Mobile apps have “PING” for breakfast!

Why operators should measure what the customer sees…

Mobile operators are under pressure to offer super-responsive connectivity with low latency: whether it’s for your favourite multi-player game or whether it’s to ensure that zoom call with your boss runs smoothly.

The problem is, there’s quite a gap between the network latency measured with ICMP (via the “ping” command) and the effective latency experienced by an application which never actually uses ICMP. 

ICMP (specified in RFC 792) operates in a layer just above IP, meaning its messages are carried as IP payload.  ICMP is often used to signal errors between hosts, but in addition to the error messages, it defines one of the Internet’s most famous applications: ping. Most TCP/IP implementations support Ping directly in the operating system thereby avoiding the time taken to decapsulate the transport-level segment, identify the correct socket and send the message on to the application process.

These steps are necessary when interacting with applications such as HTTP (the application used to request a web page) or HTTPs (when performing a secured transaction). These applications and others, such as FTP, DNS or SMTP also use either TCP or UDP as a transport layer protocol which ICMP does not.

As wireless low-latency specialists, we make sure that our real-time latency tracking tool addresses these issues by performing different types of measurement campaigns. First, we do actively measure network latency using ICMP (knowing that ICMP may be routed differently than payload traffic). Hence, we also use other protocols like TWAMP and PTPd to get the most accurate results: important when we are measuring in milliseconds!

Second, we aim to measure the latency as the application sees it.  We use an intelligent mix of the HTTP/HTTPS/TCP/UDP protocols to mimic the application’s behaviour. We measure multiple times  per second and perform statistical analysis to generate an aggregated view. We accumulate these results to  perform advanced analysis like anomaly and trend detection – all in near real-time – and send notifications back to the application (and to OSS/NMS systems) if something is wrong.  To complement this, we also perform real-time measurements of the bandwidth and reliability (using packet loss count) in addition to taking snapshots of the path(s) taken by the data. This allows us to perform route cause analyses whenever the latency results are outside accepted boundaries for a prolonged period.

The results of both these campaigns can then be displayed and used for comparison and evaluation.

The diagram below shows a snapshot of LatenceTech’s Compact Dashboard presenting latency results for a mobile gaming application running on the  Encqor 5G (NSA) network using a MmWave RAN site and a Mobile EDGE platform in Montréal Canada in early 2022.

Real-time dashboard showing live low-latency results from MMwave measurements

Here we can see the difference between network level latency (10.6ms) and application level (17.2ms) latency by means of results which were aggregated over a 5- minute period. The 38% difference is significant! Knowing the network latency is great but having factual and continuous knowledge about application-level latency is a prerequisite to deploying a time-critical mobile applications.  

Below is another dashboard example, showing results from a low frequency band  and testing the latency from the device to server residing, this time, on the cloud.

Real-time dashboard showing live low-latency results from low-band measurements

We can see here the impact of the lower frequencies and longer distances to data processing, underlining the important role that MmWave and Edge computing will have in low-latency communications.

Time-sensitive mobile applications will require reliable, sustained low-latency connectivity which is best served by 5G technology.  But 5G alone will not be enough to deliver the service users need.  Tools to perform continuous tracking of  quality of service, and in particular network and application latency levels will be central to providing a reliable, high-performance service. Being able to rely on this quality of service means that new applications can be developed, and these will generate new economic benefits and value to end customers.  With the right network performance, think of all the innovate services that will be developed…

Short list of time critical application requiring ultra-low latency connectivity

When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it… your knowledge is of a meager and unsatisfactory kind.”

–Lord Kelvin

We would like to hear your remarks and comments.

Contact us for more information

The Secrets Hidden in CPRI

Written by Parsa Alamzadeh and Siân Morgan

If you want to start a riot in a room full of radio engineers, start talking about CPRI and watch those conference muffins fly.   

person holding magnifying glass
Photo by Maurício Mascaro on Pexels.com

The Common Public Radio Interface (CPRI) was defined by 3GPP to connect two functions in the cellular radio network: it links the Remote Radio Units (RRU) with the Base Band Units (BBU) over fibre.

CPRI’s first challenge is the extremely strict jitter and latency budgets – it’s a serial interface and round-trip latency must be kept in the range of 100 µs.

The second issue is that it has become a largely proprietary interface, making mixing and matching BBU and RRU vendors a nearly impossible task – leading to complaints about vendor lock-in.

