AI/ML
Nokia and Vodafone to use machine learning on Google Cloud to detect network anomalies
Nokia and Vodafone have partnered to jointly develop a new machine learning (ML) system designed to detect and remediate network anomalies before they impact customers. Based on Nokia’s Bell Labs algorithm, the Anomaly Detection Service product runs on Google Cloud and is already being rolled out across Vodafone’s pan-European network.
In a joint statement, the partners said the ML system quickly detects and troubleshoots irregularities, such as mobile site congestion and interference, as well as unexpected latency, that may have an impact on customer service quality. Following an initial deployment in Italy on more than 60,000 LTE cells, Vodafone said it will be extending the service to all its European markets by early 2022, and there are plans to eventually apply it on the company’s 5G and core networks.
Vodafone added that it expects that around 80 percent of all its anomalous mobile network issues and capacity demands to be automatically detected and addressed using Anomaly Detection Service.
Vodafone’s deal with Nokia signed last year complements its recent six-year agreement with Google Cloud to jointly build integrated cloud-based capabilities backed by hubs of networking and software engineering expertise.
The platform, called ’Nucleus’, will house a new system ‘Dynamo’, which will drive data throughout Vodafone to enable it to more quickly offer its customers new, personalized products and services across multiple markets. Dynamo is expected to help Vodafone to tailor new connectivity services for homes and businesses through the release of new features such as providing a sudden broadband speed boost.
Capable of processing around 50 TB of data per day, Nucleus and Dynamo are considered “industry firsts”. Being built in-house by Vodafone and Google Cloud specialist teams, the project involves up to 1,000 employees of both companies located in Spain, the UK and the US.
Vodafone said it has already identified more than 700 use-cases to deliver new products and services quickly across its markets, support fact-based decision-making, reduce costs, remove duplication of data sources, and simplify and centralize operations.
Johan Wibergh, Chief Technology Officer, Vodafone, said: “We are building an automated and programmable network that can respond quickly to our customers’ needs. As we extend 5G across Europe, it is important to match the speed and responsiveness of this new technology with a great service. With machine learning, we can ensure a consistently high-quality performance that is as smart as the technology behind it.”
Amol Phadke, Managing Director, Telecom Industry Solutions, Google Cloud, said:
“We are thrilled to partner with Nokia and Vodafone to deliver a data- and AI-driven solution that scales quickly and leverages automation to increase cost efficiency and ensures seamless customer experiences across Europe. As behaviors change and the data needed for analysis increases in velocity, volume, and complexity, automation and a cloud-based data platform are now key in making fast and informed decisions.”
Anil Rao, Research Director, Analysys Mason, said: “Vodafone’s anomaly detection use case, developed in partnership with Nokia and run on Google Cloud, automates root-cause analysis for efficient network planning, optimization, and operations. This type of partnership provides a new opportunity for operators to rethink data management and increase the focus on use cases and application development.”
Raghav Sahgal, President of Cloud and Network Services, Nokia, said: “This first commercial deployment of Anomaly Detection Service with Vodafone on Google Cloud provides a great boost to customer service. It not only addresses the critical need to quickly detect and remedy anomalies impacting network performance using machine learning-based algorithms, but it also highlights Nokia’s technology leadership and the deep technical expertise of Nokia Bell Labs.”
Vodafone said it will convert its entire SAP environment to Google Cloud, including the migration of its core SAP workloads and key corporate SAP modules such as SAP Central Finance.
Project Marconi: Machine Learning-based RAN application to boost 5G spectrum capacity
At MWC today Intel and Capgemini Engineering unveiled the industry’s first Machine Learning-based RAN application to boost 5G spectrum capacity. Capgemini says their solution gives mobile network operators a significant advantage to monetize 5G services faster. Entitled “Project Marconi,” it conforms to O-RAN (Open Radio Access Network) guidelines to maximize spectrum efficiency. The solution intelligently boosts subscriber quality of experience (QoE) with real-time predictive analytics.
Project Marconi is the industry’s first Artificial Intelligence / Machine Learning (AI/ML) based radio network application for 5G Medium Access Control (MAC) scheduler. Optimized with Intel AI Software and 3rd Gen Intel Xeon Scalable processors.
Network providers globally have invested heavily in spectrum and are looking for solutions to develop and gain 5G services faster. According to the Global Mobile Suppliers Association, the total value of spectrum auctions reached over $27 billion in 2020.
Capgemini’s application (running on Intel Architecture) increases the amount of traffic each cell can handle. It allows operators to serve more subscribers and deliver an outstanding experience, while launching new Industry 4.0 services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) use cases.
Walid Negm, Chief Research and Innovation Officer at Capgemini Engineering said: “Our teams worked closely with Intel to create a truly innovative solution that can really move the needle for operators. We gathered and utilized over one terabyte of data and conducted countless test runs with NetAnticipate5G to fine-tune the predictive analytics to meet diverse operator requirements. In short, machine learning can be deployed for intelligent decision-making on the RAN without any additional hardware requirement. This makes it cost efficient in the short run and future proof in the long run as we move into Cloud Native RAN implementations.”
