Wednesday, 10 October 2018

Automated 4G / 5G HetNet Design


I recently heard Iris Barcia, COO of Keima speak after nearly 6 years at Cambridge Wireless CWTEC 2018. The last time I heard her, it was part of CW Small Cells SIG, where I used to be a SIG (special interest group) champion. Over the last 6 years, the network planning needs have changed from planning for coverage to planning for capacity from the beginning. This particular point started a little debate that I will cover in another post, but you can sneak a peek here ­čśë.

Embedded below is the video and presentation. The slides can be downloaded from SlideShare.





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Monday, 8 October 2018

Wi-Fi gets new name


Wi-Fi Alliance has announced that the next generation WiFi technology, 802.11ax, will be known as Wi-Fi 6. This is to probably make it simpler, similar to mobile technology generations. Everyone knows 3G and 4G but how many people know UMTS or LTE. Similarly they are hoping that people will be aware of Wi-Fi 4, 5 & 6. They haven't bothered to name anything below Wi-Fi 4.


Looking at this picture from R&S above, you can see that according to Wi-Fi Alliance naming convention:

Wi-Fi 1: 802.11a (1999)
Wi-Fi 2: 802.11b (1999)
Wi-Fi 3: 802.11g (2003)
Wi-Fi 4: 802.11n (2009)
Wi-Fi 5: 802.11ac (2014)
Wi-Fi 6: 802.11ax (2019)

Anyway, I am not going in any technical details in this post but look for the really good links on this topic below.

To learn more about the naming of next-gen Wi-Fi, check this link.

Further reading:

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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Friday, 28 September 2018

Multi-technology :The future of IoT geolocation

In the big world of IoT, location tracking  is the  next  frontier!. Location tracking for humans is already an integral part of our lives especially for navigation. Traditional technologies enabling this are  not only expensive, they  have technical boundaries preventing scaling. For IoT geolocation to become a true reality, it is inevitable it has to be  extremely accurate, extremely low cost, and extremely low touch. 

Where is the market?


Research and Markets predict revenues from Geo IoT will reach $49 billion by 2021.

Just as location determination has become an essential element of personal communications, so shall presence detection and location-aware technologies be key to the long-term success of the Internet of Things (IoT). Geo IoT will positively impact many industry verticals. – Research and Market report about “Geo IoT Technologies, Services, and Applications Market Outlook: Positioning, Proximity, Location Data and Analytics 2016 – 2021.”

Connecting IoT objects is already a large market growing exponentially with the mix of unlicensed Low-Power Wide Area Network (LPWAN) technologies such as LoRaWAN, and combined more recent introduction of Cellular IoT technologies such as NB-IoT and LTE-M. Adding Geolocation to this introduces a whole range of new applications not possible before. Some of these applications are:
  1. Asset Management
  2. Fleet Management
  3. Anti-theft scooter/bike rental
  4. Logistics/parcel bags tracking
  5. Worker safety for Oil and Gas
  6. Elderly and Disabled care
  7. Tracking solution for skiers
  8. Pets and Animal tracking

The above applications represent large existing market which can be only be enabled with extremely low cost and low power trackers. This is the reason why LPWAN-enabled geolocation is in fact a separate product category for large existing market.

The challenges involved (Asset tracking as an example case study)


Railway cars, truck trailers, containers: tracking valuable assets on the move is a pain point for most large distributed organizations involved in logistics and supply chain, typically relying on partners such as distributors to correctly register check-in and check-out events. This registration process at specific checkpoints is usually manual, intermittent and subject to human errors.  To tackle this issue, an IoT low power asset tracking system using LPWAN (Low Power Wide Area Network) trackers brings a “timeless” checkpoint solution. Specifically, LoRaWAN™-based trackers, because of their low power, low cost and lightweight infrastructure, provide a first truly reliable tracking solution allowing to reduce downtime during transportation. 

In the logistics sector, many business cases involve additional costs due to inefficient utilization of assets. Transport companies need to invest in freight railway cars, car logistics companies need to invest in truck trailers, and of course there are the standard containers and pallets.

“The profitability of these business cases directly depends on the minimization of asset downtime: every day or hour lost in a warehouse, parking or rail station reduces the number of times the moving asset will generate profit in a year.”

However, measuring this downtime is also a challenge. Traditional solutions involved cellular or satellite trackers, which require significant CAPEX, but perhaps more importantly also ongoing OPEX due to battery replacements and connectivity costs. In some cases, trackers are located in hard to reach areas especially when mounted on railroad cars, or in oil and gas rigs, which makes it very costly to replace batteries especially if there are several hundreds of thousands of trackers deployed in the field. The battery replacement is done by humans and is one of the dominating OPEX factors in overall Total Cost of Ownership ( TCO) of the whole solution. These replacement costs actually made it difficult to justify the mass adoption of conventional geolocation solutions in the logistics sector.