It turns out that the data carried in the CPRI interface can be used to detect and predict network issues, as long as the correct high-performance unsupervised learning algorithms are applied.

The final problem is with 5G. LTE, already requires high bandwidth links:  for example, 10 MHz of spectrum to a 2×2 MIMO antenna requires 1.2 Gbps.   In 5G – and especially in the mmWave range – the CPRI data streams are exploding in size and the network will soon run out of fibre.

Luckily, eCPRI (enhanced) was introduced to address these problems – and it turns out it can also be used to detect and predict network issues, as we discovered when we applied high-performance, unsupervised learning to huge volumes of power measurements extracted from the interface.

All this data came from the Encqor 5G testbed network; a state-of-the-art 5G platform deployed in five cities in Canada using Mid-Band (N78 / 3.5GHz) and experimental mmWave (N261 / 28 GHz) spectrum.

Over 1 Gbps of data was filtered and distributed into 1000 payloads of over 98 000 datapoints – at a rate of 1 payload per milliseconds (ms).

The CPRI data was flying at over one Gbps per second and to be able to predict anomalies in the network we needed to extract, process, and analyze it in under 10 ms.  As a basis for comparison, that’s over 20 times faster than the average person’s reaction time. (Want proof?  check out this little test https://humanbenchmark.com/tests/reactiontime).

The useful power measurements we needed to extract was in the IQ portion of the interface (In-Phase and Quadrature signal samples).  With some bitshifting we were able to filter out and discard over 850 Mbps per second of data and distribute what was left into 1000 payloads of over 98 000 datapoints each – at 1 payload per ms.

The fun part came once the raw data was in the payloads.  It involved converting the datapoints into complex numbers, reshaping the data, and performing a Fourier transform on it to convert it from the time to the frequency domains.  

Fig. 1, preprocessed IQ Data, where the x-axis represents the frequency and y-axis represents the normalized power measurement. This graph was obtained after using the Autoencoder to select the anomaly candidates. Blue dots represent normal data and red dots represent anomaly candidates.

Detecting which points were truly anomalies was a challenge because we did not have any labels or classifications. Hence, we needed to take an unsupervised learning approach that allowed for high performance and parallel processing. We settled on a Deep Neural Network model, using autoencoders on data that had been brought to a lower dimension. By itself, it was capturing too many points, so we then used binning and thresholding to improve the output as shown in the figures below.

Fig. 2, On the left we have the histogram of anomalies, where every 32 sub-frequencies are grouped up together, and a threshold is indicated with a dashed red line. Bins that have a value greater than the threshold value are then considered as anomalous sub-frequencies. On the right hand side we have the original data with bins 

This model allowed us to detect anomalous power measurements and associate them with the specific antenna frequencies that were causing the problems. Because we can process the data so quickly (<10ms), problems that would normally go undetected can be identified and alerts or corrective action can be taken before any customers or industrial applications are affected.

Fig. Favoring reactiveness vs. accuracy in ML-based anomaly detection

Our general approach has been to favour reactiveness over accuracy. This generates warnings immediately as issues occur thereby keeping 5G applications fully secured. Warning (or alerts) can be used to instruct the 5G application to revert into “Safe Mode”. This is especially important in transport (e.g., robotaxis), manufacturing (teleoperation of remote equipment), and other industrial use cases. Furthermore, a network orchestrator could easily subscribe to these warnings, and after receiving a high volume of them, send self-healing commands to the affected 5G RAN sites.

It turns out that both the CPRI and eCPRI interfaces have a wealth of information in there – it’s just a matter of having the high-performance AI-based algorithms to extract and process their hidden secrets.

Shopping for latency?

Why IT’s wish-list just got a little longer

Written by Siân Morgan, link to Bio

When the IT department starts shopping around for a telecommunication services provider, it has a lot of things on its mind.  It’s looking at connectivity requirements: Wi-Fi on the shop floor, mobile phones for the sales team, wired Ethernet in the office. Then come the critical applications it needs to run the business:  soft-phones, on-line collaboration, and network analytics.  Finally, it is balancing quality against price before laying down the cash. What does quality mean to the IT department today?  Coverage.  Throughput.  Reliability. Chances are that low latency is not on the list…yet.

asphalt blur car city
Photo by Pixabay on Pexels.com

Pretty soon, low latency is going to move its way up to one of the important factors influencing telecom purchase decisions – especially for enterprises whose businesses are becoming simpler and more productive thanks to 5G.

