1. Engineering updates point to a DNS resolution problem affecting a key database endpoint (DynamoDB) alongside internal network and gateway errors in EC2, which then propagated across dependent services such as SQS and Amazon Connect. When a foundational component like DNS or an internal networking fabric falters, service discovery and API calls fail in bulk.

Frequently Asked Questions

What’s the difference between automation and AI in a telecom context?
Automation, in its traditional form, generally follows pre-defined rules and scripted logic: a specific condition triggers a specific, predetermined response, with no real interpretation or judgment involved beyond what was explicitly programmed in advance. AI adds the capacity to learn from data, recognize patterns that weren’t explicitly anticipated, and make more nuanced decisions about what action makes sense in a given situation, even one the system hasn’t seen in exactly that form before. In practice, most modern telecom systems combine the two: AI analyzes a situation and decides what should happen, while automation infrastructure actually executes that decision reliably and consistently across the network, a combination often described as the foundation for agentic AI.
What is ‘zero-touch’ network operation, and how close is the industry to achieving it?
Zero-touch network operation describes the long-term industry goal of running networks with minimal direct human intervention, where the network itself handles configuration, fault recovery, and optimization automatically and continuously. It’s a meaningful aspiration rather than a fully achieved reality; the industry is progressing toward it in stages, with specific functions, like automated fault detection or dynamic capacity adjustment, achieving meaningful levels of automation well before the broader vision of an entirely self-managing network becomes reality. Standards bodies including ETSI have working groups specifically dedicated to defining the requirements for zero-touch network and service management, reflecting that this remains an active area of ongoing standardization rather than settled, widely deployed technology.
Why is automation considered essential for managing modern 5G networks specifically?
5G networks are substantially more complex than earlier generations for several compounding reasons: they rely heavily on virtualized network functions running across cloud infrastructure rather than fixed dedicated hardware, they support network slicing, meaning managing multiple distinct virtual networks with different performance guarantees simultaneously, and they often combine equipment and software from multiple different vendors rather than one integrated supplier. Traffic patterns also shift constantly and unpredictably as device density and application types continue to grow. Attempting to manually manage this level of complexity at the scale of a national or global network simply isn’t realistic, making automation effectively a practical requirement for operating a modern 5G network reliably at all.
How does automation relate to network orchestration?
Automation and orchestration are closely related but distinct concepts. Automation generally refers to executing a specific task without manual intervention, like automatically restarting a failed process or adjusting a configuration parameter. Orchestration refers to the broader coordination of multiple automated tasks and virtualized network components across their full lifecycle, deciding where different network functions should run, how they should scale, and how they interact with each other to deliver a complete service. In practice, orchestration systems often rely on underlying automation capabilities to actually carry out the individual tasks they coordinate, providing higher-level coordination while automation provides the lower-level mechanism for reliably executing decisions.
What are the risks of relying heavily on automated network systems?
Heavy reliance on automated systems introduces specific risks alongside its clear efficiency benefits. If an automated system makes an incorrect decision, that error can potentially propagate quickly and broadly across the network before a human notices and intervenes, compared to a manual process where mistakes tend to be more contained. There’s also a risk of reduced visibility and understanding among human staff over time, since heavily automated systems can create a gap between what the network is actually doing and what engineers fully understand about why, particularly as AI-driven decision-making becomes more involved. Operators generally manage these risks by maintaining careful guardrails for automated actions, expanding autonomous scope gradually as confidence grows.
How has automation changed the day-to-day role of network engineers?
Automation has shifted network engineers’ day-to-day work away from repetitive, manual configuration and troubleshooting tasks and toward higher-level responsibilities like designing automation policies, overseeing AI-driven systems, and handling more complex or novel problems that automated systems aren’t yet equipped to resolve independently. Rather than manually configuring each new service or personally diagnosing every fault, engineers increasingly spend time defining the rules, guardrails, and escalation criteria that govern how automated systems behave, then stepping in directly for situations that fall outside those parameters. This represents a meaningful skills shift, with growing demand for engineers comfortable working with automation platforms and AI systems alongside traditional networking expertise.
What’s ‘closed-loop automation,’ and why does it matter?
Closed-loop automation refers to systems that don’t just execute an automated action once, but continuously monitor the result of that action, compare it against the intended outcome, and adjust automatically if the result doesn’t match expectations, creating a self-correcting cycle without requiring a human to manually verify and re-trigger each step. A closed-loop system managing network capacity, for example, might automatically increase resources in response to rising traffic, then continuously monitor whether that adjustment actually resolved the congestion, and make further automatic adjustments if it didn’t. This concept is considered a meaningful step toward the zero-touch network vision, since it moves automation beyond simple one-off actions toward genuinely self-managing behavior based on real-world feedback.
The next evolution of Ciena’s DCOM: OOB management for diverse data center needs
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