Cristina Rodriguez, VP of Wireless Access Network Division at Intel said: “Our 3rd Gen Intel Xeon Scalable processors with built-in AI acceleration provide high performance for deep learning on the Net Anticipate 5G platform. Together, our collaboration delivered ultra-fast inference data to enhance the Open-Source ML libraries resulting in an intelligent RAN that can predict and quickly react to subscriber coverage requirements while reducing TCO.”
Capgemini deployed its NetAnticipate5G and RATIO O-RAN platform to introduce advanced AI/ML techniques. The AI powered predictive analytical solution forecasts and assigns the appropriate MCS (modulation and coding scheme) values for signal transmission through forecasting of the user signal quality and mobility patterns accurately. In this way, the RAN can intelligently schedule MAC resources to achieve up to 40% more accurate MCS prediction and yield to 15% better spectrum efficiency in the case studies and testing. As a result, it delivers faster data speeds, better and more consistent QoE to subscribers and robust coverage for use cases that rely on low latency connectivity such as robotics-based manufacturing and V2X (vehicle-to-everything).
Project Marconi will be demonstrated live at Intel virtual booth and O-RAN virtual booth during MWC 2021.
More information can be found on Capgemeni’s website.
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Last week, Capgemini Research Institute released a report titled, “Accelerating the 5G Industrial Revolution: State of 5G and edge in industrial operations” stating that industrial 5G adoption is still at the ideation and planning stages, with only 30% of industrial organizations having moved to the pilot stage or beyond. This means there is a huge window of opportunity for telcos and those industrial organizations that are yet to make a move.
Signaling a paradigm shift, 40% of industrial organizations surveyed expect to roll out 5G at scale at a single site within two years, and the experience of early adopters could persuade others to make the move. 5G trials and early implementations are delivering strong business benefits, with 60% of early adopters saying that 5G has helped to realize higher operational efficiency, while 43% saying they have experienced increased flexibility.
The study also found that industrial organizations are optimistic that 5G will drive revenues by enabling the introduction of new products, services, and business models. In fact, 51% of industrial organizations plan to leverage 5G to offer new products, and 60% plan to offer new services enabled by 5G.
Furthermore, industrial organizations are aware of the role of edge computing in their 5G initiatives and view it as essential to realizing the full potential of 5G. 64% of organizations plan to adopt 5G-based edge computing services within three years, driven by the increased performance, reliability, data security and privacy it offers. More than a third of industrial organizations across sectors surveyed prefer to deploy private 5G networks, with interest in private 5G networks led by the semiconductor and high-tech sector (50%), followed by aerospace and defense (46%).
“Industrial 5G is a key catalyst in unlocking the potential of intelligent industry and accelerating data-driven digital transformation,” comments Fotis Karonis, Group Leader of 5G and Edge Computing at Capgemini. “Enterprises need to take advantage of the benefits of 5G by engaging with the ecosystem to tap into the shared expertise and co-create innovative, sustainable solutions for tomorrow. An element of iteration is required, but organizations should seek to leverage the 5G ecosystem to jointly test solutions and progress with full-scale 5G adoption, fine-tuning the approach as the ecosystem evolves.”
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About Capgemini:
Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of 270,000 team members in nearly 50 countries. With its strong 50 year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group reported in 2020 global revenues of €16 billion.
References:
https://capgemini-engineering.com/uk/en/industries/communications/marconi-project/
https://www.dropbox.com/sh/i6j3jmvro58tv9q/AAC2UGWH1FvozJcVXElcbGnwa?dl=0
Highlights of AT&T CFO and CTO remarks at Morgan Stanley Investor Conference
Network quality driven by significant investments in 5G and fiber:
AT&T believes that its recent and anticipated network investments will bolster its network foundation to compete as the need for high-quality connectivity only continues to increase. At a Morgan Stanley European Investor Conference, AT&T CFO John Stephens indicated that AT&T’s integrated fiber strategy is expected to improve the company’s connectivity offering for both consumer and enterprise markets and enhance its 5G network quality in a cost-efficient manner.
COVID-19 Impact:
AT&T CTO Andre Fuetsch said: “Obviously what happened was everyone basically started working, started schooling from home, and all of a sudden we had to readjust our lives to work from home, learn from home, and all of a sudden we had to adapt very quickly to that. Within our homes, we had to have these different personas that we normally don’t do — whether it’s doing your day job, performing that duty, helping your children get online so they can do their schooling, and then all the other things in life. That was a blurring, in a way, of these sort of enterprise and consumer segments coming together.”
“All of this technology is great, but at the end of it, we are humans and anything we can do to help facilitate [and] build better, stronger human connections” will benefit society at large, Fuetsch added. “This year we’re really getting pushed and challenged to do that. I really think this type of technology is just going to make things better.”
Artificial Intelligence (AI) Improves Operations:
Some of these technologies, like Artificial Intelligence (AI), are already helping AT&T improve its operations, especially among its field technicians, he said, noting that AT&T’s entire routing and scheduling program relies heavily on AI.
“Any given day we have 35,000 network technicians driving around in trucks installing, and repairing, and maintaining our network. It’s essentially a very complex logistics algorithm and, as you can imagine with a company of our scale, just a single percentage improvement in efficiencies can lead to big, big dollars,” Fuetsch said.