The standards are promising…

The 5G standards promised low latency (75% lower than LTE) but that’s just the beginning: 6G is coming up with even more drastic targets (1 microsecond in the radio portion!). When businesses start to revolutionize their operations with wireless connectivity and new services such as Augmented or Virtual Reality (think assisted trouble-shooting and virtual training experiences), latency problems in the network are going to become glaringly obvious – and potentially damaging to the bottom line.

…but what about the reality?

Operators have mostly been deploying a first version of 5G – anchoring the radio to LTE and using the LTE core network (called Non-Standalone, or NSA). But even with these early-technology deployments there is evidence that wireless network latency is creeping down.

Benchmarking has shown that 5G can improve round-trip latency by 15 ms, but depending on the distance to the nearest server, the total latency can be over 100ms.  With cloud gaming applications requiring under half of that for a decent experience, let’s say there’s room for improvement.

In an August 2021 paper, researchers from the Universities of Minnesota and Michigan performed tests of commercial 5G networks and were able to demonstrate that 5G improved round-trip latency by between 6 and 15ms, depending on the radio band.   

Rootmetrics performed other 5G network benchmarking and measured latencies of between 22 to 42 ms in Seoul and 46 to 127 ms latencies in Los Angeles. To put this is in context, a decent cloud gaming experience needs a latency of between 10 and 50ms. Let say there’s still room for improvement! In a bid to help commercial networks reduce their end-to-end latency, new technologies are being deployed and tested in leading edge labs across the world.

mmWave is not available in all countries, but testing has shown that it can reduce latency by between six and eight milliseconds, compared to a low band 5G deployment

In Montreal’s outdoor 5G lab, Encqor, LatenceTech recently performed latency tests with experimental mmWave spectrum. The results from late 2021 demonstrate a sustained average ultra-low latency of 10ms measured from the device to the edge, using ICMP. The application-level latency was also measured using TCP/UDP and HTTP and averaged about 17ms, all while offering over 1Gbps  of throughput as shown below:

The results highlight the great performance of mmWave.  These high frequency bands are above 20 Ghz and have a lower end-to-end delay than the low bands because of wider carriers and shorter transmission time intervals. (For a more in-depth discussion on why, check out this blog).

Not all countries have auctioned off their mmWave spectrum yet, but as the research paper from the Universities of Minnesota and Michigan discovered, commercial deployment of these high frequency bands can make a real difference. They found that mmWave provided a six to eight millisecond reduction compared to a low-band 5G deployment.  Qualcomm estimates that there are over 120 5G mmWave devices, including smartphones, PCs, CPEs and hotspot devices.

Meanwhile, there are other cutting-edge technologies that will soon lower 5G latency even further, fueling both critical business applications and the cloud-gamer experience.

Closer is better

Multi-Access Edge Computing (MEC) involves installing computing resources at distributed geographical locations, closer to where the users need their service. When some of the critical data processing is done near the end-user, network response times become lower. It is still early days and both the technology and the business case for MEC need to be fine-tuned. StarlingX  is a great example of an initiative (from the Open Infrastructure Foundation) designed to solve the technical problems with delivering ultra-low-latency use cases on a virtualized edge infrastructure.

gray double bell clock
Photo by Moose Photos on Pexels.com

Streamlining and optimizing

To be “truly 5G” operators are going to move away from the LTE network and deploy 5G Stand-Alone (SA) Cores.  In fact, T-Mobile and Rogers already have, but since it also requires support in the handset, the impact on end-users has been mitigated so far.  The standalone core will allow for network slicing, or the dedication of network processing to services with similar traffic profiles.  This means that low-latency services can make use of network functions optimized for their strict performance requirements.  During testing, T-Mobile stated that they were able to obtain 40% improvements in latency on their SA core.

“…with cutting-edge AI technologies…enterprises will be able to trust that the network can deliver the performance they need” says Benoit Gendron, LatenceTech’s CEO.

What gets measured gets…. paid

Of course, this latency revolution means more than just gluing together a bunch of technical acronyms. Companies whose critical operations depend on low-latency communications need consistent performance from one end of the pipe to the other and they need it at the application layer. Most importantly they need proof that the service they are paying for is delivering the quality they were promised: the throughput, the up-time, and the end-to-end latency. Measuring and reporting on these performance indicators is going to be an important piece of the solution.  Says Benoit Gendron, CEO of LatenceTech, “with cutting-edge AI technologies we can identify trends in 5G latency which will help operators apply predictive maintenance and resolve issues before they become apparent. Ultimately, being able to measure, predict and report on latency will allow operators to sell it more effectively, and enterprises will be able to trust that the network can deliver the performance they need”.

