Reigniting Growth in the Telecom Industry with AI and Cloud

The telecom industry is at a crossroads. While demand for connectivity continues to surge, operators face stagnating revenues, rising costs, and increasing competition. In his keynote at the Brooklyn 6G Summit 2024, Manish Singh, CTO of Telecom Systems Business at Dell Technologies, outlined a compelling vision for how AI and cloud-native networks can reignite growth in the sector.

The Growth Challenge in Telecom

The traditional telecom business model is under pressure. Operators are struggling with:

• Revenue stagnation despite increasing data consumption.
• Rising operational costs driven by legacy infrastructure and inefficient processes.
• Intensifying competition from hyperscalers and alternative connectivity providers.

To overcome these challenges, Manish argues that telcos must embrace AI-native and cloud-native architectures as fundamental enablers of transformation.

AI: The Catalyst for Intelligent Networks

AI is not just an add-on; it must be at the core of future telecom networks. Manish highlighted several ways AI can drive growth:

• Automation of network operations: AI-driven predictive maintenance and self-optimising networks reduce downtime and operational expenses.
• Enhanced service delivery: AI enables hyper-personalised customer experiences and intelligent traffic management.
• Operational efficiency: AI optimises energy consumption, spectrum allocation, and overall network resource utilisation.

Manish emphasised that AI-native networks will be a defining feature of 6G, making networks more autonomous, efficient, and scalable.

Cloud-native Architectures: The Foundation for Scalability

Moving beyond traditional, hardware-centric networks is essential. Manish advocates for a cloud-first approach, where telecom networks are:

• Software-defined and virtualised, reducing dependence on costly proprietary hardware.
• Highly scalable, allowing operators to adjust capacity dynamically.
• Interoperable and open, fostering innovation through Open RAN and disaggregated networks.

By embracing cloud-native principles, telcos can accelerate service delivery, reduce costs, and stay competitive in an increasingly software-driven ecosystem.

AI Infrastructure: Scaling from Edge to Core

A key enabler of AI and cloud-native networks is the AI Factory approach, which provides scalable infrastructure from mega-scale data centres to the edge. Manish highlighted how AI workloads must be supported across different network layers—from on-premise enterprise deployments to far-edge, near-edge, and core data centres.

Dell Technologies' AI Factory is designed to:

• Support diverse AI edge use cases in telecom.
• Handle power and cooling constraints, crucial for efficient AI model training and inference.
• Leverage cloud-native architectures to ensure seamless scalability and automation across the entire network.

This modular infrastructure ensures that telecom networks can efficiently process AI workloads at every layer, enabling real-time decision-making and optimised operations.

Overcoming Challenges in AI and Cloud Adoption

Despite the clear benefits, Manish acknowledged key barriers:

• Legacy infrastructure: Transitioning from traditional networks requires significant investment.
• Security and privacy concerns: AI-driven automation raises questions about data integrity and network security.
• Industry mindset shift: Operators must adopt a culture of innovation and rapid iteration.

Addressing these challenges requires industry-wide collaboration, strong partnerships with cloud providers, and a commitment to open innovation.

Conclusion: The Time to Act is Now

Manish’s message to the industry was clear—AI and cloud are not future aspirations; they are essential for telecom survival and growth. By leveraging AI-native automation and cloud-native architectures, operators can reignite growth, drive efficiency, and prepare for the 6G era.

Watch Manish Singh’s full keynote embedded below:

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Lessons from ANRW ’24: AI and Cloud in 5G/6G Systems

The ACM, IRTF & ISOC Applied Networking Research Workshops (ANRW) offer a vibrant forum for researchers, vendors, network operators, and the Internet standards community to exchange emerging results in applied networking research. To foster collaboration across these diverse groups, ANRW events are co-located with IETF standards meetings, typically held annually in July. These workshops prioritise interactive discussions and engagement, complementing traditional paper presentations.

ANRW '24, held on 23 July 2024 at the Hyatt Regency Vancouver, brought together industry leaders and academics to share insights on advancing networking technologies. Among the standout sessions was a keynote presentation by Sharad Agarwal, Senior Principal Researcher at Microsoft. His keynote titled, "Lessons I Learned in Leveraging AI+ML for 5G/6G Systems", highlighted pivotal themes influencing telecom and networking.

