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Edge/MEC

Edge computing and multi-access edge computing (MEC) place processing close to where data is generated — at the network edge rather than in distant centralized clouds — to cut latency and reduce backhaul. For applications that demand fast, local responses, such as industrial automation, computer vision, AR, and autonomous systems, the edge is often what makes them viable. Edge is tightly linked to 5G standalone, private networks, and AI inference, and is a key area where operators, hyperscalers, and enterprises both compete and partner. For decision-makers, the questions are where edge genuinely beats centralized cloud and how to balance on-premises, network-edge, and public-cloud processing. This channel covers edge and MEC across operator, hyperscaler, and enterprise deployments — architectures, partnerships, and use cases — with analysis of where moving compute to the edge actually pays off.

Connected Industries: The Future of Smart Mobility with 5G, AI, and Intelligent Transport

Connected Industries: The Future of Smart Mobility with 5G, AI, and Intelligent Transport
Smart mobility is reshaping how the world moves, powered by 5G, AI, and edge computing. From autonomous vehicles and real-time logistics to AI-driven drones and connected public transport, intelligent transportation systems are redefining urban mobility, logistics, and industrial automation. As global investment and collaboration grow, the transportation industry is transforming into a $11.1 trillion smart ecosystem focused on sustainability, efficiency, and connectivity.

Telecom’s $300B Problem: Missing Network Observability

Telecom’s $300B Problem: Missing Network Observability
Telecom providers have spent over $300 billion since 2018 on 5G, fiber, and cloud-based infrastructure—but returns are shrinking. The missing link? Network observability. Without real-time visibility, telecoms can’t optimize performance, preempt outages, or respond to security threats effectively. This article explores why observability must become a core priority for both operators and regulators, especially as networks grow more dynamic, virtualized, and AI-driven.

IoT Convergence: Building Seamless, Scalable Connectivity Across Networks

IoT Convergence: Building Seamless, Scalable Connectivity Across Networks
Connectivity convergence is redefining the Internet of Things by integrating legacy systems, cellular, Wi-Fi, LoRaWAN, BLE, and satellite networks. From agriculture to logistics, IoT ecosystems are evolving to prioritize seamless communication, modular hardware, and intelligent data handling with edge AI. This article explores how convergence is shifting the focus from hype to practical, scalable deployment—unlocking the true potential of IoT everywhere.

On-Device AI in 2025: Are We Ready to Go All-In?

On-Device AI in 2025: Are We Ready to Go All-In?
With AI shifting from the cloud to the device, on-device AI is transforming privacy, speed, and user experience. Experts from Honor, Broadcom, and Orange explore the challenges and innovations shaping this future, from AI chips to offline capabilities and ethical implications. Is it time to go all-in?

Bridging the Digital Divide: Are Telecom Giants Supporting SMEs?

Bridging the Digital Divide: Are Telecom Giants Supporting SMEs?
Telecom giants play a critical role in SMEs’ digital transformation, yet a gap remains in access to broadband, cloud, and 5G technologies. While large enterprises receive priority, SMEs often struggle with affordability and digital adoption. This article explores how telecom providers can bridge the digital divide by offering tailored solutions, strategic partnerships, and flexible pricing models to support SME growth.

Nvidia GTC 2025: AI Breakthroughs with Blackwell Ultra, Agentic AI and Robotics

Nvidia GTC 2025: AI Breakthroughs with Blackwell Ultra, Agentic AI & Robotics
Nvidia GTC 2025 introduced AI advancements, including Blackwell Ultra AI chips, agentic AI, and AI Factories. With innovations in robotics, generative AI, and AI-driven cloud computing, Nvidia is shaping the future of AI-powered industries. Discover how these technologies are transforming healthcare, finance, automotive, and enterprise applications.

NVIDIA Unveils Halos: AI-Powered Safety System for Autonomous Vehicles

NVIDIA Unveils Halos: AI-Powered Safety System for Autonomous Vehicles
NVIDIA has launched Halos, a full-stack AI-powered safety system designed to enhance autonomous vehicle (AV) development. By integrating AI models, simulation tools, and compliance frameworks, Halos ensures AV safety from cloud to car. With industry partners like Continental, onsemi, and OMNIVISION, NVIDIA is setting new safety benchmarks for self-driving technology.

