Anomaly detection: using AI to identify, prioritise and resolve network issues

Anomaly detection: efficiently identifying and resolving issues across mobile networks is vital for the success of any CSP or MNO. With 5G network deployment ramping up, and beyond that, work towards autonomous networks, the use of AI to handle anomalies is vital. In this article, Amr Ashraf, RAN and Software Solution Architect and Trainer, describes how CSPs can more efficiently identify and resolve the real issues hidden amongst all the noise.

Identifying the real issues hiding in all the noise

Data volumes are only going in one direction – there are substantial increases in data volume as more people connect online, with more devices, more solutions move to the cloud, continued changes in customer behaviour and movement of people and information across society, and more and more daily interactions move online. This increased volume of data also provides far greater noise for cyber threats to hide within. Whilst networks continue to perform well, and meet demand, efficient use of resources will become ever more important.

New tools and approaches are needed to be able to identify and resolve issues in all the noise.

Data from Statista, image credit World Economic Forum.

Anomaly detection and resolution: the role of AI

AI and ML provide the vital tools to handle this ever-growing volume of data. Soon – if not already – there will simply be too much data to analyse everything – but we can use AI to identify the unusual issues and outliers, and dig deeper into these anomalies.


The AI Hierarchy of Needs, Monica Rogati 2017
“How do we use AI and machine learning to get better at what we do?”. M.Rogati placed anomaly detection as a vital transformational step in providing a solid foundation for data before being effective with AI and ML.

Digis Squared & anomaly detection

Digis Squared’s INOS AI tool is a vendor-agnostic, multi-network-technology solution delivering automated assessment, testing and optimisation of networks, across all technologies. The data collected by INOS is analysed in the cloud-based AI engine, and it is here where anomalies are detected, assessed and actioned.

INOS: Three data collection methods

“INOS collects data in the field by one of three methods – a traditional “suitcase” format for drive testing, or a highly mobile backpack which can be used in narrow streets, or walking through shopping malls for example,” explains Amr Ashraf, RAN and Software Solution Architect and Trainer. “And this paper focusses on the third method, the static INOS active probe, and how the data it collects is analysed and actioned.”

A background image of a green landscape is overlaid with icons and photos of equipment. INOS

INOS active probe

“The active probe is a static box which is typically deployed inside a building – maybe a corporate HQ, a high-profile area within an airport, or a new business facility. Perhaps the location is selected because the CSP wants to proactively support a new VIP client, gather KPI data, or improve SLAs. Once deployed, the probe continuously monitors the networks, and data is streamed to the INOS platform in the cloud, where it is analysed by the INOS AI engine.”

“In the first step of the analysis, the data collected by the INOS active probe is used to identify QoE – the quality of experience – problems experienced by mobile devices in the probe. INOS will assess the data and carry out root-cause investigations to identify the fault that led to the problem. This is accomplished by gathering various performance measurements from several layers – the network, hardware, link, and operating system – which are then aggregated and delivered to INOS cloud.”

“To put it more specifically, the probe regularly initiates a test scenario while recording network, hardware, link, and OS measurements. Given that QoE problems can come from a variety of locations along the path, measuring the performance of each layer enables not only the detection of QoE problems, but also the determination of the problem’s root cause. A database on the INOS cloud receives aggregated metrics as soon as they are made available.”

Why use active probes?

“One of the best ways to understand how the end-user perceives the performance from beginning to end is using probes. They offer real-time and historical end-to-end call tracing, KPIs that continuously track network health and customer experience, and proactive alarming (QoE).”

“The two basic methods—probe measurements—that can improve performance and enrich end-to-end analysis (from the user terminal to the core network) are active and passive probe measurements, Because they give detailed information that enables service operators to assess the service quality across various transport technologies, probes play a significant role in the ever-increasing complexity of modern telecom networks.”

“By defining some parameters, the INOS probe uses AI models to detect anomalies in any field KPIs. For example, parameters such as deviation parentage and time windows can be used to calculate the deviation value. The example below for RSRP in LTE takes this approach, and identifies a deviation of 20%.”

INOS report of RSRP in LTE, identifying an anomaly with 20% deviation

“Additionally, INOS is able to assess data from other channels, including WhatsApp, Telegram, Twitter and email, and can assess behaviour of those apps for network anomalies too.”

Telegram Notification for a customer with certain Anomaly Detection

“To achieve the goal of end-to-end quality metrics, these probe measurements should be connected with numerous node-to-node performance data as well as customer data,” explains Amr.