Sharad distilled his experiences into three key lessons, each underscored by examples of research and systems developed to address specific challenges in the telecom industry:

1. Leverage Cloud Scale to Overcome Limitations of Deployed Protocols: He emphasised that the scale of cloud computing is critical to managing the massive demands of modern telecom networks. For instance, systems like TIPSY (Traffic Ingress Prediction SYstem) demonstrate how AI and ML can predict traffic ingress points across thousands of peering links, helping to avoid bottlenecks and ensure optimal traffic distribution.
2. Custom Learning Algorithms vs. Off-the-Shelf Solutions: While bespoke algorithms offer higher precision for niche applications, their complexity and deployment challenges often outweigh their benefits. Sharad argued for balancing innovation with practicality, advocating for leveraging pre-built AI and ML models wherever possible to streamline integration.
3. Mitigate Risks of AI Hallucinations through Careful System Design: Acknowledging the risks posed by unreliable AI outputs, he stressed the importance of robust system design. Using LLexus, an AI-driven incident management system, as an example, Sharad highlighted techniques like iterative plan generation, validation rules, and human auditing as essential safeguards against AI errors.

The talk also delved into broader trends shaping the telecom landscape:

• Cloudification of Telecom Infrastructure: The shift from hardware-based to software-based network functions, underpinned by cloud-native principles, has revolutionised telco infrastructure. This transformation facilitates rapid upgrades, reduces costs, and introduces new opportunities for AI-driven analytics.
• Challenges in Performance and Reliability: Ensuring high throughput, low latency, and carrier-grade reliability in cloudified networks remains a significant hurdle. Innovations like PAINTER and LLexus demonstrate how AI and ML are being applied to optimise these aspects.
• Emerging Business Models and Private Deployments: The integration of new radio technologies and virtualised network functions is driving novel revenue streams, such as private 5G/6G networks for mission-critical applications like factory automation.

Finally, Sharad’s keynote underscored how AI, ML, and cloud computing are reshaping the telecom industry, particularly in the era of 5G and the forthcoming 6G. By leveraging the scale of cloud infrastructure, balancing algorithmic complexity, and designing systems with resilience against AI pitfalls, the industry is poised to meet its ambitious goals of high bandwidth, low latency, and unparalleled reliability.

The video of his talk is embedded below and the slides are available here:

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AI/ML and Other ICT Industry Trends in the coming decades

At the Brooklyn 6G Summit (B6GS) 2023, top tier economist Dr. Jeff Shen from BlackRock, presented a talk from the industry perspective of AI (Artificial Intelligence) and investment. Jeff Shen, PhD, Managing Director, is Co-CIO and Co-Head of Systematic Active Equity (SAE) at BlackRock. He is a member of the BlackRock Global Operating Committee, BlackRock Systematic (BSYS) Management Committee and the BlackRock Asian Middle Eastern & Allies Network (AMP) Executive Committee.

How are firms reacting to AI? From 'ICT Industry Trends in the coming decades' by Jeff Shen, BlackRock at #B6GS - I am guessing the peak of mobile mention was the peak 5G hype in 2015/16. Hopefully the industry is now much wiser and won't repeat the 6G hype. pic.twitter.com/xjEiDtmSUZ

— Zahid Ghadialy (@zahidtg) November 5, 2023

In his talk he covered the history of how and where AI has been traditionally used and how the thinking around AI has changed over the last few decades. He then presented his view on if AI is just a fad or it's more than that. To illustrate the fact, he provided an example of how Generative AI market is expected to grow from $40 Billion in 2022 to $1.3 Trillion in 2032. 

There are many challenges that AI faces that one should be aware of; namely regulation, cyber threats and ethical concerns. In the US, AI touches the entire economy, from legal to healthcare. In their quarterly reporting, firms are now discussing AI and the larger tech companies are not afraid to grow inorganically in order to get more exposure to the trend. 

You can watch the whole of his talk embedded below, courtesy of IEEE Tv.

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Presentations from 2nd IEEE Open RAN Summit

The second IEEE SA (Standards Association) Open RAN summit, hosted by the Johns Hopkins University Applied Physics Lab, took place on 9-10 Aug 2023. It covered the topics related to the standardization of Open RAN including O-RAN Alliance, 3GPP, IEEE, various deployment scenarios, testing and integration, Open RAN security, RAN slicing, and RAN optimization among others. 

The videos of the presentations can be viewed on the summit page here or though the video playlist here.