Telcos to Techcos: How AI & 5G Are Transforming Telecom

Telcos to Techcos: How AI & 5G Are Transforming Telecom
The telecom industry is in the midst of a major shift from “telco” to “techco”, with operators investing in AI, 5G, cloud computing, and digital services to compete with tech giants like Amazon and Google. At MWC 2025, leaders from e&, KDDI, MTN, and SK Telecom discussed their AI-driven strategies, including self-healing networks, smart city infrastructure, fintech expansion, and enterprise 5G solutions. As telcos embrace AI-powered automation and cloud-based innovations, they are redefining their role in the digital economy.

MWC 2025 Keynote 7: Tech Game Changers

MWC 2025 Keynote 7: Tech Game Changers
At MWC 2025 Keynote 7: Tech Game Changers, industry pioneers including Peggy Johnson (Agility Robotics), Yuanqing Yang (Lenovo), Naveen Rao (Databricks), Arthur Mensch (Mistral AI), and Kate Ryder (Maven Clinic) shared insights on AI, robotics, and digital transformation. Key topics included humanoid robotics, AI-driven UI, healthcare innovation, and enterprise automation. Discover how AI, data intelligence, and open-source models are revolutionizing industries worldwide.

MWC 2025 Keynote 3: Balancing Innovation and Regulation: Global Perspectives on Telecom Policy

MWC 2025 Keynote 3: Balancing Innovation and Regulation: Global Perspectives on Telecom Policy
As telecom innovation accelerates with 5G, AI, cloud computing, and 6G, regulators worldwide must balance progress with consumer protection, cybersecurity, and fair competition. At MWC 2025, industry leaders from the USA, India, and Europe will explore spectrum management, big tech regulation, net neutrality, and digital inclusion. This keynote provides critical insights into how telecom policies can foster innovation while ensuring security and fairness in a hyper-connected world.

MWC 2025 Keynote 2: Beyond Connectivity; The Telco to Techno Transformation

MWC 2025 Keynote 2: Beyond Connectivity; The Telco to Techno Transformation
The telecom industry is undergoing a major transformation. With AI, cloud computing, and 5G SA driving innovation, operators are shifting from traditional connectivity providers to tech-first companies. The Telco-to-Techco transformation is redefining business models, creating new revenue streams, and enhancing digital services. At MWC 2025, industry leaders from MTN, e&, and GSMA will discuss how telcos can navigate this shift and unlock new opportunities in the digital economy.

MWC 2025 Keynote 1: The Gateway to a New Future – Open APIs & 5G Evolution

MWC 2025 Keynote 1: The Gateway to a New Future - Open APIs & 5G Evolution
As the digital world evolves, connectivity is reshaping industries through API-driven telecom solutions. The GSMA Open Gateway initiative is pioneering this shift by enabling seamless integration between telecom operators, developers, and enterprises. With 5G expected to power 1.2 billion connections by 2025, Open Gateway’s standardized APIs are fostering innovation in smart cities, fintech, logistics, and more. Join us at MWC25 Barcelona to explore the future of telecom connectivity.