AT&T is also trialing the use of drones with computer vision analytics to help improve inspections of its roughly 70,000 cell sites. When those drones take flight, they are scanning towers, looking for excessive heat dissipation, corrosion, loose cables, and bird nests, among other signs that indicate a required repair.
“All of this is getting fed back into a neural network, which is basically AI based,” and that program identifies the repair checklist, the technician and skill sets required, and the parts needed to remedy the problem, Fuetsch said.
AT&T’s experiences here and elsewhere gives him confidence that “the camera is still and will be the killer app” for the foreseeable future. However, the use of cameras is undergoing dramatic changes, he said.
“We carry about 400 petabytes a day across our network. About 50% of that traffic we carry is video traffic. Most of that is going out in a sort of downstream way. The future is going to be about upstream,” Fuetsch said.
Use of Video Cameras:
Fuetsch envisions new applications that “can help better manage our lives through a simple video camera” with the aid of video analytics and sensing. These advancements are occurring not just despite the scourge of COVID-19, but rather because of it in some ways as well, he said.
“This pandemic has really created some new norms here. I think the good news for operators is connectivity is so important and so relevant for everything we do. As we go into 2021, certainly with hopefully a light at the end of the tunnel here in terms of the pandemic with the latest news we’re hearing about vaccines, I’m actually very optimistic.”
“As we go into 2021, certainly with hopefully a light at the end of the tunnel here in terms of the pandemic with the latest news we’re hearing about vaccines, I’m actually very optimistic,” Fuetsch added.
References:
https://about.att.com/story/2020/john_stephens_update.html
ITU-T SG13 FG on “Machine Learning (ML) for Future Networks including 5G” completes mission; 10 technical specs approved
Introduction:
ITU-T Study Group 13 Focus Group on Machine Learning for Future Networks including 5G (FG ML5G) has accomplished its mission. The FG ML5G was active from January 2018 until July 2020.
During its lifetime, FG ML5G delivered ten technical specifications.. Four of those specifications have already been approved by ITU-T SG13 and published by ITU-T. Six further technical specifications are being considered by ITU-T SG13. These ten technical specifications are publicly available free of charge. Please refer to ITU-T FG ML5G webpage to download the documents. [All ITU-T Focus Group publications are available for download at ITU-T Focus Group webpage]
Deliverables processed by ITU-T SG13 and published by ITU-T are:
Y.Sup55: ITU-T Y.3170-series – Machine learning in future networks including IMT-2020: use cases
This Supplement describes use cases of machine learning in future networks including IMT-2020. For each use case description, along with the benefits of the use case, the most relevant possible requirements related to the use case are provided. Classification of the use cases into categories is also provided.
ITU-T Y.3172: Architectural framework for machine learning in future networks including IMT-2020
ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020. A set of architectural requirements and specific architectural components needed to satisfy these requirements are presented. These components include, but are not limited to, an ML pipeline as well as ML management and orchestration functionalities. The integration of such components into future networks including IMT-2020 and guidelines for applying this architectural framework in a variety of technology-specific underlying networks are also described.
ITU-T Y.3173: Framework for evaluating intelligence levels of future networks including IMT-2020
ITU-T Y.3173 specifies a framework for evaluating the intelligence of future networks including IMT-2020 and a method for evaluating the intelligence levels of future networks including IMT-2020 is introduced. An architectural view for evaluating network intelligence levels is also described according to the architectural framework specified in Recommendation ITU-T Y.3172.
In addition, the relationship between the framework described in this Recommendation and corresponding work in other standards or industry bodies, as well as the application of the method for evaluating network intelligence levels on several representative use cases are also provided.
ITU-T Y.3174: Framework for data handling to enable machine learning in future networks including IMT-2020
ITU-T Y.3174 describes a framework for data handling to enable machine learning in future networks including International Mobile Telecommunications (IMT)-2020. The requirements for data collection and processing mechanisms in various usage scenarios for machine learning in future networks including IMT-2020 are identified along with the requirements for applying machine learning output in the machine learning underlay network. Based on this, a generic framework for data handling and examples of its realization on specific underlying networks are described.
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This document is at an advanced stage in ITU-T SG13:
Draft Recommendation ITU-T Y.3176: “ML marketplace integration in future networks including IMT-2020”
This document is a draft Recommendation under study by Q20 of SG13. This draft Recommendation provides the architecture for integration of ML marketplace in future networks including IMT-2020. The scope of this draft Recommendation includes: – Challenges and motivations for ML marketplace integration – High level requirements of ML marketplace integration – Architecture for integration of ML marketplace in networks.
The July 2020 ITU-T SG13 meeting started the approval process for this draft new Recommendation, which is largely based on the output of the FG ML5G.
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Deliverables which FG ML5G submitted to ITU-T SG13 for consideration:FG ML5G specification:
“Requirements, architecture and design for machine learning function orchestrator”
This technical specification discusses the requirements for machine learning function orchestrator (MLFO). These requirements are derived from the use cases for machine learning in future networks including IMT-2020. Based on these requirements, an architecture and design for the machine learning function orchestrator is described.
FG ML5G specification: “Serving framework for ML models in future networks including IMT-2020”
This specification describes a serving framework for ML models in future networks including IMT-2020. The specification includes requirements and architecture components for such a framework.