References

[1]          A. Narayanan et al., “A variegated look at 5G in the wild: performance, power, and QoE implications,” in Proceedings of the 2021 ACM SIGCOMM 2021 Conference, Virtual Event USA, Aug. 2021, pp. 610–625. doi: 10.1145/3452296.3472923.

[2]          “RootMetrics_South_Korea-5G_report-1H-2021-final.pdf.” Accessed: Mar. 14, 2022. [Online]. Available: https://assets.ctfassets.net/ob7bbcsqy5m2/5iBWRRoiEgnEVxVp9WcXMK/59725d8bb80c3240b2d9ea1c382ccbea/RootMetrics_South_Korea-5G_report-1H-2021-final.pdf

[3]          “RootMetrics_Gaming_Report_Final.pdf.” Accessed: Jan. 11, 2022. [Online]. Available: https://downloads.ctfassets.net/ob7bbcsqy5m2/4xIeqsGvxfw4fejLt2ChdV/e07972594acb5f9f86b4cfac322d4cee/RootMetrics_Gaming_Report_Final.pdf

[4]          “Open Source Edge Cloud Computing Architecture – StarlingX.” https://starlingx.io/ (accessed Mar. 14, 2022).

[5]          “T-Mobile Launches World’s First Nationwide Standalone 5G Network,” Aug. 04, 2020. https://www.businesswire.com/news/home/20200804005636/en/T-Mobile-Launches-World%E2%80%99s-First-Nationwide-Standalone-5G-Network (accessed Jan. 11, 2022).

[6]          Qualcomm, Fierce Wireless, “Millimeter wave is the missing piece of the 5G puzzle,” Jan. 25, 2022.

5G environmental and social impacts? – part 2

This is the second & final part of our article on probable 5G environmental and social impacts. Use this link to reach part 1.

Caution: this study is not a scientific paper, it’s a blog post with the intent to share ideas.

Before going into more details on the potential environmental and social impacts of 5G, let’s list some of the more positive impacts that will be generated by 5G cellular technology:

  • Improved connectivity for consumers (more bandwidth, low latency, resilience) allowing new services like 4K streaming, massive multiplayer mobile gaming, almost instantaneous access to internet, immersive AR/VR glasses, etc.
  • Improved connectivity for industries and businesses (top bandwidth, ultra low latency below 15 milliseconds, very high reliability at 99,999%, mobile EDGE computing and overall a much-improved connectivity that can be adjusted for specific needs and for each vertical markets with the help of network slicing and new radio technology). For many industries, seamless connectivity will be a game changer!
  • Improve battery life as 5G will offer energy management features to connected devices and wearables allowing them to go into a kind of “sleep mode” thanks to 5G NR-RedCap (New Radio Reduced Capability Device) and other 5G improvements.
  • Better internet access for all. 5G supports more consumers and devices versus 4G/LTE hence it shall be easier to widely deploy in market with low internet access. 5G will offer seamless connectivity facilitating the development of “smart cities” and probably lower the need for physical travel and commute (hence helping lower global warming).
  • Faster network evolution thanks to virtual and cloud Native approaches used on many components like the core and edge network nodes enabling more flexibility, openness and interoperability between equipment and vendors. In short, we shall see a faster evolution of the 5G network capable to handle new consumer and industrial needs.

Now let’s dig a bit more on the potential environmental impacts of 5G:

As stated in part 1, the study has identified three main domains of environmental impacts which are: waste, resources and ecosystems. The figure below presents some more details on each of them.

Details on probable environmental impact of 5G.

The main environmental challenges linked to the implementation of the 5G network come from the manufacturing of the 5G components linked to the infrastructure and the waste created (if we cannot or can only partially recycle) by replacing older 3G and 4G installations and the need to use new devices. Then, 5G networks may probably lead to an important increase in power consumption due to larger networks supporting more devices. As you may know, many countries still rely on fossil fuel for energy hence larger wireless networks may imply more greenhouse gases and more climate changes. Producing billions of 5G devices will also consume lots of new raw material, such as rare earth elements if we do a poor job in recycling existing devices. Finally, as explained in part 1, 5G may have impacts on the ecosystem of Earth’s living things.