LPWAN trackers: a game changer

LoRaWAN  is LPWAN connectivity standard developed by LoRa Alliance primarily for unlicensed ISM spectrum, to create disruption in both the technology and business models. On the technology front, the main impact is on drastic reduction of power consumption, which reduces battery usage and ultimately also OPEX related to ongoing maintenance. It also creates new opportunities for more dynamic tracking, as communication events are less costly. On the business model side, logistics companies can now trade off between CAPEX and OPEX: most LPWAN systems operate on an unlicensed band, for example the leading LoRaWAN™  technology operates in the 915MHz band in the US, the 868MHz band in Europe and equivalent ISM bands in other parts of the world. This means that logistics companies can invest in their own wireless networks to reduce or eliminate variable connectivity costs.

“The cost of LPWAN radio network gateways has decreased due to higher production volumes and are now affordable even to very small logistic centers, such as a car distributor. “

 Next generation LPWAN trackers


The potential of LPWAN-enabled tracking requires a new generation of hardware. The lower radio frequency power consumption is only a part of a massive effort to decrease overall power consumption of the whole system. This requires developing a multi-technology geolocation tracker platform that can combine GPS, Low-Power GPS, WiFi Sniffing, WiFi fingerprinting and Bluetooth with the goal of reducing power consumption and provide location information opportunistically in variety of scenarios such as (indoor/outdoor, urban/rural, slow/fast moving and so on). 

Another key factor is the usage of LPWAN technologies such as (LoRaWAN, NB-IoT, LTE-M) for transporting geolocation data back to the cloud. This is the key as traditional cellular technologies such as 2G/3G/4G are just too power hungry to meet the target goal of 5-10 year battery lifetime. However, there will be licensed Cellular IoT options based on NB-IoT/LTE-M that will be also be used for some of the applications.

IoT geolocation asset tracking, logistics, rolling stock tracking, containers tracking, trucks tracking, supply chain, internet of things, LoRa

LoRaWAN and Low Power GPS significantly increases battery lifetime

IoT geolocation asset tracking, logistics, rolling stock tracking, containers tracking, trucks tracking, supply chain, internet of things, LoRa

Merging an IoT network solution such as LoRaWAN with  multi-mode geolocation technologies for outdoor and indoor positioning increase by at least a factor of 10 the battery lifetime compared to the standard cellular solution using GSM/AGPS. Source: Actility

The Road Ahead:


The next frontier in IoT geolocation will be two fold. The first will be the multi-technology cloud platform that will combine intelligently Over-The-Top (OTT) geolocation technologies such as GPS, Low-Power GPS, WiFi and Bluetooth with network based TDoA geolocation technologies using LoRaWAN and/or Cellular. This requires close cooperation between public network operators with geolocation service providers.

Webinar: MULTI-TECHNOLOGY IOT GEOLOCATION
The future of IoT geolocation is multi-technology


In order to shed some light on the above mentioned points, we are hosting a webinar that explains where  we will explore the challenges of network-based geolocation and how it can be combined with other geolocation technologies such as GPS, WiFi and Bluetooth. We will explain how multi-technology geolocation differs from traditional cellular+GPS based geolocation, and show how it opens up an entirely new market and product category. We’ll also explore how multi-technology geolocation meets the requirements and use cases for connecting small sensors which are low-cost with very long battery lifetime. A guest speaker from KPN will share selected case studies demonstrating IoT geolocation deployments and discuss real-world experience. The webinar will conclude with outlook for technological evolution in the field, and give an overview of our Location portfolio.

What will you learn from this webinar?
  1. What are the market opportunities and use cases enabled by IoT Geolocation?
  2. What are the benefits of multi-technology geolocation?
  3. What are the benefits of using LPWAN technologies(LoRaWAN, NB-IoT, LTE-M) for connectivity?
  4. How LPWAN-enabled Geolocation will evolve in the future?
  5. How is Actility building multi-technology geolocation platform?

Follow the link below for registration to the webinar,

For any questions, contact the author below,

Monday, 24 September 2018

5G New Radio Standards and other Presentations


A recent Cambridge Wireless event 'Radio technology for 5G – making it work' was an excellent event where all speakers delivered an interesting and insightful presentation. These presentations are all available to view and download for everyone for a limited time here.