“An integral view from the customer, network, service, or terminal perspective is provided by Digis Squared’s in-house developed INOS and RAI tools. Together these two AI-tools can proactively manage the network by continuously monitoring end-to-end KPIs, created from various perspectives in the network. They can immediately identify any deteriorating trends and anomalies, for example, dropped-call ratio and set-up times.”

“All Digis Squared’s tools are vendor agnostic – networks are such a complex mix of solutions, that our tools simply have to be able to work with and analyse data from all vendors. And, of course, they also handle data from all network technologies, legacy 2G platforms through to 5G, they’re designed for all of this.”

Prioritization: counter-intuitive approaches are sometimes best

The costs and impacts associated with low and medium-severity anomalies may be far greater than the total cost of high-severity issues – smaller issues are often harder to detect, and take longer to identify and implement a fix, so their compounded cost can be higher. AI can help ensure counter-intuitive approaches to assessing priority can be handled without bias.

“A proactive approach can save money in addition to ensuring high levels of customer satisfaction by reducing the number of trouble tickets and so optimizing resource allocation. The pre-defined INOS reports’ ability to show service quality makes root-cause investigation possible across all network layers.”

“Today, the Digis Squared AI tools are able to continuously receive data from active probes in the network, identify anomalies and negative trends. They are also able to identify root cause, and propose recommended solutions to fix the issue. Working with our clients, in some installations we enable those recommended fixes to be automatically implemented, ensuring that frequently occurring minor issues are identified and resolved automatically. Of course, all issues are included in reporting. This approach ensures that staff do not need to intervene in the mundane, predictable issues, and can instead focus on assessing the recommendations the system makes for more complex issues.”

Anomaly handling and autonomous networks

The use of AI in anomaly detection and, critically, resolution, has great value for legacy technologies, and even greater value for new technologies and transformations. It’s a vital step in network function virtualization (NFV), cloud-native computing (CNC) and software-defined networking (SDN) technologies. And provides important preparation for CSPs as they ready their organizations for operations based on autonomous networks.

“The Digis Squared INOS active probes are a vital tool in providing high-quality data on background network behaviour and performance,” shared Amr. “Using this, our AI tools are able to continuously assess the streamed data and identify anomalies, assess their root cause, and then propose and implement recommended actions. AI solutions like this will soon be the only way in which CSPs can efficiently identify and resolve the real issues hidden amongst all the vast quantities of noise.”

Find out more about INOS

INOS can be implemented as a public or private cloud, or on-premise solution, and is also available as a “Radio Testing as-a-service” model. Its extensive AI analysis and remote OTA capabilities ensure speedy and accurate assessment of all aspects of network testing: SSV, in-building and drive testing, network optimization and competitor benchmarking, across all vendors, network capabilities and technologies, including 5G, private networks and OpenRAN.

INOS is built with compute resources powered by Intel® Xeon® Scalable Processors. Digis Squared is a Partner within the Intel Network Builders ecosystem program, and a member of the Intel Partner Alliance.

In conversation with Amr Ashraf, Digis Squared’s RAN and Software Solution Architect and Trainer.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email hello@DigisSquared.com

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Digis Squared ◦ Enabling smarter networks.

AI enhancement of capacity management in mobile networks

The optimisation of capacity management in mobile networks is vital: too little capacity constraints revenue opportunities and impacts customer experience, but idle capacity risks high opex and under-performing investment in assets. Capacity management has always used mathematical modelling techniques to attempt to find the sweet spot, and optimise opportunities and costs. In the past, such predictions were based on historical data, but now AI enhancement of capacity management changes that. The deployment of network virtualization, 5G and network slicing requires the use of cognitive planning; it is vital that capacity planning models are able to assess a step-change in the volume of data points in real-time or near-real-time.

RAN Automation Architect and Data Scientist at Digis Squared, Obeid Allah Ali, describes how AI, automation and advanced analytics are being deployed to gain even greater network capacity planning efficiencies.

What exactly is machine learning, and why is it important?

Machine Learning (ML) is an application of artificial intelligence (AI) that enables computer programs to learn and improve over time because of their interactions with data.

It automates analytics by making predictions using algorithms that learn repeatedly.

Its easy self-learning technique, rather than rule-based programming, has found widespread use in a variety of contexts.

So, whether it’s making life easier with navigation advice based on predicted traffic behaviour, assessing large amounts of medical data to identify new patterns and links, or warning you about market volatility so you can adjust financial decisions, AI and ML technology has permeated many aspects of our daily lives.