Similarly, here are more details on potential social impacts of 5G:

As stated in part 1, the study has identified three main domains of social impacts which are: Urban (street) Furniture, Health and Security. The figure below provides more details on each of them.

Details on probable social impact of 5G.

The implementation of 5G will require the multiplication of antennas near to each other mainly in dense areas like cities. On one side, the architecture of a city often represents its history, traditions, and socio-economic aspect. On the other hand, street or urban furniture must also be practical and evolve technologically to adapt to the advanced needs of its citizens. The arrival of 5G will allow communities to move closer and enjoy the benefits of “smart cities“. More efficient and faster first aid services (ambulances, firefighters, and police), intelligent infrastructure and autonomous public transport services, seamless access to internet services are opportunities that will potentially be more accessible thanks to 5G.

However, the arrival of new technology, such as new 5G small cells, requires the evolution and adaptation of the design of current cities. The implementation of 5G will be no exception and is currently generating anxiety among many communities. This will be a real challenge for architects, urban designers, and construction companies. Indeed, the architecture of buildings may have to be redesigned to ensure an integrated and seamless installation of new antennas while allowing great indoor and outdoor 5G coverage.

The emergence of 5G will probably require the installation of multiple antennas, closer to humans and thus possibly generating more electromagnetic waves. The population is trying as best they can to find the relevant information to educate themselves, understand and try to reach some understanding of the situation. In a world of caution and with limited information, it is rather easy, for some, to jump to alarming conclusions. Currently, the noted impact on health remains a shared concern today and shows many cases of misinformation. According to the World Health Organization (WHO), “Despite much research, there is no indication for the moment that exposure to low-intensity electromagnetic fields is dangerous for human health”.

In addition to the continuing increase in public anxiety over a sense of uncertainty and a certain powerlessness in the implementation of 5G close to human life, more and more individuals are being diagnosed as having “technophobia”. In a world where technology is constantly evolving and where we adapt quickly, it is more than normal, for some, to feel some level of anxiety. In contrast, diagnosed technophobia is a feeling of fear, anxiety or desire to avoid new technologies (e.g., new vaccines). For example, hypersensitivity disease, or Electromagnetic hypersensitivity (EHS), seems to be on the rise but does not find any consensus in modern medicine. Whether these effects are psychological or physical, no individual should have to isolate themselves from society to feel safe. Thus, it is through better transparency, an increased sense of security, easier, clearer, and more efficient access to real-time 5G network data for medical research that the psychological effects could be researched and eventually elucidated.

Security concerns will probably have a huge impact on the adoption rate of 5G in communities. The fear mainly stems from the vulnerability of systems and the potential theft of personal information or identity. The cause of these uncertainties stems specifically from a fear of being watched and recorded by “Big Brother” often called Snowden effet. In addition, faced with the proliferation of technological links and access by international entities to citizens’ data, many fear that future conflicts will not be fought by arms, but by access to technology and data. Current research is lacking in terms of security, and the assurance that a system is foolproof holds until a breach occurs. In any case, only information, communication and transparency will resolve this issue and its potential impacts. For now, we can only observe that cellular networks, using SIM cards and a multiple of high-end security features in RAN, Core and Transport, seems to offer very strong protection of individual privacy.

On trying to conclude this long post…

There are still several hurdles to overcome before deploying 5G more widely. So far little academic research has been carried out and therefore little data is known on the potential environmental and social impacts of 5G as discussed in this article.

On solutions that LatenceTech can bring…

We can do our part by helping mobile operators collect and analyze various type of data and information from their new 5G network as shown in this representation of our solution:

LatenceTech real-time SAAS an AI solution to monitor 5G quality of service

For mobile operators, first and foremost is to get data to assess the situation. It’s starts by understanding the behaviour of their 5G network during the day/week/seasons by obtaining and aggregating the data and then perform deep analysis to find ways to continuously optimize their network and lower its potential impacts. As stated in part 1, with real-time data and AI, multiple insights can be extracted leading to network optimisation such as lower energy consumption of RAN sites, lower electromagnetic output of unused cells (e.g., urban small cells at night), supporting more devices and apps on the same network equipment, etc. etc.

Second most important thing for mobile operators to lower environmental impacts of 5G, is to make sure they implement strong recycling measures, in partnership with their network and device vendors, to recycle devices and obsolete network equipment.

Finally, with improved data on their 5G network, mobile operators can gradually open up and be more transparent with communities, cities, government and research entities. Data availability will foster more research helping make 5G a great solution for all of us while limiting its environment and social impacts to a minimum.