I blogged about the base station antennas last week but there are other couple of presentations that stood out for me.


The first was an excellent presentation from Sylvia Lu from u-Blox, also my fellow CW Board Member. Her talk covered variety of topics including IoT, IIoT, LTE-V2X and Cellular positioning, including 5G NR Positioning Trend. The presentation is embedded below and available to download from Slideshare





The other presentation on 5G NR was one from Yinan Qi of Samsung R&D. His presentation looked at variety of topics, mainly Layer 1 including Massive MIMO, Beamforming, Beam Management, Bandwidth Part, Reference Signals, Phase noise, etc. His presentation is embedded below and can be downloaded from SlideShare.



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Friday, 21 September 2018

Base Station Antenna Considerations for 5G

I first mentioned Quintel in this blog three years back for their innovations in 4T8R/8T8R antennas. Since then they have been going strength to strength.


I heard David Barker, CTO of Quintel at Cambridge Wireless event titled "Radio technology for 5G – making it work" talking about the antennas consideration for 5G. There are quite a few important areas in this presentation for consideration. The presentation is embedded below:



Related Posts:

Friday, 14 September 2018

End-to-end Network Slicing in 5G

I recently realised that I have never written a post just on Network slicing. So here is one on the topic. So the first question asked is, why do we even need Network Slicing? Alan Carlton from Interdigital wrote a good article on this topic. Below is what I think is interesting:

Network slicing is a specific form of virtualization that allows multiple logical networks to run on top of a shared physical network infrastructure. The key benefit of the network slicing concept is that it provides an end-to-end virtual network encompassing not just networking but compute and storage functions too. The objective is to allow a physical mobile network operator to partition its network resources to allow for very different users, so-called tenants, to multiplex over a single physical infrastructure. The most commonly cited example in 5G discussions is sharing of a given physical network to simultaneously run Internet of Things (IoT), Mobile Broadband (MBB), and very low-latency (e.g. vehicular communications) applications. These applications obviously have very different transmission characteristics. For example, IoT will typically have a very large number of devices, but each device may have very low throughput. MBB has nearly the opposite properties since it will have a much smaller number of devices, but each one will be transmitting or receiving very high bandwidth content. The intent of network slicing is to be able to partition the physical network at an end-to-end level to allow optimum grouping of traffic, isolation from other tenants, and configuring of resources at a macro level.

Source: ITU presentation, see below

The key differentiator of the network slicing approach is that it provides a holistic end-to-end virtual network for a given tenant. No existing QoS-based solution can offer anything like this. For example, DiffServ, which is the most widely deployed QoS solution, can discriminate VoIP traffic from other types of traffic such as HD video and web browsing. However, DiffServ cannot discriminate and differentially treat the same type of traffic (e.g. VoIP traffic) coming from different tenants.

Also, DiffServ does not have the ability to perform traffic isolation at all. For example, IoT traffic from a health monitoring network (e.g. connecting hospitals and outpatients) typically have strict privacy and security requirements including where the data can be stored and who can access it. This cannot be accomplished by DiffServ as it does not have any features dealing with the compute and storage aspects of the network. All these identified shortfalls of DiffServ will be handled by the features being developed for network slicing.

I came across this presentation by Peter Ashwood-Smith from Huawei Technologies who presented '5G End to-end network slicing Demo' at ITU-T Focus Group IMT-2020 Workshop and Demo Day on 7 December 2016. Its a great presentation, I wish a video of this was available as well. Anyway, the presentation is embedded below and the PPT can be downloaded from here.



The European Telecommunications Standards Institute (ETSI) has established a new Industry Specification Group (ISG) on Zero touch network and Service Management (ZSM) that is working to produce a set of technical specifications on fully automated network and service management with, ideally, zero human intervention. ZSM is targeted for 5G, particularly in network slice deployment. NTT Technical review article on this is available here.

Finally, here is a presentation by Sridhar Bhaskaran of Cellular Insights blog on this topic. Unfortunately, not available for download.

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Tuesday, 11 September 2018

Introduction to Fixed Wireless Access (FWA)


We have just produced a new tutorial on Fixed Wireless Access (FWA). The high level introductory tutorial looks at what is meant by Fixed Wireless Access, which is being touted as one of the initial 5G use cases. This presentation introduces FWA and looks at a practical deployment example.

According to GSA report, "Global Progress to 5G – Trials, Deployments and Launches", July 2018:

One use-case that has gained prominence is the use of 5G to deliver fixed wireless broadband services. We have identified 20 tests so far that have specifically focused on the fixed wireless access (FWA) use-case, which is five more than three months ago.