The power of prediction machines

In simplified terms, prediction is the process of filling in the missing information. It takes the information you have, often called ‘data,’ and uses it to generate information you don’t have. Most machine learning algorithms are mathematical models that predict outcomes.

How will machine learning impact businesses?

There are two major ways that forecasts will alter the way businesses operate.

  1. At low levels, a prediction machine can relieve humans of predictive activities, resulting in cost savings, and for example removing emotional bias.
  2. A prediction machine could become so accurate and dependable that it alters how a company operates.

How big is the growth in mobile connectivity?

Above: from GSMA “The State of Mobile Internet Connectivity Report 2021” [3], their most recent report

Some further statistics on the growth in mobile data, from the same GSMA report [3],

  • global data per user reaching more than 6 GB per month – double the data usage for 2018
  • 94% of the world’s population covered by mobile broadband network
  • By the end of 2020, 51% of the world’s population – just over 4 billion people – were using mobile internet, an increase of 225 million since the end of 2019

And from [4] GSMA Mobile Economy 2021 report,

  • By the end of 2025, 5G will account for just over a fifth of total mobile connections.

Capacity and performance of mobile networks

The rapid growth of mobile traffic places enormous strain on mobile networks’ ability to provide the necessary capacity and performance.

To meet demand, communications services providers (CSPs), mobile network operators and their suppliers need a range of options, including more spectrum, new technology, small cells, and traffic offloading to alternate access networks.

To meet commercial business objectives, mobile network operators are under pressure to maximize the utilization of existing resources while avoiding capacity bottlenecks that reduce revenues and negatively influence end-user experience.

Additionally, network operators have to assess risk, contractual SLAs (especially in the context of MVNOS who utilise their network, and corporate contracts), the total cost of ownership, and the impact on customer experience, perception and brand.

Radio Access Network costs are estimated to be 20% of the opex cost of running a network [1]. And the impact of opex on network quality correlates strongly with increased ARPU and reduced churn; when network quality is highest, service providers benefit from a higher average ARPU (+31 %) and lower average churn (-27%) [2].

Finding the perfect balance of capacity, quality, efficiency and cost – not too much, not too little – is complex and dynamic.

Capacity forecasting for mobile networks

The Digis Squared team have developed machine learning algorithms and decoders that can, based on network activity, decode how User Traffic Profiles are changing. With the deployment of 5G and network slicing techniques, modelling network usage patterns and customer behaviour and predicting future demand becomes immediately far more complex – the only way to successfully model this will be with AI.

Detecting a problem

We detect anomalies in cells in the existing network, plus highly utilized cells, using machine learning and a design approach algorithm based on several reported KPIs. We use this information to distinguish what requires immediate attention from what should be monitored for proactive action. Using multivariable modeling techniques, that is, assessing multiple KPIs across each cell, enables us to have a highly nuanced model, optimising all available capacity.

Forecasting

Operators must be able to estimate the required traffic capacity for their mobile networks in this competitive climate to invest in extensions when they are truly needed, and deploy the most cost-effective solution, while maximizing investment and maintaining good network quality.
In this phase of the development of the model, we will discover future troublesome cells to guide our approach and actions using predictive models.

AI enhancement of capacity management: what’s next?

Today, we use an open-loop control system to apply our AI methods. However, as predictive model accuracy improves, we anticipate transitioning to a fully automated Self-Organized Network (SON) – enabling closed-loop network management with self-planning, self-configuration, self-optimization, and self-healing – system in the near future.

In conversation with Obeid Allah Ali, RAN Automation Architect and Data Scientist at Digis Squared.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

References

AI-native network slicing for 5G networks

AI-native network slicing for 5G networks. Successful 5G deployments rely on the use and integration of many other new technologies. In this blog post, Tameem Sheble RAN data scientist at Digis Squared, and AI for 5G researcher, takes us on a deep dive through the close integration of 5G with AI, and how network slicing is vital to deliver the 5G vision.

The view from 2015: the 5G vision

New telecom technologies take years to be discussed, agreed and defined in standards. Previous wireless technologies have been designed and architected for one major use case: enabling mobile broadband.

During the development phase of 5G standards, by means of reaching consensus and aligning expectations, the International Telecoms Union (ITU) defined their framework and overall objectives of the future out to 2020 and beyond, in a vision document. As part of this workaround 5G architecture, they considered three distinctive, cutting-edge service verticals – enhanced mobile broadband, massive machine-type communications and ultra-reliable and low-latency communications – and their usage scenarios and opportunities for communications service providers (CSPs).