FG ML5G specification: “Machine Learning Sandbox for future networks including IMT-2020: requirements and architecture framework”
Use cases for integrating machine learning (ML) to future networks including IMT-2020 has been documented in Supplement 55 and an architecture framework for this integration was specified in ITU-T Y.3172. However, network stakeholders are apprehensive about using ML-driven approaches directly in live networking systems because it can lead to unexpected situations that can degrade KPIs. This is mostly due to the apparent complexity of ML mechanisms (e.g., deep learning), the incompleteness of the available training data, the uncertainty produced by exploration-exploitation approaches (e.g., reinforcement learning), etc. In the face of such impediments, the ML Sandbox emerges as a potential solution that allows mobile network operators (MNOs) for improving the degree of confidence in ML solutions before their application to the network infrastructure. This technical specification deals with the requirements, architecture, and implementation examples for ML Sandbox in future networks including IMT-2020.
FG ML5G specification: “Machine learning based end-to-end network slice management and orchestration”
This document proposes the framework and requirements of machine learning based end-to-end network slice management and orchestration in multi-domain environments.
FG ML5G specification: “Vertical-assisted Network Slicing Based on a Cognitive Framework”
This technical specification proposes a new framework that enables vertical QoE-aware network slice management empowered by machine learning technologies.
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Conclusions:
The activities of the FG ML5G were concluded and its mandate was accomplished. SG13 closed the FG ML5G while recognizing the FG ML5G chairman Prof. Dr. Slawomir Stanczak (Frauenhofer HHI, Germany) and his management team, active contributors and all the FG members.
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Contact: |
Leo Lehmann |
Tel: 41 58460 5752 |
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References:
https://www.itu.int/en/ITU-T/focusgroups/ml5g/Pages/default.aspx
COVID-19 has changed how we look at telecom infrastructure, cloud and AI
by John Strand, Strand Consult, Denmark; edited for clarity by Alan J Weissberger
The coronavirus (COVID-19) crisis has proven that telecom infrastructure is critically important. Telecommunications networks delivered service during the lockdown, enabling many to continue to work, learn, shop, and access healthcare.
Policymakers will likely revisit regulation for telecom networks, not only to optimize network investment, but to improve security. Indeed, policymakers will also realize that security which has focused to date on the transport layer of networks is leaving the access and applications layers vulnerable.
While the focus on Huawei is long overdue, the discussion of network security and Huawei’s role are oversimplified. It is insufficient to address only one aspect of conventional components of networks: access, core, and transport.
The bigger issues are how end-user data will be protected while stored in the network/cloud and processed by artificial intelligence (AI) agents? Also, how will connected devices on Internet of Things (IoT) networks and other applications (such as smart city solutions) can be secured?
Historically network connectivity was likened to the dumb pipe, the medium which transmits data. The “smart parts” of the network were the edge and the core, where users access networks and where information processing occurs. These actions have become more complex with third party providers of AI and cloud computing. Naturally, these models don’t fit 5G because intelligence must exist throughout the network. However, telecom regulation has been associated with these three traditional functions.
Now that networks have evolved, it’s time for telecom regulation to evolve. If the goal of security measures is to reduce the risk and vulnerability of exposure to Chinese state-owned and affiliated firms, then policymakers need holistic frameworks that address the multiple aspects of network security at its various layers: application, transport, and access.
Indeed, the singular focus on Huawei in connectivity misses the fact that Huawei sells products for the other layers, and that many other Chinese state-owned firms should be scrutinized. For example, Baidu, WeChat, Alibaba, and Huawei provide AI solutions in the Applications layer; Huawei and ZTE in transport; and Huawei smartphones and laptops by Lenovo (the world’s leading maker of laptops as well as a leader in servers).
Strand Consult has described this in the research note The debate about network security is more complex than Huawei. Look at Lenovo laptops and servers and the many other devices connected to the internet.
The EU’s 5G Toolbox is the first step towards greater security and accountability for a discrete part of 5G network transport, but it does not address all elements of 5G security nor other layers. In performing its security assessment, the United Kingdom looked beyond 5G mobile Radio Access Networks (RAN) and Core to other network layers, types and technology, notably wireline networks. Policymakers need a broader focus than 5G when assessing the security of telecommunications networks in the future. Policymakers need to look at Huawei’s movement into cloud and AI solutions in the application layer as well as the many state-owned Chinese firms in the access layer like Lenovo.
In China, Huawei is vertically integrated and delivers a suite of products and services for all the layers of a network: it transports the data; it provides access through end user devices, and it provides the applications in the form of AI and cloud solutions. While European policymakers debate issues of RAN and core, Huawei is busy selling other solutions for the rest of the network: smartphones, routers, AI, and cloud solutions. As restrictions tighten on Huawei’s network products, the company will naturally push other business lines to compensate for lost revenue. A large operator in Europe works with Huawei on a joint Chinese-German cloud platform, and the reference customer for this solution is the European Organization for Nuclear Research, CERN in Switzerland. It is not logical how Huawei which can be deemed high risk for telecommunications and military networks, but somehow neutral for nuclear research. The agreement for the project is four years old; the question is whether such a project will be acceptable for political and security standards going forward.