We hope you enjoyed this study. Do not hesitate to contact us if you have remarks, comments or would like to share your view point with us.

Regards,
LatenceTech team

Special Thanks to the HEC Montréal MBA team (see list below) and Joey EL-Khoury (MBA, PhD), Teaching Assistant at Pôle IDEOS at HEC Montréal Business School.

  • Louay Haouari
  • Clémence Hauduc
  • Ibrahima-Kalil Kaba
  • Laurence Lebel
  • Jacques Mikael Noupeu Nguemecheu

For resources consulted by the MBA team for the study, please refer to part 1.

5G environmental and social impacts? – part 1

In the last two years, we have heard many rumors and fake news about the environmental and social impacts of 5G. Some of them even linked the Covid-19 pandemic with the deployment of 5G networks…

Introduction

Since the main focus of our company is related to 5G technology challenges, we have worked with a group of MBA students at HÉC Montréal Business School to investigate the topic of “How 5G will impacts our society and how and where could we help?“. We would like to share the result of this study in the form of two blog articles. This first part will classify the impacts and the second one will dig further into details and possible solutions.

Caution: this is not a scientific paper. Our intent is simply to share interesting ideas.

The main question we asked is: “Will 5G have impacts on the human life? If yes, in which fields?” The study found that yes, 5G has or will have different effects or impacts similar to any human endeavor and any new technology. 5G will most probably make impacts on human life. These impacts could be positive, negative or mixed. We have decided to focus on the negative ones in this study in order to understand if our approach with real-time 5G network data and AI can be useful to address any of them.

Some 5G impact examples

  • 80% of the actual mobile phones (about 6 billion today…) are not compatible with 5G and will need to be replaced in the near future. How will those phones be recycled?
  • Similarly, a good portion of the deployed 3G/4G/LTE network need to be modernized and the same question can be asked on the level of recycling of that equipment.
  • More consumers, more smartphones & IoT devices and new innovative industrial use cases will also call for more antennas (specially to support high band frequencies), an overall larger mobile network needing more electrical consumption (and probably generating more greenhouse gas emissions where energy is produced with fossil fuels).
  • On the ecosystem, there are new studies, mentioned in a report by Columbia Climate School [9], showing possible impacts on living things like birds (nesting, migration) after a long exposition to radio towers. An older study by Warnke [27] discovered that cellular devices could negatively impact bees. In this study, beehives exposed to cellular waves may have suffered from impaired navigation abilities.

These examples show that there are different types of impacts. In short, they can be classified into two mains categories: social and environmental impacts.

5G Environmental impacts

At it is shown in above examples, environmental impacts are easier to study and to measure and therefore we have seen more research papers and reports being published on the matter (refer to resources section below). Indeed, whether it is the impact on ecosystems (e.g., on living things), on the mass of future waste or the use of resources, these impacts can currently be measured and are more confirmed. These concrete elements are often used as inputs in the deployment plans of 5G networks and are thus better understood and calculated. The main environmental issues linked to the implementation of the 5G network come from manufacturing building blocks of the 5G infrastructure (towers, radios, transport network, virtual core, etc.) and the need to use new devices (smartphones, FWA and industrial modems, IoT devices, etc.). We can subdivide the environmental group into waste impacts, resources impacts and ecosystem impacts. We will provide details on each in part 2 of our article.

5G Social impacts

On the other hand we have social impacts. Social impacts are the effects on people and communities that happens as a result of an action or inaction, an activity, project, program or a policy. They are much more difficult to understand and analyze. The information seems less concrete. There’s a lack of data and opinions diverge enormously. These can be further classified in impacts on urban furniture (street/city design), then impacts on health and finally on security. The impacts on urban furniture can be analyzed only when 5G is well and truly established in dense areas and when the companies carrying out the deployment decide on the type of installation to be deployed in our cities. We shall see many new small cells probably incorporated into the street furniture. Information regarding the impacts on the health of citizens is rather mixed. Moreover, considering the uncertainty of these impacts, this is the subject where we seem to find the most articles using disinformation. The impact on security will depend on legislation and requires a thorough understanding of 5G in order to comprehend its potential future impacts on businesses and consumers. Hence for now, most of these social impacts are speculative and unconfirmed (which does not mean they should not be studied). Again, we will provide details on each impact group in part 2 of our article.

A summary of the potential 5G impacts

The potential 5G impacts could therefore be summarized with the diagrambelow:

Our study carried on in detailing these six main impact areas and listing some existing and potential solutions to lower the negative outcome to an acceptable level for all.

It also revealed that many social impacts may be due to a lack of transparency regarding 5G cellular technology and a lack of real-time data to share facts and hopefully evidences on the real impacts. With increased access to data, there could be new research performed by 5G stakeholders. Internally, we discussed how our real-time AI-based solution could possibly be used to help address impacts on resources and ecosystem.

Here are some examples:

  • Electrical Consumption of 5G sites: Collecting real-time data on the electrical consumption of 5G sites (e.g., continuously collecting power level of devices emitting to the radio tower and vice-versa). This data could then be used as input for advanced AI models aiming to minimize the energy consumption of each site by optimizing the power level of radios and connected devices.
  • Application Optimization on EDGE: Again, collecting real-time data end-to-end latency, bandwidth usage and other application-level characteristics could enable the computing of the best possible location on the network to limit resource consumption. For example, a 5G and AI-based surveillance camera service could be optimized by placing the AI function on EDGE therefore avoiding sending the video feed up to the cloud and limiting expensive on-device compute power.
  • Open 5G Data for Impact Research: Real-time collection of 5G network data could be shared with stakeholders (University Research Groups, government entities, cities/municipalities, citizen groups (NPOs and NGOs), 5G network and device manufacturers, etc.) for improved transparency, to stimulate new 5G research and help address concerns from citizens.
  • Other ideas? Do you have other ideas for us? if so, please share!

LatenceTech is motivated to work with a mobile operator in developing real-time tools to collect data, perform monitoring and help reduce environmental and social impacts of 5G networks. Contact us if you are interested.

In the second part of this article, we will discuss further the solutions and concrete actions which could be applied to lower the environmental and social impact of 5G as well as how we (citizens and society) and the LatenceTech could play a more active role.

Regards,
LatenceTech team

Special Thanks to the HEC Montréal MBA team (see list below) and Joseph EL-Khoury (MBA, PhD), Teaching Assistant at Pôle IDEOS at HEC Montréal Business School.

  • Louay Haouari
  • Clémence Hauduc
  • Ibrahima-Kalil Kaba
  • Laurence Lebel
  • Jacques Mikael Noupeu Nguemecheu

Resources section

List of articles consulted by the MBA team for the study

  1. 5Gradar. 5G security : everything you need to know. Online <https://www.5gradar.com/features/5g-security-5g-networks-contain-security-flaws-from-day-one > consulté le 29 juin 2021  
  2. 5G Canada Conseil. Explicateurs 5G. Online < https://www.5gcc.ca/fr/ressources/  > Consulté le 28 juin 2021uay: 
  3. Ariase. La 5G est-elle dangereuse pour la santé?. Online <https://blog.ariase.com/mobile/dossiers/5g-sante-danger> consulté le 20 juin 2021 
  4. BBC. Does 5G pose health risk?. Online <https://www.bbc.com/news/world-europe-48616174> consulté le 20 juin 2021 
  5. UniversFreeBox. Recyclage des vieux mobile. <https://www.universfreebox.com/article/59290/recyclage-des-vieux-mobiles-un-autre-gros-chantier-pour-orange-free-bouygues-et-sfr> consulté le 20 juin 2021 
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Industrial use cases requiring 5G ultra-low latency

Some industrial use cases / innovative services needing sustained ultra-low latency and high reliability to be fully functioning

While developing our value proposition and our SAAS & AI-based solution, we pondered a lot about which consumer or industrial use cases (or innovative service) are required now or will soon require ultra-low latency (below 15 milliseconds) combine with very high reliability.

New categories of applications require high network reliability and very low latency communication to be able to achieve tasks in network. According to [4], the emerging 5G is designed to support enhanced Mobile Broadband (eMMB), Ultra-Reliable Low Latency Communication (URLLC) and massive Machine Type Communication (mMTC). URLLC communication ensure at the same time a very high network reliability and low latency communication, that claims most of network applications. Using the 5G communication, mobile provider network will offer high performance networks to their customer and will meets applications requirements.

The table below shows some URLLC use cases with their requirements in terms of latency and reliability compiled by our team [1] through academic research [2].