The talk from Dr. Chih-Lin I, O-RAN Alliance TSC Co-Chair and CMCC Chief Scientist, Wireless Technologies on 'AI/ML impact, from 5.5G to 6G' is embedded below:

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What Is the Role of AI and ML in the Open RAN and 5G Future?

Artificial Intelligence and Machine Learning have moved on from just being buzzwords to bringing much needed optimization and intelligence in devices, networks and infrastructure; whether on site, on the edge or in the cloud.

New Study in 3GPP RAN3 on Artificial Intelligence and Machine Learning in NG-RAN - https://t.co/o4o4kq6Wpp via @3GPPLive #Free5Gtraining #3GPP #5G #ArtificialIntelligence #MachineLearning #AIML #RAN #Algorithms #SON #5GC #5GS pic.twitter.com/pvPlG9HpPC

— Free 5G Training (@5Gtraining) October 15, 2021

Qualcomm has been very active in talking about AI/ML in webinars and on their site. A detailed blog post looking at 'What’s the role of artificial intelligence in the future of 5G and beyond?' is available here. It was posted in time for a Light Reading webinar where Gabriel Brown, Principal Analyst – Mobile Networks and 5G, Heavy Reading and Tingfang Ji, Senior Director, Engineering - Wireless R&D, Qualcomm discuss the topic. The video is embedded below and slide deck is available here.

Standardizing AI/ML in cellular communication systems. From #Qualcomm presentation, The essential role of AI in the 5G future - How machine learning is accelerating wireless innovations in the new decade and beyond - https://t.co/fwxtoKIZKR#Free5Gtraining #3G4G5G #5G #AI #ML pic.twitter.com/YS5w8KXQuw

— Free 5G Training (@5Gtraining) September 27, 2021

Louis Scialabba, Senior Director of Marketing at Mavenir, looking at AI and Analytics spoke at Layer 123 conference on the topic, 'AI/ML for Next Gen 5G Mobile Networks'. His talk is embedded below and a blog post by him on the topic, 'The RIC Opens a New World of Opportunities for CSPs' is available here.

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ITU Standardization Bureau on Machine Learning for 5G

Last year I blogged about Global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks".  The grand challenge finale happened in December. All the recording and presentations are available here.

Back in October, Bilel Jamoussi from ITU presented a keynote to the 2020 IEEE 5G World Forum plenary session where he addressed the challenges of applying machine learning in networks, ITU’s ML toolkit, and ITU’s AI/ML in 5G Competition. IEEE Tv shared the presentation only in April so the competition part is a bit outdated. It does nevertheless an interesting 20 minute talk.

ITU Recommendation Y.3174, Framework for data handling to enable machine learning in future networks including IMT-2020 is available here.

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Anomaly Detection and other AI Algorithms in RAN Optimization

Yesterday I watched this very inspiring live chat that I would like to recommend to anyone who is interested in how machine learning techniques (aka "AI") can help to optimize and troubleshoot the Radio Access Network.

 [The real contents of in the video starts at approx. 42:00 min] 

My key takeaways from this fireside chat are: 

Verizon Wireless has enough data (100… 500 time series KPIs per cell) that they use to feed anomaly detection ML algorithms and this generates a huge number of alarms, but only a few actionable outputs. 

The “big elephant” (Nick Feamster) is to identify if these alarms indicating real problems that can and have to be fixed or if they just indicate a new behavior of e.g. a new handset or a SW version that was not present in the training phase of the ML algorithm and hence, its pattern is detected as a new “anomaly”. 

For Bryan Larish (Director Wireless AI Innovation, Verizon) the “big open problem” is “that it is not clear what the labels are” and “no standard training sets exist”. 

[For more details watch the video section between 52.00 min and 57:32 min and listen to Bryan’s experience!] 

In most cases Verizon seems to need subject matter experts to classify and label these anomaly alarms due to “the huge diversity” in data pattern. 

According to Bryan only for very few selected use cases it is possible to build an automated loop to fix the issue. Especially the root causes of radio interference are often mechanical or cabling issues that need manual work to get fixed. 

All in all it is my personal impression at the end of the session that anomaly detection is currently a bit overhyped and that the real challenges and problems to be resolved start after anomalies are detected.

Nevertheless, as Bryan summarizes: “ML is a very, very powerful tool.” 

However, strategically he seems not to see a lot of value in anomaly detection by itself, but rather: “Can we use machine learning (results) to change how we build networks in the future?”