Vertical integration was the standard model for traditional state-owned telecommunications. The government built a telephone network (the wires and switches); it delivered a single service – telephony; and it sold the end user device, typically a classic phone. Privatizing networks was about opening up the value chain to different kinds of providers. This worked well in mobile networks; different firms specialized for different parts of the value chain. However, Huawei is driving the decentralized chain back to the state-centered concept, perhaps fitting for its practice in China where it partners with the government to deliver full-service surveillance solutions.
Policymakers, regulators, and competition authorities have long been skeptical of vertical integration in telecommunications, and it was frequently a way to control traditional telecom operators by demanding that the divest certain parts of their business or by prohibiting certain acquisitions.
COVID-19 has proven that telecommunications networks are vital infrastructure at all layers and levels. It’s not just military and public safety networks that need to be secure. Everyone needs to have secure networks if we are live in a digital society. If politicians and telecom operators don’t recognize this, network users do. Change is being driven by companies which themselves are increasingly victims of cyber-attacks. Companies are putting increased pressure on telecom operators and governments to do more to make networks secure.
John Strand of Strand Consult
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Six big issues on the future of telecom regulation:
Strand Consult believes that governments will take a broader view about network security. Here are six categories of issues for policymakers to consider.
- What is critical infrastructure, and how will it be defined in the future? Historically, critical infrastructure had to do with physical and digital network assets which are required for physical and economic security, health, and safety. Indeed, there are many vital network assets deemed “critical” including those for chemicals, communications, manufacturing, dams, defense, emergency services, energy, financials, food/agriculture, government facilities, healthcare, information technology, nuclear reactors, transportations systems, and water/wastewater. Are these networks equally prioritized? What are the security concerns and protocols for each, both on the physical and cyber fronts? Do some have greater security than others? How does this change in the COVID-19 world?
- What are the government’s responsibilities to ensure the security of communications infrastructure?What are the minimum requirements to restrict a vendor? How is this balanced with requirements for fair and open processes for bids and tenders?
- What are the relevant communications networks to secure? Is it enough to focus on 5G mobile RAN and core or should security requirements apply to wireline networks, satellite, Wi-Fi, 3G/4G and so on?
- Is it sufficient only to address the transport element of network security? How will security be ensured for the storage and processing of data, for example on the computers, laptops, and servers provided by Chinese state-owned Lenovo where China’s rules for surveillance and espionage also apply? What about apps like TikTok and Huawei’s AI and cloud solutions?
- Who should perform the security assessment? Telecom operators, military departments, intelligence agencies, private security consultants, law enforcement, or some other actor?
- How will shareholders account for increased security risk in networks? Have shareholder asked relevant questions about operators’ security practices and the associated risk?
Strand Consult believes it is dangerous for telecom operators to make the decision about Huawei themselves without involving the authorities.
There are a lot of arguments for why telecommunications companies should involve the competent and relevant authorities. Telecom operators must understand that If telecommunications companies assume responsibility as those who assess each supplier in relation to national security, they will be held responsible when things go wrong.
When you take on a responsibility, you also take on risk. Thus, shareholders are exposed to increasing risk when using high risk vendors. The historical facts show that telecommunications companies have been wrong in the past when assessing cooperation with partners which have proven to be corrupt. Some partnerships have cost shareholders billions of euros. In practice, operators are limited in their ability to judge whether partners and vendors are trustworthy.
Strand Consult’s research shows that is not only government, intelligence, and security officials who are concerned about companies like Huawei. Nor is it just telecom operators which build and run networks. It is the small, medium, and large enterprises that use networks that fear that their valuable data will be surveyed, sabotaged, or stolen by actors associated with the Chinese government and military. Consequently, it is the clients of telecom operators which push to restrict Chinese made equipment from networks. This is described in this research note, The pressure to restrict Huawei from telecom networks is driven not by governments, but the many companies which have experienced hacking, IP theft, or espionage.
What the future looks like – just ask the banks:
If you want to see the future of the telecom industry, look at what happened with banking. European banks have been required to implement Anti-Money Laundering (AML) and the Counter Terrorist Financing (CFT). About 10% of European banks employees are today working with compliance. Telecom authorities, defense officials, and other policymakers and will likely see cybersecurity is vital for Europe and that telecom infrastructure is critically important. So just as the banks have been put under a heavy regulatory regime to address corruption and financial crimes, the telecom industry will be required to implement deterrence of cyberattacks.
In practical terms, the authorities in the EU and in each nation state will likely make some demands that challenge the network paradigm that telecommunications companies operate today. The rules will likely be so rigid that they will effectively eliminate Huawei and other Chinese companies from being vendors without making explicit bans. However, it won’t be governments alone driving the charge. Corporate customers of telecom networks, companies that have experienced hacking, IP theft, or espionage, will also join the effort. This is described in this research note, The biggest taboo in European telecom industry is the cost of cybersecurity – just ask the banks.
Copyright 2020. All rights reserved
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About Strand Consult:
Strand Consult, an independent company, produces strategic reports, research notes and workshops on the mobile telecom industry.
For 25 years, Strand Consult has held strategic workshops for boards of directors and other leaders in the telecom industry. We offer strategic knowledge on global regulatory trends and the experience of operators worldwide packaged it into a workshop for professionals with responsibility for policy, public affairs, regulation, communications, strategy and related roles.
