- Tech News & Insight
- February 27, 2025
- News Feed
MediaTek is showcasing wireless evolution towards 6G, including hybrid computing, a live LEO broadband NR-NTN trial, and SBFD.
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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.
- Tech News & Insight
- February 26, 2025
- Hema Kadia
At MWC 2025, Qualcomm and Nokia Bell Labs demonstrated how AI-driven wireless networks can achieve multi-vendor interoperability without sharing proprietary data. Their AI-enhanced channel state feedback (CSF) technology optimizes 5G performance, improving network efficiency, signal strength, and reliability. With implications for 6G, Open RAN, and private 5G, this breakthrough is reshaping the future of AI-powered wireless communications.
- Tech News & Insight
- February 24, 2025
- News Feed
As telcos seek growth beyond connectivity, a $400 billion enterprise opportunity awaits. At MWC25’s Connected Industries, leaders from NVIDIA, 5GAA, and Accenture will explore how 5G, AI, IoT, and private networks are reshaping industries like manufacturing, fintech, smart mobility, and entertainment. Learn why GSMA’s Connected Communities is key to unlocking new revenue streams and driving digital transformation.
- Usecase
- February 20, 2025
- Hema Kadia
Roularta Media Group (RMG), Belgium’s leading multimedia company, is revolutionizing its printing operations with NTT DATA’s Private 5G network. This high-performance, secure infrastructure enables automation, real-time tracking, and AI-driven workflows, improving efficiency and reliability in a 24/7 printing environment. With plans to expand into Germany and the Netherlands, RMG is setting a new standard for digital printing transformation.
- Usecase
- February 20, 2025
- Hema Kadia
SailGP is enabling high-speed sailing with Ericsson Private 5G and Edge Computing, ensuring real-time race analytics, seamless connectivity, and immersive fan engagement. With Cradlepoint edge routers in each F50 catamaran, teams process over 53 billion data points per race day, optimizing performance and ensuring fairness. This 5G-powered digital transformation sets a new benchmark for sports connectivity.
- Tech News & Insight
- February 11, 2025
- Hema Kadia
AI is playing a key role in telecom security by strengthening threat detection, fraud prevention, and regulatory compliance. As 5G, IoT, and edge computing expand, telecom networks face cyber threats such as AI-specific attacks, network intrusions, and data breaches. AI-powered security solutions provide automated threat response, anomaly detection, and AI lifecycle protection, helping telecom providers maintain a secure and resilient network infrastructure.
- News
- February 11, 2025
- Hema Kadia
AI is transforming the relationship between telcos and hyperscalers like AWS, Google Cloud, and Microsoft Azure. With AI-driven automation, cloud-native networks, and edge computing, telecom operators are optimizing efficiency, reducing costs, and unlocking new revenue streams. As AI-powered innovations reshape 5G, cybersecurity, and digital services, these strategic partnerships are set to redefine the future of telecom.
- News
- February 7, 2025
- Hema Kadia
Celona and stc Group have announced a strategic partnership to expand private 5G adoption in Saudi Arabia, Kuwait, and Bahrain. This initiative enhances business efficiency through secure, scalable, and high-performance wireless connectivity. Designed for industries like oil and gas, logistics, manufacturing, and mining, the solution addresses key challenges of traditional networks, reducing operational costs and driving digital transformation.
- News
- January 23, 2025
- Hema Kadia
India approves 687 MHz of spectrum refarming to accelerate 5G rollout and lay the foundation for 6G services. This move increases total telecom spectrum to 1,587 MHz and addresses growing demands for mobile broadband, boosting innovations in edge computing and IoT while supporting telecom operators like Jio, Airtel, and Vodafone Idea.
- Tech News & Insight
- January 23, 2025
- Amit Mohite
Discover how semiconductor packaging is transforming technology, driving advancements in AI, 5G, IoT, and autonomous vehicles. This in-depth analysis explores cutting-edge technologies like System-in-Package (SiP), 3D ICs, and chiplet design, highlighting their transformative impact on device performance, energy efficiency, and miniaturization. From AI accelerators to sustainable packaging solutions, explore the trends, challenges, and future opportunities shaping the semiconductor industry's next wave of innovation.
- Partner Event
- January 22, 2025
- Hema Kadia
Start: March 3, 2025
End: March 6, 2025
Venue: Fira Gran Via, Barcelona
Location: Barcelona
End: March 6, 2025
Venue: Fira Gran Via, Barcelona
Location: Barcelona
- News
- January 7, 2025
- Hema Kadia
Edge computing is transforming telecom by enabling efficient 5G networks. By processing data closer to its source, it minimizes latency, reduces network congestion, and supports real-time applications like IoT, AR, and remote healthcare. Learn how this transformative technology tackles challenges like infrastructure costs and security while opening new revenue streams and enhancing customer experience.
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.
