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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.

Bridging the Digital Divide with 5G and Open RAN | Supermicro

Bridging the Digital Divide with 5G and Open RAN | Supermicro
Michael Clegg, VP & GM for 5G and Edge at Supermicro, shares insight on how Supermicro is bridging the digital divide with Open RAN & 5G. What does Open RAN mean to Supermicro? What is the state of Open RAN market today? How Open RAN can help improve global connectivity to bridge the digital divide?

Securing Connected Vehicles: Harman’s Cybersecurity Pathway

How to prevent cyberattacks in connected vehicles? | HARMAN
Explore Harman’s innovative strategies in fortifying cybersecurity for connected vehicles, led by VP Ravi Puvvala. Navigate through their approach in tackling regulatory challenges, investing in in-vehicle and edge platform security, and deploying unified cybersecurity architectures to safeguard the future of automotive connectivity and ensure compliant, secure, and future-proof operations in the evolving digital vehicular landscape.

Proximus to Create 5G Lab on Howest Campus

Proximus to Create 5G Lab on Howest Campus
Howest 5G lab in partnership with Proximus will be used by - Digital Arts, Entertainment, Multimedia, Creative Technology & Cyber Security students.

30. Technology Convergence with Paul Stager of EdgeQ

5G Guys Podcast
Remember when you were a kid, and you tied two cups together with a string and used it as a phone? Or maybe you remember the first time you opened up AOL chat and got to talk with a friend instantly. In 2022 it takes a lot more to keep people...

AT&T and DoD demonstrates 5G-enabled smart warehouse

AT&T and DoD demonstrates 5G-enabled smart warehouse
AT&T, DoD & U.S. Navy demonstrated 5G-powered technology solutions that can support the enablement of a Naval "Smart Warehouse."

Edzcom & Signify partner to build private 5G based smart-city network in Tampere, Finland

Edzcom & Signify partner to build private 5G smart-city network
The smart city private network partnership is an innovative, large-scale, high-performance infra-project designed to bring enhanced and fully controlled digital 5G connectivity to the city for the benefit of the city, its citizens, and visitors.
A first of its kind in Finland, the connectivity project leverages the indigenous light infrastructure to create new data driven services that are run on analytics, as well as being predictive and cognitive in nature.

Athonet opens private 5G lab in London’s square mile for Financial Services

Athonet opened Fintech 5G lab in London
Athonet opened Fintech 5G lab in London’s financial district to help banks, financial institutions, hedge funds and their technology partners.

Exploring 5G and IoT Security Strategies with AT&T Leadership

Security in a 5G Connected World | AT&T
Dive into the insights from AT&T's VP of Product Strategy and Innovation, Rupesh Choksi, on navigating the complexities of IoT security amidst the proliferation of 5G technology. Explore the strategic approaches toward securing connections, ensuring compliance, and enhancing data processing and connectivity across various sectors, all while innovating with security embedded in design and function. Discover how 5G technology is not just a connection tool, but a pivotal element in safeguarding and optimizing future digital interactions in a hyper-connected world

Betacom to deploy indoor private 5G at US Manufacturing Institute with Airspan & Druid

Betacom to deploy indoor private 5G at US Manufacturing Institute with Airspan & Druid
Betacom to deploy private 5G network at MxD, the Digital Manufacturing Institute, and the National Center for Cybersecurity in Manufacturing.

Zero Trust security framework for 5G+ edge applications

Zero Trust security framework for 5G+ edge applications
The security paradigm has made significant evolution over the last couple of decades. When it comes to securing today’s 'no-boundaries' business applications, no assumptions, and zero trust.

Nokia & Mobile Industrial Robots (MiR) showcase real-time robot fleet management using private 5G wireless

5G for smart manufacturing | smart factory | industry 4.0 report - TeckNexus | Auto manufacturer AD Plastik to deploy private 5G with Nokia and OIV
> Nokia and MiR to show how private 5G wireless networks can deliver high-performance, low-latency and secure connectivity for Industry 4.0 digital transformation
> MiR’s autonomous mobile robots will be connected to a 5G private wireless network using Nokia DAC, to showcase real-time fleet management at the event in Stuttgart
> Will be the first demonstration to use private 5G wireless to connect AMRs at an international logistics event

27. In-flight Cellular with Fred Teichman of GoGo Wireless

5G Guys Podcast
Back in the day, air travel looked a lot different than it does today. People would dress to the nines, the isles were wider and the seating was roomier, the finest food would be brought to your seat even if you weren’t first-class and so much more....

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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