Learn more about John Strand: www.understandingmobile.com
Learn more about Strand Consult: www.strandreports.com
Strand Consult
Gammel Mønt 14
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[email protected]
Juniper Research: Network Operators to Spend Billions on AI Solutions
A new study from Juniper Research has found that total network operator spending on AI solutions will exceed $15 billion by 2024; rising from $3 billion in 2020. The research identifies network optimisation and fraud mitigation solutions as the most highly sought-after AI based services over the next 4 years. AI-based solutions automate network functionalities including routing, traffic management and predictive maintenance solutions.
For more insights, download our free whitepaper, How AI Analytics will Boost Operators’ Revenue.
Network Optimisation & Fraud Prevention Driving Adoption in Developed Markets
The new research, AI Strategies for Network Operators: Key Use Cases & Monetisation Models 2020-2024, found that operators in developed regions, such as North America and Europe, would account for over 40% of AI spend by 2024, despite only accounting for less than 20% of global subscribers. It predicts that growing demand for operational efficiencies will drive operators in these regions to increase their overall investment into AI over the next 4 years.
The research urges operators to embrace a holistic approach to AI implementation across service operations, rather than applying separate AI strategies to individual use cases. It suggests network operators leverage AI to unify internal data resources and encourage cross-functional insight sharing into network efficiencies to maximise the benefits of collaboration across internal teams.
The research predicts that AI spend by Emerging Markets operators will exceed $5 billion by 2024, rising from only $900 million in 2020. It found that this growth will be driven largely by operators exploring early use cases of AI before expanding the presence of AI in their networks to include more comprehensive services.
The report forecasts that Indian Subcontinent and Africa & Middle East will experience the highest growth in spend on AI services, with operator spend in both regions forecast to grow over 550% over the next 4 years. It anticipates that operators in these regions will initially invest in AI-based CRM (Customer Relationship Management) solutions that yield immediate benefits.
Related post:
SK Telecom: Over-the-Air Transmission on Multi-Vendor Commercial Stand Alone 5G Network
SK Telecom today announced that it has successfully accomplished the world’s first standalone (SA) 5G data session on its multi-vendor commercial 5G network in South Korea.
Editor Notes:
1. T-Mobile claimed they were the first carrier to successfully test 5G SA operation which we covered in this article: T-Mobile Claim: 1st Standalone 5G Data Session on a Multi-Vendor Radio and Core Network.
2. Definition: 5G Stand Alone (SA) refers to using 5G specifications (in cells/base stations and endpoints) for both signalling and information transfer. All 5G deployments to date use NSA operation which uses 4G-LTE signaling and 4G-Evolved Packet Core (EPC) as well as LTE network management
SA operation requires the new 5G Packet Core (5GC) architecture from 3GPP instead of relying on the EPC to allow the deployment of 5G without the LTE network.
Ericsson provides 5G Standalone 5G facts:
- New cloud-native 5G Core
- Simplified RAN and device architecture
- The only option to provide same 5G coverage for low band as legacy system
- Supports advanced network-slicing functions (not standardized yet – may be in 3GPP Release 16)
- Facilitates a wider range of use cases for new devices
- Brings ultra-low latency (that won’t happen to completion of 3GPP Release 16 in June 2020 if then)
3. South Korea was the first country in the world to launch 5G services and currently has some of the most wide ranging 5G networks anywhere in the world. That’s largely due to government co-ordination with the three large South Korean wireless network operators – SK Telecom, KT and LG Uplus.
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With this major breakthrough in 5G, SK Telecom says it is now fully set to provide standalone 5G services. SK Telecom said that it plans to launch the world’s first 5G SA service in the first half of this year.
The standalone 5G data call took place on January 16, 2020 in Busan, the second largest city in Korea, using SK Telecom’s commercial 5G network deployed in that region.
To achieve this standalone 5G milestone, the company applied standalone 5G New Radio (NR) software to its existing non-standalone (NSA) 5G base stations, and completed multi-vendor interoperability between network equipment of Ericsson and Samsung.
SK Telecom has also applied key 5G technologies such as network slicing and mobile edge computing (MEC) to its standalone 5G network. Network slicing is being highlighted as an essential technology for providing optimal support for different types of 5G services by partitioning a single physical network into multiple virtual mobile networks. MEC minimizes latency by providing a shortcut for data transmission through installation of small-scale data center at 5G base station or router. MEC can improve the performance of ultra-low latency 5G services such as cloud gaming, smart factory and autonomous driving.
“With the successful standalone 5G data call on our multi-vendor commercial 5G network, we are now standing on the threshold of launching standalone 5G service, a key enabler of revolutionary changes and innovations in all industries,” said Park Jong-kwan, Vice President and Head of 5GX Labs of SK Telecom. “SK Telecom will offer the best 5G networks and services to realize a whole new level of customer experience in the 5G era.”
About SK Telecom
SK Telecom (NYSE: SKM) is the largest mobile operator in Korea with nearly 50 percent of the market share. As the pioneer of all generations of mobile networks, the company has commercialized the fifth generation (5G) network on December 1, 2018 and announced the first 5G smartphone subscribers on April 3, 2019. With its world’s best 5G, SK Telecom is set to realize the Age of Hyper-Innovation by transforming the way customers work, live and play.