Above: The original vision for 5G. Diagram from “IMT Vision for 2020 and beyond”, published 2015 [1]

Network Slicing (NS) is one of the 5G key enablers. It’s a technique that CSPs can use to satisfy the different needs and demands of the 5G heterogeneous verticals, as illustrated below, using the same physical network infrastructure.

AI-native network slicing for 5G networks

Network slicing enables the virtual and independent logical separation of physical networks. It’s a technique used to unlock the value of 5G networks, by opening the possibilities for customer-centric services based on the demand on the network while managing cost and complexity. Consequently, vendors and standardization communities consider 5G NS a key paradigm for 5G and beyond mobile network generations.

Although the 5G NS process brings flexibility, it also increases the complexity of network management. The introduction of AI into the 5G architecture, AI-native, is motivated by the vast amount of unexploited data and the inherent complexity and diversity that requires AI to be deployed as an integral part of the overall system design. Although the rising temptation is to rely on AI as a pillar for managing 5G network complexity, in practical terms, AI and 5G are indivisible. They tend to converge from an application perspective, and they become two halves of a whole. AI’s value relies on 5G; for example; critical data-driven decisions need to be communicated with ultra-low latency and high reliability.

AI as a potential solution to network slicing

Let’s turn now to addressing AI in a nutshell for the management of the complex sliced 5G network, a complexity that relates to decision-making towards efficient, dynamic management of resources in real-time. CSPs need to leverage the use of the vast volume of data flowing through the network in a proactive way, by forecasting and exploiting the future system behaviour.

The management lifecycle of a network slice consists of four main phases,

  1. Preparation
  2. Instantiation
  3. Operation
  4. Decommissioning.

Many researchers have proposed AI solutions that underline the first 3 phases, as the decommissioning phase doesn’t involve management decisions. Admission control and network resources orchestration are some of the key slice management functions that need to make slice-level decisions to meet their requirements, while simultaneously maximizing the overall system performance.

Looking at this in more detail, this is achieved by controlling a massive number of parameters as a result of uncovering complex multivariate relationships that are related to each other in time, geolocation, etc. Proposing an AI solution must be done case by case depending on problem formulation and framing, algorithmic requirements, the scarcity and type of data and the operational time dynamics.

AI for network slices admission control (Phase 1)

Admission control – during the slice preparation phase – is a very critical decision-making control mechanism, it ensures that the requirements of the admitted slices are satisfied. During this control mechanism, a trade-off between resources sharing and KPIs fulfilment needs to be tackled. The decision on how many network slices run simultaneously, and how to share the network infrastructure between those slices, has an impact on the revenues of the CSPs.

The trade-off is further complicated by variables that alter over time, which makes the optimization of revenue based on admitted slices a difficult task. This is where Deep Reinforcement Learning (DRL) approach comes into the picture.

In a nutshell, the DRL algorithm has to learn the arrival pattern of network slices and make, for example, revenue-maximizing decisions based on the current system utilization and the anticipated long-term revenue evolution. Once a network slice is requested, and based on the system current utilization, two separate neural networks are in charge of scoring the two actions (i.e., accepting or rejecting the request), where each score represents the revenue associated with each action. Based on the difference in scores, the action corresponding to the higher revenue is selected; the algorithm interacts with the system and evaluates the accuracy of the forecasted revenue through a loss function. This value is then feedback to the corresponding neural network to perform weight update, so that the algorithm starts converging to a global maximum and performs better in the subsequent request iterations.

AI for network resources orchestration and re-orchestration (Phase 2 and 3)

After the successful admission, slices must be allocated sufficient resources in such a way that the available capacity is used in the most efficient way that minimizes the operational expenses (OPEX). The trade-off here is between under-provisioning that leads to Service Level Agreement
(SLA) violation, and over-dimensioning thus wasting resources.

CSPs need to be proactive by forecasting, at a slice level, the future capacity needed, based on previous traffic demand, and consequently timely reallocate resources when and where needed. This is where the Convolutional Neural Network (CNN) architecture comes into the picture for time-series forecasting.

Legacy state-of-the-art traffic time-series forecasting models focus on forecasting the future demand that minimizes some symmetric loss (e.g., mean absolute error), that treats both under- and over-prediction equally. But this type of legacy approach doesn’t consider the risk of under-provisioning and SLA violation – it is useless for 5G deployment!