If we zoom in on Factory Automation, this use case often describes the industry 4.0 paradigm which enables interconnection and communication between machines, devices, sensors and people. High reliability and a guaranty of low latency is often required in an industrial manufacturing [1]. The figure below shows the scenario of a factory automation use case where a robotic hand sends latest data on its action and context and in return receives command(s) from remote server control unit on the next actions to perform. This approach provides increased production flexibility compared to a pre-programmed robot that only perform repetitive batch manufacturing tasks. However, for this to result in high production quality, flawless connectivity with sustained low-latency is required.

Maintaining 5G QoS such as sustained ultra-low latency, in order fulfill B2B customer SLA is a puzzle game for the provider. It is not possible anymore to just “throw extra bandwidth” to the problem and hope it will fix itself. This strategy, proven useful in the last 30 years with IP networks, will not be sufficient with ultra-low latency (<15ms) and very high reliability (>99,999%) requirements. For us, the most efficient and effective way to ensure that QoS can be maintained is by using advanced analytics and performing the real time network monitoring.

By advanced analytics, we envision the following features (all in real-time as dealing with low-latency cannot wait):

  • Real-time AI-based anomaly detection of ultra-low latency metric per application or network site
  • Real-time decomposition analysis of ultra-low latency into its sub-components such as propagation delay, transmission delay, jitter, etc. We will soon publish a specific blog post on this topic.
  • Real-time AI-based prediction & trend analysis of end-to-end latency and reliability
  • Real-time comparative analysis between 5G networks, sites, frequency, network protocols, B2B use case, etc.

We have started to implement the above functions and would be happy to perform a live demo of our latest prototype.

Regards,
LatenceTech Team

References:

  1. Njakarison Menja Randriamasinoro, Low Latency communication, Monitoring, Measurement and estimation, LatenceTech WhitePaper, 2020.
  2. X. Jiang, H. Shokri-Ghadikolaei, G. Fodor, E. Modiano, Z. Pang, M. Zorzi, and C. Fischione.
    Low-latency networking: Where latency lurks and how to tame it. Proceedings
    of the IEEE, 107(2):280-306, 2019.

Ultra-Low Latency is the real disruption of 5G networks

Snapshot from our Compact Dashboard monitoring real-time latency and reliability of a 5G network

In a wireless environment, a sustained end-to-end response time under 15ms is a fantastic technological achievement. For us at LatenceTech, such ultra-low latency is the key feature and differentiator of 5G mobile networks. We are confident it will have a greater impact than bandwidth improvements and higher reliability.

But first, let’s have a quick reminder of the key 5G characteristics starting with the “top 4” as shown below:

Top 4 new strengths of 5G cellular technology
  • Enhancing Mobile Broadband with 10-25Gbps + per radio; up to 1Gbps + per user/device
  • Much lower end-to-end latency from ~50 ms in 4G/LTE networks to single digit, as low as 5ms in 5G when combined with EDGE. In many cases, the bandwidth shall be about 10x larger than 4G/LTE.
  • Much higher reliability with the possibility of reaching, for the first time for a wireless network, the famous “5 nines uptime” or 99,999% which correspond to an average of less than 6 minutes downtime per year.
  • EDGE capabilities at the radio site with compute (CPU/GPU), network, storage capacity to be shared amongst applications.

Other very important characteristics of the 5G cellular technology are:

  • Enhanced and more flexible machine to machine communication allowing devices to consume less power to communicate and permitting a higher density of connected IoT devide per cell site.
  • More and new spectrum available such as higher band in the 24-39 GHz range) and improved spectral efficiency
  • Cloud native Core – an optimized core network build using Network Virtual Functions (NFV) enabling dynamic and automated network topology and configuration.
  • Network slicing enabling segregation of virtual networks with their own characteristics.

In our opinion, all the new 5G strengths are important but the ultra-low latency is the real disruptor, especially when combined with high reliability.

Ultra-low latency is opening up new possibilities and will positively impact the value chain of many industries. Ultra-Low latency now allows multiple innovative and new use cases in the industrial field by replacing inadequate and cumbersome legacy networks, many of them reaching their limits or unable to provide appropriate quality of service. 5G’s low-latency permits instant interactions with and between connected objects such as autonomous vehicles, industrial IoT devices or robots.

5G connectivity with sustained low-latency will play a key role in the near future in many industrial services and probably in some consumer services. We consider that it is now critical to properly and continuously measure it, understand its “behavior” in different environments, detect anomalies in near real-time, predict future variations and identify ways to improve it.

This is the core belief behind our solution.

Regards,
LatenceTech team