Building on its strength in mobile services, the company is also creating unprecedented value in diverse ICT-related markets including media, security and commerce.
For more information, please contact [email protected] or [email protected].
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Related—Big 3 Korean carriers vow to offer seamless telecom service during lunar new year:
Korea’s big three mobile network operators have committed to provide “seamless” connectivity over the Korean Lunar New Year festivities, according to reports in the Korean press.
SK Telecom, KT and LG Uplus all committed to provide extra capacity in their networks over the Seollal period, as Koreans travel home for the holiday, placing extra strain on the country’s networks, particularly in public spaces such as train stations and along the country’s roads.
“To respond to possible data traffic jams, LG Uplus checked out base stations for 4G and 5G networks and will run an emergency situation room. We will also increase the number of technicians for highly-populated areas such as airports,” the company told journalists from the Korea Times.
SK Telecom predicted that it would see a 24 per cent hike in data traffic over the holiday period, as vacationing Koreans make use of high demand services like UHD video streaming and geolocation services. SK Telecom identified 750 busy areas that will receive special attention over the period, while KT said that it would be proactively managing traffic in 970 locations across the country.
All three mobile network operators have said that they will have more technicians and service staff working over the holiday to help them cope with the increase in demand.
Technicians of SK Telecom check network quality at an airport, Sunday. Photo Courtesy of SK Telecom
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LG Uplus will also run an emergency response center at its office in Magok, western Seoul during the holiday.
The company has completed inspections of 4G and 5G base stations installed at highway rest areas, SRT and KTX train stations and bus terminals throughout the country.
“To respond to possible data traffic jams, LG Uplus checked out base stations for 4G and 5G networks and will run an emergency situation room. We will also increase the number of technicians for highly-populated areas such as airports,” the company said.
KT designated 970 places including highways, department stores, bus terminals, airports, train stations and other busy areas in the country as data quality management zones.
South Korea was the first country in the world to launch 5G commercial services with the big three wireless carriers doing so on the same day.
References:
https://www.koreatimes.co.kr/www/tech/2020/01/133_282175.html
Market Research Firms say Telcos Need to Invest in AI now!
Due to ever increasing demand for data, saturated mobile markets, and stiff opposition from cloud companies, global telecom network providers are facing difficult times. These market pressures have led to vicious price wars for mobile services and, as a result, declining average revenue per user (ARPU). This is especially true in India where Vodafone Idea and Bharti Airtel have recently announced huge losses, write-downs as their share prices collapsed.
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Artificial Intelligence (AI) use in Telecommunications:
For many global telecoms, shoring up market share under today’s pressures while also future-proofing operations means having to invest in AI. The telecom industry is expected to invest $36.7 billion annually in AI software, hardware, and services by 2025, according to Tractica.
Through its ability to parse large data sets in a contextual manner, provide requested information or analysis, and trigger actions, AI can help telecoms cut costs and streamline by digitizing their operations. In practice, this means leveraging the increasingly vast gold mine of data generated by customers that passes through wireless networks — the amount of data that moves through AT&T’s wireless network has increased 470,000% since 2007, for example.
AI applications in the telecommunications industry use advanced algorithms to look for patterns within the data, enabling telcos to both detect and predict network anomalies, and allowing them to proactively fix problems before customers are negatively impacted.
Some forward-thinking telcos have focused their AI investments on four main areas:
- Network optimization
- Preventive maintenance
- Virtual Assistants
- Robotic process automation (RPA)
In these areas, AI has already begun to deliver tangible business results, according to blogger Liad Churchill
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Meanwhile, a Tractia report on AI for Telecommunications Applications identifies the following functions which will benefit from AI:
- Network Operations Monitoring & Management
- Customer Service & Marketing VDAs (Voluntary Disclosure Agreements)
- Intelligent CRM Systems
- Customer Experience Management
- Cybersecurity & Fraud Mitigation
- Other Use Cases
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Here are a few takeaways from the AI in Telecommunications report by Business Insider Intelligence:
- Telecoms have long struggled with their customer experience image: In 2018, telecommunications had the lowest average Net Promoter Score (NPS), a measure of how favorably a company is viewed by customers, of any industry.
- Companies that use advanced analytics, which can be accessed via AI, to improve this image and the overall customer experience are seeing revenue gains and cost reductions within a few years of adoption.
- Most (57%) executives believe that AI will transform their companies within three years, per Deloitte’s State of AI in Enterprise.
- Overall, telecoms should focus on a hybrid organizational model to move beyond pilots to launch full-scale AI solutions that can have the biggest impact on their companies.
References:
https://www.businessinsider.com/the-ai-in-telecommunications-report-2019-7
https://techsee.me/blog/artificial-intelligence-in-telecommunications-industry/
Cisco announces AI/ML and Security Software to transform networks
The Network Gets Smarter, Simpler and More Secure with Artificial Intelligence and Machine Learning:
Cisco today announced software innovations designed to make managing and securing networks easier. As today’s businesses increasingly invest in digital technologies, IT teams are struggling under the amplified workload. To alleviate this burden and allow IT to focus on delivering innovation, Cisco is introducing new artificial intelligence and machine learning capabilities to allow IT teams to function at machine speed and scale through personalized network insights. As part of its broadened capabilities offering, Cisco is also unveiling innovations to more effectively manage users and applications across the entire enterprise network – from campus networks and wide-area networks, to data centers and the IoT edge.