Researchers argue that a practical AI-native resource orchestration solution has to forecast the minimum provisioned capacity that prevents SLA violation. The balance between over-dimensioning and under-provisioning is therefore controlled by the CSPs, by introducing a customized loss function that overcomes the drawbacks of the “vanilla symmetric losses”.

The AI literature proposes the use of 3-dimensional CNN architecture over the recurrent neural network (RNN) architecture – which is considered the state-of-the-art algorithm for forecasting time-series data –  in order to exploit and uncover spatial and temporal traffic relationships.

The future for AI in 5G and beyond

Whilst general AI limitations are now well known – trustworthiness, generalization and interpretability – exploiting AI to assess and manage complex decisions is vital for the smooth operation of 5G networks. And as network technologies continue to grow in complexity and capability, AI will clearly be necessary as a pillar technology for future-generation zero-touch mobile networks

In conversation with Tameem Sheble RAN data scientist at Digis Squared, and AI for 5G researcher.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

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Digis Squared, independent telecoms expertise.

References

Cognitive solutions for telecom operations

Digis Squared Chief Technology Officer, Abdelrahman Fady, shares insights into Digis Squared’s approach to cognitive solutions and AI.

“Today, there are three distinct and significant challenges that Mobile Operators face,

  • 5G and new technologies are adding extra dimensions of complexity to the networks
  • Mature markets, ever-increasing customer expectations, and higher standards to reach for customer satisfaction
  • Revenues shrinking and increased budget pressures.”

“For sure, you can find an opportunity in every challenge,” shares Abdelrahman, “and here the opportunities we found within this complexity, pressure and maturity is the existence of massive amounts of data and very strong computational power. So let’s see how we can tackle these challenges using the created opportunities. This article digs into some answers!”

What is cognitive technology?

“Yes, it is software-based technology built on the 3Vs – volume, variety, velocity. Characteristics of big data lakes generated from networks, deployed over the strong computational power provided to us by new technologies, in combination with ML advanced modelling that fits in with SMEs unique logic.”

“Cognitive technologies refer to a multiple set of techniques, tools and platforms that enable the implementation of intelligent agents.”

Intelligent agent tasks can be considered as,

  1. Sense
  2. Think: Previous knowledge + known data
  3. Act

Intelligent agent thinking stakes: how cognitive agents work with ML & MR

“Cognitive computing represents self-learning systems that utilize machine learning, ML, and machine reasoning, MR, models to mimic the way brain works,” explains Abdelrahman.

The characteristics of cognitive computing include that they are,

  • Adaptive: cognitive software mimics the ability of human logic and brains to learn from and adapt to its surroundings
  • Interactive: cognitive solutions interact with all elements in the system (processors, devices, clouds and users)
  • Iterative: cognitive software always remembers previous interactions in a process
  • Stateful: cognitive solutions return suitable information
  • Contextual: cognitive software is capable of identifying contextual elements such as syntax, time, location, users, profiles etc

Cognitive benefits

“Cognitive solutions nowadays are in the circle of focus of all mobile operators. Applying them in technical operations as well as commercial operations are likely to bring a lot of benefits to operators,” says  Abdelrahman.

  • CAPEX rationalization: “Decisions about where to add new sites, layers, technologies, where and when to undertake network expansion should be taken based on many factors. ROI is part of this decision-making process, along with many other technical and commercial aspects, including the network growth and consumer behaviour changes, commercial positioning in the market, the general economic climate. Cognitive software like Smart Planning (Smart Capex) software ensure that proper investment and budgeting decisions are based on the complex interaction of such a diverse range of factors.”
  • Operational efficiency “Optimizing operations activities and resources are vital, and the hottest active topic these days due to the impact of COVID-19 on the overall telecom ecosystem. Automation has been used for ten years or more in the telecoms sector, however continuing to reach high efficiency targets needs more than just automation. It needs a combination of automation, AI, Big Data Analytics and human brain emulations, and this can only be achieved by deploying cognitive solution in operations.”
  • Superior network and customer experience “Smart Optimization for newly deployed sites, sectors and technologies are very important to enhance customer experience, and must work very swiftly to have impact. Additionally, network KPI enhancement plus handling customers pain-points before complaints arise or impact network churn KPIs are vital. It’s very important that all of these elements should be automated and continuously updated. To achieve that you must adopt smart cognitive solutions for network optimization.”
  • Fast time to market “Analysing consumer behaviour and response to what is offered by operators, as well as the impact of broader economic changes, help in the design optimisation of operators’ products and services. Because of the complexity of these inputs, the only way to assess the users and market needs is adopting cognitive technology in commercial analysis of client behaviour and product usage.”
  • Speedy mean time to resolve “Currently, mobile networks are very mature and very complex. In general, competitors are focused on customer centricity. Actually, this customer centricity couldn’t be in place without very accurate and decisive solutions that help us to identify and resolve network and customers’ technical and commercial issues and pain-points very quickly. This is one for the early targets achieved by the application of cognitive solutions and software.”