Frequently Asked Questions

What’s the difference between ‘the cloud’ and ‘the edge’ in telecom?
Cloud computing typically runs in a relatively small number of large, centralized data centers, often located far from any individual user, which is efficient for many workloads but introduces unavoidable physical distance, and therefore latency, between where data is generated and where it’s processed. Edge computing, specifically MEC, places computing resources much closer to where data actually originates, at cell towers, base stations, or local facilities, cutting the round-trip delay for applications where that distance meaningfully matters. The tradeoff is that edge sites generally have far less raw computing capacity than a massive centralized data center, so edge deployments tend to handle specific, latency-sensitive workloads locally while still relying on the broader cloud for less time-critical processing and coordination.
Is MEC mainly a telecom-specific concept, or does it apply more broadly?
It started as a mobile-network-specific concept, originally called Mobile Edge Computing when ETSI introduced it in the mid-2010s, focused on placing computing resources within mobile radio access network infrastructure. ETSI broadened the concept to Multi-access Edge Computing in 2017 specifically to extend it beyond cellular networks to also cover fixed-line broadband and Wi-Fi access, recognizing that the underlying need, computing resources close to the point of data generation, applies regardless of access technology. Current standards work is extending the concept further still, with ETSI’s MEC group releasing Phase 4 specifications in late 2025 focused on developer-friendly APIs for vertical industries and explicit alignment with emerging 6G requirements.
What applications actually benefit from edge computing?
The clearest use cases are ones where milliseconds genuinely matter, or where large amounts of locally generated data would otherwise need to travel back to a distant data center unnecessarily. Autonomous vehicles need to process sensor data and make navigation decisions in near real time, where even modest added latency could be meaningful for safety. Industrial automation and predictive maintenance benefit from edge processing of sensor data from factory equipment. AR and VR applications need responsive, low-latency rendering support. Smart city video analytics, like traffic monitoring, generates enormous volumes of video data far more efficient to process locally. Increasingly, running AI inference closer to users for real-time applications is becoming one of the most significant edge use cases of all.
Why are telecom operators excited about edge computing as a revenue source?
Beyond reducing backhaul costs, edge sites give telecom operators something cloud hyperscalers don’t have by default: physical proximity and direct integration with the radio network across thousands of locations nationwide. This positions operators uniquely to offer latency-sensitive computing services that a centralized cloud data center simply can’t match on responsiveness, regardless of raw computing power. Operators are increasingly positioning these edge locations specifically as AI inference points, sometimes described as compact ‘AI factories,’ capable of running real-time AI workloads close to users. This opens a genuinely new monetization path beyond selling connectivity itself, letting operators compete in the broader computing and AI infrastructure market using distributed physical infrastructure cloud-only providers would need years to replicate.
How mature is MEC deployment in 2026?
By 2026, MEC has moved well past the concept or early-pilot stage into active, expanding commercial deployment. ETSI’s MEC group has produced more than 50 technical specifications covering reference architectures, service enablers, and deployment guidelines, and released its Phase 4 work in late 2025, focused on developer-friendly APIs and explicit alignment with open-source projects and 6G preparation. Telecom operators worldwide are actively pairing MEC deployments with private 5G networks, AI workloads, and Open RAN integration in live commercial deployments rather than isolated trials. The technology continues to mature rather than being fully settled; convergence between MEC and Open RAN architectures remains an active area of development.
How does edge computing relate to private 5G networks?
Edge computing and private 5G networks are frequently deployed together because they solve complementary problems for the same enterprise use cases. A private 5G network provides dedicated, reliable, high-performance wireless connectivity across a facility like a factory or port, while edge computing provides the local processing power needed to actually act on the data that connectivity carries, without sending everything back to a distant cloud data center. A manufacturing facility, for example, might use private 5G to connect cameras and sensors across the factory floor, with an edge deployment at that same facility processing video analytics or controlling automated machinery in near real time. This pairing is one of the most common patterns in enterprise digital transformation projects today.
What’s the difference between edge computing and Open RAN’s ‘Cloud RAN’ concept?
Edge computing and Cloud RAN address related but distinct parts of the network. Cloud RAN refers specifically to running radio access network functions, the software controlling how a cell site transmits and receives wireless signals, on cloud-based, software-defined infrastructure rather than dedicated radio hardware. Edge computing, particularly MEC, refers more broadly to running general-purpose application workloads, not just radio network functions, close to the network edge, things like video analytics, AI inference, or industrial automation software. In practice, the two concepts increasingly converge physically, since the same edge infrastructure supporting Cloud RAN’s virtualized radio functions can often also host MEC application workloads on shared hardware.
What are the biggest technical challenges in deploying edge computing at scale?
Deploying edge computing at scale introduces several persistent technical challenges. Managing and orchestrating computing resources across potentially thousands of geographically distributed edge sites is meaningfully more complex than managing a small number of centralized data centers, since each edge location has limited physical space, power, and cooling capacity. Ensuring consistent security across so many distributed locations, each a potential point of vulnerability, requires more extensive security architecture than securing a handful of centralized facilities. There’s also a workload placement challenge: deciding which tasks genuinely benefit from edge processing versus which are better handled centrally, since over-provisioning edge capacity for workloads that don’t truly require it can be an inefficient use of limited, expensive infrastructure.
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