Embedded below is the video and presentation of the FWA tutorial.



If you found this useful, you would be interested in other tutorials on the 3G4G website here.

Wednesday, 5 September 2018

LiFi can be a valuable tool for densification

LiFi has been popping up in the news recently. I blogged about it (as LED-Fi) 10 years back. While the concept has remained the same, many of the limitations associated with the technology has been overcome. One of the companies driving LiFi is Scottish startup called pureLiFi.


I heard Professor Harald Haas at IEEE Glasgow Summit speak about how many of the limitations of LiFi have been overcome in the last few years (see videos below). This is a welcome news as there is a tremendous amount of Visible Light Spectrum that is available for exploitation.


While many discussions on LiFi revolve round its use as access technology, I think the real potential lies in its use as backhaul for densification.

For 5G, when we are looking at small cells, every few hundred meters, probably on streetlights and lamp posts, there is a requirement for alternative backhaul to fiber. Its difficult to run fiber to each and every lamp post. Traditionally, this was solved by microwave solutions but another option available in 5G is Integrated Access and Backhauling (IAB) or Self-backhauling.


A better alternative could be to use LiFi for this backhauling between lamp posts or streetlights. This can help avoid complications with IAB when multiple nodes are close by and also any complications with the technology until it matures. This approach is of course being trialed but as the picture above shows, rural backhaul is just one option.
LiFi is being studied as part of IEEE 802.11bb group as well as its potential is being considered for 5G.

Here is a vieo playlist explaining LiFi technology in detail.




Further reading:

Monday, 13 August 2018

Telefonica: Big Data, Machine Learning (ML) and Artificial Intelligence (AI) to Connect the Unconnected


Earlier, I wrote a detailed post on how Telefonica was on a mission to connect 100 Million Unconnected with their 'Internet para todos' initiative. This video below is a good advert of what Telefinica is trying to achieve in Latin America


I recently came across a LinkedIn post on how Telef├│nica uses AI / ML to connect the unconnected by Patrick Lopez, VP Networks Innovation @ Telefonica. It was no brainer that this needs to be shared.



In his post, Patrick mentions the following:

To deliver internet in these environments in a sustainable manner, it is necessary to increase efficiency through systematic cost reduction, investment optimization and targeted deployments.

Systematic optimization necessitates continuous measurement of the financial, operational, technological and organizational data sets.

1. Finding the unconnected


The first challenge the team had to tackle was to understand how many unconnected there are and where. The data set was scarce and incomplete, census was old and population had much mobility. In this case, the team used high definition satellite imagery at the scale of the country and used neural network models, coupled with census data as training. Implementing visual machine learning algorithms, the model literally counted each house and each settlement at the scale of the country. The model was then enriched with crossed reference coverage data from regulatory source, as well as Telefonica proprietary data set consisting of geolocalized data sessions and deployment maps. The result is a model with a visual representation, providing a map of the population dispersion, with superimposed coverage polygons, allowing to count and localize the unconnected populations with good accuracy (95% of the population with less than 3% false positive and less than 240 meters deviation in the location of antennas).


2. Optimizing transport



Transport networks are the most expensive part of deploying connectivity to remote areas. Optimizing transport route has a huge impact on the sustainability of a network. This is why the team selected this task as the next challenge to tackle.

The team started with adding road and infrastructure data to the model form public sources, and used graph generation to cluster population settlements. Graph analysis (shortest path, Steiner tree) yielded population density-optimized transport routes.


3. AI to optimize network operations


To connect very remote zones, optimizing operations and minimizing maintenance and upgrade is key to a sustainable operational model. This line of work is probably the most ambitious for the team. When it can take 3 hours by plane and 4 days by boat to reach some locations, being able to make sure you can detect, or better, predict if / when you need to perform maintenance on your infrastructure. Equally important is how your devise your routes so that you are as efficient as possible. In this case, the team built a neural network trained with historical failure analysis and fed with network metrics to provide a model capable of supervising the network health in an automated manner, with prediction of possible failure and optimized maintenance route.

I think that the type of data driven approach to complex problem solving demonstrated in this project is the key to network operators' sustainability in the future. It is not only a rural problem, it is necessary to increase efficiency and optimize deployment and operations to keep decreasing the costs.


Finally, its worth mentioning again that I am helping CW (Cambridge Wireless) organise their annual CW TEC conference on the topic 'The inevitable automation of Next Generation Networks'. There are some good speakers and we will have similar topics covered from different angles, using some other interesting approaches. The fees are very reasonable so please join if you can.

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