IT teams currently face a daunting challenge. According to 451 Research, nearly two-thirds of organizations report that their IT teams are facing increased workloads; but increased IT headcount is in the cards for only about one-third of companies in the coming year. At the same time, it has never been more imperative for IT to deliver great digital experiences in this hyper-competitive landscape. Bridging the gap between the needs of a business and the resources available requires innovative network automation and analytics tools, powered by data and underpinned by artificial intelligence and machine learning.
Cisco’s new capabilities will grant IT teams:
- More Visibility: No two networks are the same. Environments are always changing. Cisco continuously collects relevant data from local networks and correlates it against the aggregate deidentified data set to create highly individualized network baselines. These baselines constantly learn and adapt as the number of devices, users and applications evolves, and as environments change.
- Greater Insights: Network complexity has grown beyond the human scale of processing. Cisco uses machine learning to correlate the immense amount of data coming from the network against the individualized network baselines to uncover the issues that will have the greatest impact on the network. This improves issue relevancy, alerting IT of the issues that matter most. It also discovers trends and patterns, so IT can pre-emptively identify issues before they become a problem.
- Guided Actions: Cisco uses machine reasoning algorithms and automated workflows to perform the logical troubleshooting steps that an engineer would execute to resolve a problem. This helps IT detect issues and vulnerabilities, analyze the root cause and execute corrective actions faster than ever.
“As the pace of change and diversity of the environment continues to rapidly evolve, Cisco is committed to continually simplifying our solutions,” said Scott Harrell, Senior Vice President and General Manager of Cisco’s Enterprise Networking Business. “Artificial intelligence and machine learning can enable businesses to efficiently discern which issues to prioritize, becoming more nimble and proactive. This will have a profound effect on network operations and the IT teams that run them. At Cisco, we’re future proofing our networks and the workforce through automation and intelligence.”
Reducing Complexity with the Multidomain Network
To help customers simplify the unprecedented complexity of modern IT, Cisco is building an architecture that spans every domain of the intent-based network — campus, branch, WAN, IoT, data center and cloud. Cisco has created solutions optimized to meet the unique needs of each of these networking domains. Today, Cisco is introducing new integrations, so users have a secure, consistent experience no matter where, when or how they connect. The new integrations allow for end-to-end:
- Network segmentation: The integration of Cisco SD-Access with Cisco SD-WAN and Cisco Application Centric Infrastructure (ACI) makes it easier for IT teams to consistently authorize, onboard and segment users and devices across campus, branch, data center and cloud networks, even when users and applications change. Because of this segmentation, IT is able to safeguard against unauthorized access to sensitive data and critical applications.
- Application experience: Cisco now automatically conveys application requirements between the data center and the WAN, allowing the network to select the best path and prioritize traffic even if applications move or change. This allows IT teams to dynamically elevate application performance across the enterprise and branch.
- Pervasive security: As an industry leader in cybersecurity, Cisco is leveraging its security innovations across all domains. By extending the ability to detect threats in encrypted traffic across public clouds, and by protecting the campus, branch and WAN against threats, Cisco says it’s providing the end-to-end security customers need.
Cisco’s Ecosystem Drives Innovation
As the network becomes increasingly programmable, Cisco’s ecosystem of partners and developers has been crucial to drive innovation. To help organizations keep up with the relentless pace of change, Cisco DevNet, the company’s developer program, has introduced community-backed efforts to make adopting networking technology easy and accessible. This includes machine learning and artificial intelligence developer resources, which include use cases and resources to get started with new applications; the Cisco DevNet Automation Exchange, which contains a curated repository of code for all levels of network automation use cases; and the Cisco DNA Center Platform, which helps networking professionals and software developers alike to build new applications and integrations.
Cisco: How AI and machine learning are going to transform your enterprise network
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Availability, Licensing and Services
- Cisco AI Network Analytics will be a standard part of Cisco DNA Assurance and will be available in the next version of Cisco DNA Center, generally available summer of 2019. Cisco AI Network Analytics capabilities will be included in the Cisco DNA Advantage software licensing tier.
- The multidomain network integrations will be available with the next version of Cisco DNA Center, generally available summer of 2019. These integrations will be included in the Cisco DNA Advantage software licensing tier.
- Cisco Customer Experience for Cisco DNA solutions accelerates deployment of next-gen intent-based networking solutions while reducing risk and disruption. The Cisco Customer Experience portfolio of services delivers expert guidance, best practices and innovative tools to help customers transition with greater ease and confidence. This also allows them to innovate faster, stay competitive, extract more value and realize faster ROI.
Additional Resources
- Executive Blog: Intelligent Next Step for Intent-Based Networking (Scott Harrell)
- Executive Blog: Cisco AI Network Analytics: Making Networks Smarter and Simpler to Manage (Anand Oswald)
- Executive Blog: 3 Ways Intent-Based Networking Fulfills Business Intent with Multidomain Integration (Prashanth Shenoy)
- Cisco DNA Assurance Solution Overview
- Cisco Multidomain Integrations At-a-Glance
- Cisco AI Network Analytics wireless experience use-case video
SOURCE: Cisco Systems
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References: