Showing posts with label Artificial Intelligence. Show all posts
Showing posts with label Artificial Intelligence. Show all posts

Wednesday 29 November 2023

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.

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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Wednesday 4 October 2023

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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Wednesday 10 August 2022

AI/ML Enhancements in 5G-Advanced for Intelligent Network Automation

Artificial Intelligence (AI) and Machine Learning (ML) has been touted to automate the network and simplify the identification and debug of issues that will arise with increasing network complexity. For this reason 3GPP has many different features that are already present in Release-17 but are expected to evolve further in Release-18. 

I have already covered some of this topics in earlier posts. Ericsson's recent whitepaper '5G Advanced: Evolution towards 6G' also has a good summary on this topic. Here is an extract from that:

Intelligent network automation

With increasing complexity in network design, for example, many different deployment and usage options, conventional approaches will not be able to provide swift solutions in many cases. It is well understood that manually reconfiguring cellular communications systems could be inefficient and costly.

Artificial intelligence (AI) and machine learning (ML) have the capability to solve complex and unstructured network problems by using a large amount of data collected from wireless networks. Thus, there has been a lot of attention lately on utilizing AI/ML-based solutions to improve network performance and hence providing avenues for inserting intelligence in network operations.

AI model design, optimization, and life-cycle management rely heavily on data. A wireless network can collect a large amount of data as part of its normal operations. This provides a good base for designing intelligent network solutions. 5G Advanced addresses how to optimize the standardized interfaces for data collection while leaving the automation functionality, for example, training and inference up to the proprietary implementation to support full flexibility in the automation of the network.

AI/ML for RAN enhancements

Three use cases have been identified in the Release 17 study item related to RAN performance enhancement by using AI/ML techniques. Selected use cases from the Release 17 technical report will be taken into the normative phase in the next releases. The selected use cases are: 1) network energy saving; 2) load balancing; and 3) mobility optimization.

The selected use cases can be supported by enhancements to current NR interfaces, targeting performance improvements using AI/ML functionality in the RAN while maintaining the 5G NR architecture. One of the goals is to ensure vendor incentives in terms of innovation and competitiveness by keeping the AI model implementation specific. As shown in Fig.2 (on the top) an intent-based management approach can be adopted for use cases involving RAN-OAM interactions. The intent will be received by the RAN. The RAN will need to understand the intent and trigger certain functionalities as a result.

AI/ML for physical layer enhancements

It is generally expected that AI/ML functionality can be used to improve the radio performance and/or reduced the complexity/overhead of the radio interface. 3GPP TSG RAN has selected three use cases to study the potential air interface performance improvements through AI/ML techniques, such as beam management, channel state information feedback enhancement, and positioning accuracy enhancements for different scenarios. The AI/ML-based methods may provide benefits compared to traditional methods in the radio interface. The challenge will be to define a unified AI/ML framework for the air interface by adequate AI/ML model characterization using various levels of collaboration between gNB and UE.

AI/ML in 5G core

5G Advanced will provide further enhancements of the architecture for analytics and on ML model life-cycle management, for example, to improve correctness of the models. The advancements in the architecture for analytics and data collection serve as a good foundation for AI/ML-based use cases within the different network functions (NFs). Additional use cases will be studied where NFs make use of analytics with the target to support in their decision making, for example, network data analytics functions (NWDAF)- assisted generation of UE policy for network slicing.

If you are interested in studying this topic further, check out 3GPP TR 37.817: Study on enhancement for data collection for NR and ENDC. Download the latest version from here.

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Tuesday 15 February 2022

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.

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.

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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Monday 13 December 2021

5G & AI Powered Smart Hospitals

5G Telehealth has been one of the main driving use cases for upgrading the infrastructure. While some use cases definitely make sense, some others like remote surgery will most likely never happen, at least the way it's depicted today.

At the GSMA Mobile 360 APAC - 5G Industry Community Summit, Michael Fung, Chief Information Officer from CHUK Medical Centre presented a nice talk detailing how they see 5G & AI powered hospitals of the future. The video of his talk is embedded at the bottom of this post.

There have also been some other discussions on 5G & healthcare recently. Here are the links if you want to explore this topic further:

The US FDA recently published a one pager looking at how Service level agreements (SLAs) can enable 5G-enabled medical device use cases by documenting how a medical device communication requirement is met by the unique characteristics of 5G networks and the roles and responsibilities of the stakeholders involved in offering safe and effective 5G-enabled healthcare to patients.

IEEE Access has a detailed paper on this topic by the same authors. Quoting from the abstract:

Service level agreements (SLAs) can enable 5G-enabled medical device use cases by documenting how a medical device communication requirements are met by the unique characteristics of 5G networks and the roles and responsibilities of the stakeholders involved in offering safe and effective 5G-enabled healthcare to patients. However, there are gaps in this space that should be addressed to facilitate the efficient implementation of 5G technology in healthcare. Current literature is scarce regarding SLAs for 5G and is absent regarding SLAs for 5G-enabled medical devices. This paper aims to bridge these gaps by identifying key challenges, providing insight, and describing open research questions related to SLAs in 5G and specifically 5G-healthcare systems. This is helpful to network service providers, users, and regulatory authorities in developing, managing, monitoring, and evaluating SLAs in 5G-enabled medical systems.

Here is the video from GSMA 5G Industry Community Summit Part 2:

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Tuesday 16 November 2021

5G-Advanced Flagship Features

I am starting to get a feeling that people may be becoming overwhelmed with all the new 5G features and standards update. That is why this presentation by Mikael Höök, Director Radio Research at Ericsson, at Brooklyn 6G Summit (B6GS) caught my attention. 

The talk discusses the network infrastructure progress made in the previous two years to better illustrate the advanced 5G timeline to discovering 6G requirements. At the end of the talk, there was a quick summary of the four flagship features that are shown in the picture above. The talk is embedded below, courtesy of IEEE TV

In addition to this talk, October 2021 issue of Ericsson Technology Review covers the topic "5G evolution toward 5G advanced: An overview of 3GPP releases 17 and 18". You can get the PDF here.

I have covered the basics of these flagship features in the following posts:

Please feel free to add your thoughts as comments below.

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Tuesday 24 August 2021

3GPP's 5G-Advanced Technology Evolution from a Network Perspective Whitepaper


China Mobile, along with a bunch of other organizations including China Unicom, China Telecom, CAICT, Huawei, Nokia, Ericsson, etc., produced a white paper on what technology evolutions will we see as part of 5G-Advanced. This comes not so long after the 3GPP 5G-Advanced Workshop which a blogged about here.

The abstract of the whitepaper says:

The commercialization of 5G networks is accelerating globally. From the perspective of industry development drivers, 5G communications are considered the key to personal consumption experience upgrades and digital industrial transformation. Major economies around the world require 5G to be an essential part of long-term industrial development. 5G will enter thousands of industries in terms of business, and technically, 5G needs to integrate DOICT (DT - Data Technology, OT - Operational Technology, IT - Information Technology and CT - Communication Technology) and other technologies further. Therefore, this white paper proposes that continuous research on the follow-up evolution of 5G networks—5G-Advanced is required, and full consideration of architecture evolution and function enhancement is needed.

This white paper first analyzes the network evolution architecture of 5G-Advanced and expounds on the technical development direction of 5G-Advanced from the three characteristics of Artificial Intelligence, Convergence, and Enablement. Artificial Intelligence represents network AI, including full use of machine learning, digital twins, recognition and intention network, which can enhance the capabilities of network's intelligent operation and maintenance. Convergence includes 5G and industry network convergence, home network convergence and space-air-ground network convergence, in order to realize the integration development. Enablement provides for the enhancement of 5G interactive communication and deterministic communication capabilities. It enhances existing technologies such as network slicing and positioning to better help the digital transformation of the industry.

The paper can be downloaded from China Mobile's website here or from Huawei's website here. A video of the paper launch is embedded below:

Nokia's Antti Toskala wrote a blog piece providing the first real glimpse of 5G-Advanced, here.

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Monday 24 May 2021

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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Saturday 1 August 2020

Artificial Intelligence (AI) / Machine Learning (ML) in 5G Challenge by ITU


ITU is conducting a global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks". If you don't know the difference between AI & ML, this picture from the old blog post may help.


The ITU website says:

Artificial Intelligence (AI) will be the dominant technology of the future and will impact every corner of society. In particular, AI / ML (machine learning) will shape how communication networks, a lifeline of our society, will be run. Many companies in the ICT sector are exploring how to make best use of AI/ML. ITU has been at the forefront of this endeavour exploring how to best apply AI/ML in future networks including 5G networks. The time is therefore right to bring together the technical community and stakeholders to brainstorm, innovate and solve relevant problems in 5G using AI/ML. Building on its standards work, ITU is conducting a global ITU AI/ML 5G Challenge on the theme “How to apply ITU's ML architecture in 5G networks". 

Participants will be able to solve real world problems, based on standardized technologies developed for ML in 5G networks. Teams will be required to enable, create, train and deploy ML models (such that participants will acquire hands-on experience in AI/ML in areas relevant to 5G). Participation is open to ITU Member States, Sector Members, Associates and Aca​demic Institutions and to any individual from a country that is a member of ITU. ​

There are also some cash prizes, etc. There are various topics with presentation slides & recordings freely available. 

I found the slides from ITU AI/ML in 5G Challenge —”Machine Learning for Wireless LANs + Japan Challenge Introduction” (link) very interesting. There are many other topics including AR / VR / XR, etc, as well.

Have a look at the ITU website here.


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Sunday 15 March 2020

How Cellular IoT and AI Can Help to Overcome Extreme Poverty in a Climate-resilient Way

The Democratic Republic ofthe Congo (DRC) is the second largest country in Africa and it has a significant potential for agricultural development as the country has more land (235 million hectares) than Kenya, Malawi, Tanzania, and Zambia, combined, of which only 3.4% is cultivated.

Despite this, around 13 millions of Congolese live in extreme food insecurity, among them 5 millions acutely malnourished children. Current assessments show the trend is increasing.

In the southern provinces formerly known as "Katanga" the needs in maize for human consumption sum up to 700,000 tons per year, while the local production barely amounts to 120,000 tons per year. This means the provinces have to resort to importing food from neighboring countries, which represents a huge burden on the region's economy.

Another aspect of the problem is that 80% of the local production is made by women farmers, and the biggest challenge they face is the lack of daily agronomic monitoring and guidance. There is only a limited amount of agriculture experts in the region and without assistance, the farmersaverage output is at best one ton per hectare. However, field trials have proven that by using smart farming technology they can easily produce up to 6 tons per hectare year over year with the right sustainable approach and support. Artificial intelligence (AI), the Internet of Things (IoT) and big data analytics underpinned by mobile connectivity can even do more. They bring significant potential for capturing carbon, optimizing water, pesticide and fertilizer usage, and reducing soil erosion. Thus, African women can not only provide the solution to the local food gap/insecurity but also become the primary protectors of their environment.

The basic technical concept is not new. Back in 2016 Ooredoo Myanmar launched Site Pyo, a mobile agriculture information service for smallholder farmers. At its core Site Pyo is a weather forecast app that was enhanced with weather-dependent advice for ten crops, from seed selection to harvesting and storage. In addition the app displays the actual market prices for these crops. GSMA as a co-funder of the project celebrates Site Pyo as a big success, but it seems to be limited to Myanmar. Why?

„A lot of customization needs to be done to adapt the application functionality for a particular region“, says Dieu-Donné Okalas Ossami, CEO of „e-tumba“, a French Start-up specialized in smart farming solutions for Sub-Sahara Africa. His company partners with iTK, a spin-off from CIRAD, the French Institute for tropical agronomy. The iTK crop-specific predictive models are based on years of agronomic data, but have originally been designed for big farmers. To meet the demands of women in Katanga requires more granular data for both, input and output.

As in case of Site Pyo weather predictions are important, but in addition there are data feeds from sensors on the spot. Weather stations measure constantly temperature and rainfall while sensors in the soil report its saturation with water, nitrogen and potassium.

„A typical real-time advice that our software provides is to delay the harvest for some additional days to maximize the yield“, explains Okalas Ossami. „However, even for two neighboring fields the particular advices are often different.“ 

Also the communication channels need to be taylored. Many women farmers are illiterate. For them the advice must be translated into the local language they speak and transmitted to their phones as a voice message. Those who can read and write will receive the notifications through short message service.

The mobile connectivity that links all elements of the system is realized by the mobile network operators present in the region.


Infographic: The Technical Environment Behind the Project
„Actually NB-IoT would fit to our use case“, says Okalas Ossami, „but it is not available. And there is neither LoRa nor SigFox.“ Hence, the sensors are using data connections of 3G and 4G radio access technology. In case of network outage or missing coverage a local field technician must collect the sensor data manually and transfer it to the data center through alternative channels.

It is the same field technician who installs the sensors. The woman farmers receive a basic training to understand how the system works, but they do not need to care about technical components - except keeping their mobile phones charged.

Here comes another important aspect into the game: How can the women trust this technical environment?

In case of Site Pyo the operator Ooredoo observed a quickly increasing user community measured by the number of app downloads. However, there was no indication to which extend the Myanmar farmers really used the app. The e-tumba solution addresses this gap by partnering with the non-government organization „Anzafrika“.

Anzafrika is present in the villages where the people live. One of its major targets is to overcome the extreme poverty by developing the regional economy. A key factor for this is that the smallholder farmers do not just see the market prices for their crops, but get real access to large, stable and long-term markets where these prices are paid. Anzafrika is brokering contracts between the woman farmers and large multinational corporations committed to the Economics of Mutuality, growing human, social and natural capital. The business model behind this concept was outlined by Bruno Roche and Jay Jakub in their book „Completing Capitalism:Heal Business to Heal the World“. Instead of focusing on greenhouse gas emissions (output) they insist that climate-resilient business models must measure the input needed for manufacturing goods. As an example: For one hot cup of coffee the greenhouse gas emissions are extremely low, but 3.4 liters of water are needed (most for packaging, processing and drinking) and 12 gram of top soil will be eroded. These are (among others) the expenses paid by the planet that are not taken into account by a carbon tax.

Coffee plantations are monocultures with all the known disadvantages resulting form this kind of farming. In the past the Congolese women farmers have grown maize as a monoculture. Now, with advice from Anzafrika and e-tumba they transitioned from an „all-maize“ sustenance crop to a semi-industrial „maize-sorghum“ production. This helps to minimize the top soil erosion and thus, to remunerate the natural capital involved in the process.  

Regarding the human and social capital Anzafrika monitors how the overall situation in the villages  is improving. The focus is on progress in well-beeing, satisfaction and health not just for the women farmers, but for their entire communities.

In 2019 smart farming technology have been tested and deployed with a group of 150 women in the province of Lualaba. Now, in 2020, their number is expected to rise to 500 and after 6 years the stunning target of 100,000 participants shall be met. A look at the download numbers of Site Pyo (206,000 in the course of one year) shows that these numbers are not over-optimistic.

The partnership between Anzafrika, e-tumba and iTK is now considered as a best international practice, as indicated by Patrick Gilabert, UNIDO Representative to the European Union in Brussels. It fully aligns with the development of new comprehensive strategies for Africa that aim at creating a partnership of equals and mutual interest through agriculture, trade and investment partnerships.

UNIDO, as the UN convener for the implementation of the Industrial Decade for Development of Africa” (IDDA 3) is always ready to join forces with innovative partners.

Wednesday 12 February 2020

AI your Slice to 5G Perfection


Back in November, The Enhanced Mobile Broadband Group in CW (Cambridge Wireless) held an event on 'Is automation essential in 5G?'. There were some thought provoking presentations and discussions but the one that stood out for me was by Dan Warren from Samsung


The slides are embedded below but I want to highlight these points:
  • Some Network Functions will be per slice whereas others will be multi-slice, the split may not be the same for every slice
  • Two slices that have the same 'per slice vs multi-slice' functional split may be different network hardware topologies
  • Enterprise customers will likely want a 'service' contract that has to be manifested as multiple slices of different types. 
  • Physical infrastructure is common to all slices
The last point is very important as people forget that there is a physical infrastructure that will generally be common across all slices.

Again, when you apply Artificial Intelligence (AI) to optimize the network functions, does it do it individually first and then end-to-end and if this is applied across all slices, each of which may have a different functionality, requirement, etc. How would it work in practice?




As Dan says in his tweet, "It is hard to implement AI to optimise a point solution without potentially degrading the things around it.  Constantly being pushed to a bigger picture view => more data => more complexity"

Let me know what you think.

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Saturday 29 June 2019

Presentations from ETSI Security Week 2019 (#ETSISecurityWeek)


ETSI held their annual Security Week Seminar 17-21 June at their HQ in Sophia Antipolis, France. All the presentations are available here. Here are some I think the audience of this blog will like:


Looks like all presentations were not shared but the ones shared have lots of useful information.


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Wednesday 6 February 2019

AI in 5G – the why and how

IET recently held the 6th Annual 5G conference bringing together key players in the 5G world. You can watch the videos for that event here (not all have been uploaded at the time of writing this post).

We reached out to Dr. Yue Wang to share her presentation with us and she has kindly done so. The presentation and video are embedded below.






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Wednesday 23 January 2019

AI and Analytics Based Network Designing & Planning

Recently I blogged about how Deutsche Telekom is using AI for variety of things. The most interesting being (from this blog point of view), fiber-optic roll-out. According to their press release (shortened for easy reading):

"The shortest route to the customer is not always the most economical. By using artificial intelligence in the planning phase we can speed up our fiber-optic roll-out. This enables us to offer our customers broadband lines faster and, above all, more efficiently," says Walter Goldenits, head of Technology at Telekom Deutschland. It is often more economical to lay a few extra feet of cable. That is what the new software-based technology evaluates using digitally-collected environmental data. Where would cobblestones have to be dug up and laid again? Where is there a risk of damaging tree roots?

The effort and thus costs involved in laying cable depend on the existing structure. First, civil engineers open the ground and lay the conduits and fiber-optic cables. Then they have to restore the surface to its previous condition. Of course, the process takes longer with large paving stones than with dirt roads.

"Such huge amounts of data are both a blessing and a curse," says Prof. Dr. Alexander Reiterer, who heads the project at the Fraunhofer IPM. "We need as many details as possible. At the same time, the whole endeavour is only efficient if you can avoid laboriously combing through the data to find the information you need. For the planning process to be efficient the evaluation of these enormous amounts of data must be automated." Fraunhofer IPM has developed software that automatically recognizes, localizes and classifies relevant objects in the measurement data.

The neural network used for this recognizes a total of approximately 30 different categories through deep learning algorithms. This includes trees, street lights, asphalt and cobblestones. Right down to the smallest detail: Do the pavements feature large pavement slabs or small cobblestones? Are the trees deciduous or coniferous? The trees' root structure also has a decisive impact on civil engineering decisions.

Once the data has been collected, a specially-trained artificial intelligence is used to make all vehicles and individuals unidentifiable. The automated preparation phase then follows in a number of stages. The existing infrastructure is assessed to determine the optimal route. A Deutsche Telekom planner then double-checks and approves it.


In the recent TIP Summit 2018, Facebook talked about ‘Building Better Networks with Analytics’ and showed off their analytics platform. Vincent Gonguet, Product Manager, Connectivity Analytics, Facebook talked about how Facebook is using a three-pronged approach of accelerating fiber deployment, expanding 4G coverage and planning 5G networks. The video from the summit as follows:

TIP Summit 2018 Day 1 Presentation - Building Better Networks with Analytics from Telecom Infra Project on Vimeo.

Some of the points highlighted in the video:
  • Educating people to connect requires three main focus areas, Access, Affordability and Awareness – One of the main focus areas of TIP is access. 
  • 4G coverage went from 20% to 80% of world population in the last 5 years. The coverage growth is plateauing because the last 20% is becoming more and more uneconomical to connect.
  • Demand is outpacing supply is many parts of the world (indicating that networks has to be designed for capacity, not just coverage)
  • 19% of 4G traffic can’t support high quality videos today at about 1.5 Mbps
  • Facebook has a nice aggregated map of percentage of Facebook traffic across the world that is experiencing very low speeds, less than 0.5 Mbps
  • Talk looks at three approaches in which Facebook works with TIP members to accelerate fiber deployment, expand 4G coverage and plan 5G networks.
  • A joint fiber deployment project with Airtel and BCS in Uganda was announced at MWC 2018
  • 700 km of fiber deployment was planned to serve over 3 million people (Uganda’s population is roughly 43 million)
  • The real challenge was not just collecting data about roads, infrastructure, etc. New cities would emerge over the period of months with tens of thousands of people 
  • In such situations it would be difficult for human planners to go through all the roads and select the most economical route. Also, different human planners do thing in different ways and hence there is no consistency. In addition, its very hard to iterate. 
  • To make deployments simpler and easier, it was decided to first provide coverage to people who need less km of fiber. The savings from finding optimal path for these people can go in connecting more people.
  • It is also important for the fiber networks to have redundancy but it’s difficult to do this at scale
  • An example and simulation of how fiber networks are created is available in the video  from 07:45 – 11:00.
  • Another example is that of prioritizing 4G deployments based on user experience, current network availability and presence of 4G capable devices in partnership with XL Axiata is available in the video from 11:00 – 14:13. Over 1000 sites were deployed and more than 2 million people experienced significant improvement in their speeds and the quality of videos. 
  • The final example is planning of 5G mmWave networks. This was done in partnership with Deutsche Telekom, trying to bring high speeds to 25,000 apartment homes in a sq. km in the center of Berlin. The goal was to achieve over 1Gbps connection using a mixture of fiber and wireless. The video looks at the simulation of Lidar data where the wireless infrastructure can be deployed. Relevant part is from 14:13 – 20:25.
Finally, you may remember my blog post on Automated 4G / 5G Hetnet Design by Keima. Some of the work they do overlaps with both examples above. I reached out to Iris Barcia to see if they have any comments on the two different approaches above. Below is her response:

“It is very encouraging that DT and Facebook are seeing the benefits of data and automation for design. I think that is the only way we’re going to be able to plan modern communication networks. We approach it from the RAN planning perspective: 8 years ago our clients could already reduce cost by automatically selecting locations with good RF performance and close to fibre nodes, alternatively locations close to existing fibre routes or from particular providers. Now the range of variables that we are capable of computing is vast and it includes aspects such as accessibility rules, available spectrum, regulations, etc. This could be easily extended to account for capability/cost of deploying fibre per type of road. 

But also, we believe in the benefit of a holistic business strategy, and over the years our algorithms have evolved to prioritise cost and consumers more precisely. For example, based on the deployment needs we can identify areas where it would be beneficial to deploy fibre: the study presented at CWTEC showed a 5G Fixed Wireless analysis per address, allowing fibre deployments to be prioritised for those addresses characterised by poor RF connectivity.”

There is no doubt in my mind that more and more of these kinds of tools that relies on Analytics and Artificial Intelligence (AI) will be required to design and plan the networks. By this I don’t just mean 5G and other future networks but also the existing 2G, 3G & 4G networks and Hetnets. We will have to wait and see what’s next.


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Friday 26 October 2018

The Yin and the Yang of AI & Blockchain


Today I read about HTC's Exodus 1, new Blockchain smartphone that only people with crypto-currency can buy. SCMP described in very simple terms what this phone is for:

Both HTC’s Exodus and Sirin’s Finney smartphones feature a built-in digital wallet application that will enable users to securely store and use cryptocurrencies, such as bitcoin and ethereum, in daily transactions.

Those smartphones are designed to replace the special memory sticks, which employ complex usernames and passwords to access, that cryptocurrency investors use to store their digital money. These investors typically store most of their cryptocurrencies in such hardware, which are kept offline as a means of security.

“There are things that a phone manufacturer can do with a chip that nobody else can,” said Chen. “We want to be safer than the existing hardware wallets … HTC has a track record of making trusted hardware.”

The company’s Exodus smartphone, for example, can serve as a “node”, which can connect to certain blockchain networks to enable trading of tokens between users. It will also be able to act as a so-called mining rig for users to earn new tokens tied to the Exodus blockchain.

“At some point, we’ll do our own utility token,” said Chen, adding that there was no timetable for such a token release.

HTC’s foray into blockchain, the distributed ledger technology behind cryptocurrencies like bitcoin, represents a strategy to keep the company relevant in smartphones, which is a market dominated by Samsung Electronics and Apple, followed by Huawei Technologies, Xiaomi and other major Chinese brands.

Anyway, the blockchain smartphone reminds me of the joke above (via marketoonist). The second technology mentioned in this joke is AI or Artificial Intelligence.

I heard HP Enterprise talk about AI recently and this picture above is a nice simple way to show how Deep Learning (DL), Artificial Neural Networks (ANN), Machine Learning (ML) and Artificial Intelligence (AI) are related.

I see AI and blockchain often referred to together. This does not necessarily mean that they are related. iDate allowed me to share a recent presentation (embedded below) that refers to AI & blockchain as Yin and Yang. Anyway, I am happy to learn more so if you have any thoughts please feel free to share.



Further Reading:


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Tuesday 2 October 2018

Benefits and Challenges of Applying Device-Level AI to 5G networks


I was part of Cambridge Wireless CWTEC 2018 organising committee where our event 'The inevitable automation of Next Generation Networks' covered variety of topics with AI, 5G, devices, network planning, etc. The presentations are available freely for a limited period here.

One of the thought provoking presentations was by Yue Wang from Samsung R&D. The presentation is embedded below and can be downloaded from Slideshare.



This presentation also brought out some interesting thoughts and discussions:

  • While the device-level AI and network-level AI would generally work cooperatively, there is a risk that some vendor may play the system to make their devices perform better than the competitors. Something similar to the signaling storm generated by SCRI (see here).
  • If the device-level and network-level AI works constructively, an operator may be able to claim that their network can provide a better battery life for a device. For example iPhone XYZ has 25% better battery life on our network rather than competitors network.
  • If the device-level and network-level AI works destructively for any reason then the network can become unstable and the other users may experience issues. 

I guess all these enhancements will start slowly and there will be lots of learning in the first few years before we have a stable, mutually beneficial solution.

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