Cognitive technology limitations

“Having described and enthused about the benefits, lets provide some balance, and consider the limitations,” says  Abdelrahman.

  • Handling un-expected risks and abrupt changes are the most serious challenges that face cognitive solutions. Due to slow response times to this type of change, cognitive solutions risk not being very accurate and speedy. Continuous development and training for adopted models in cognitive solutions is the only way to mitigate these challenges.
  • Data bias: as with any AI system, and mathematical model, bias is always dangerous. To mitigate this requires diversified data sources.
  • Decision accuracy is another challenge here which arises from the risk and possibility of mimicking the human brains of inexperienced team members. This risk may be mitigated easily by adopting a check-points technique during the solution design phase.
  • Explain-ability & repeatability: as with any AI system, it is vital that developers are able to explain how the cognitive system arrived at the answer it did. Decision tree mapping is a vital part of this process, as is the ability to explain and demonstrate why variability or repeatability does / not occur.
  • Data protection, data privacy and security are very important legal, regulatory and ethical factors, especially when you are dealing in your solution with personal data usage. Governments, regulators and authorities are putting a lot of effort into protecting consumer data, and customers are increasingly aware and vocal on the issue. One of the techniques which is often implemented to mitigate this risk is to mask any personal info with code like mapping.

Digis Squared & cognitive solutions

“Digis Squared has a set of cognitive solutions, and extensive experience in this domain with multiple operators. Our solutions are already deployed and in action helping telecom operators and communications service providers in different regions to enhance their operational limits. If this is something you would like to know more about, I am always happy to discuss more with clients, get in touch,” shared  Abdelrahman.

Digis Squared cognitive operations

Our live cognitive solutions deployed today include,

  • Drone site audit
  • Smart CAPEX
  • Smart optimization.

“In an upcoming blog I’ll share more about a future vision for cognitive operations, and moving towards zero-touch network operations, full automation for FCAPS model.”

In conversation with Abdelrahman Fady, Digis Squared Chief Technology Officer.

If you or your team would like to discover more about our capabilities, please get in touch: use this link or email sales@DigisSquared.com .

Digis Squared, independent telecoms expertise.

Image credits

  • Digis Squared social media and blog banner image: NASA

Digis-One ◦ Unified Fault Management for multi-vendor, multi-technology networks

Why do telecom networks need a Unified Fault Management solution?

Digis-One is a Unified Fault Management (UFM) solution, delivering multi-vendor and multi-technology carrier-grade capability for telecom SOC (Service Operations Centres) and NOC (Network Operations Centres). But why is it needed?

Mobile networks today are a patchwork of systems, with solutions from multiple-vendors, becoming more complex as layers of IoT and 5G capability are added – the result of decades of commercial decisions, technology availability, and technical strategies. As the telecom sector is inherently defined by international standards, this mix-and-match approach should deliver a coherent system, but there are some critical elements which struggle to work together.

Network Operations Centre (NOC) engineers need to undertake fault and performance management activities, in addition to routine, repetitive tasks: fault detection, events enrichment, impact analysis and trouble ticketing creation – and all of this is undertaken manually.

A smart Unified Fault Management solution (UFM) is crucial to unify and automate all fault management activities.

Finding and resolving sub-optimal performance issues is often more complex than fault resolution. Improving performance in one system may create a knock-on consequence to another, which may not appear immediately, and may slowly creep before it triggers an alarm. When NOC teams have to log in to multiple, discrete NMS to view each component, it is impossible for them to assess the full picture.

“AI, 5G and automation are the key technologies driving digital transformation”

Source: EY (1)

Digis-One – UFM

A multi-vendor and multi-technology carrier-grade solution, used by Service or Network Operations Centers (SOC/NOC), focusing on fault management unification across all network and IT nodes.

The Digis Squared team have combined their knowledge of network failure occurrence mechanisms and resolution procedures, and our AI and automation skills, to develop Digis-One – a single consistent interface into all the Network and IT nodes in your network, with intelligent troubleshooting and optimisation.

“Digis-One intelligently assesses the data from all the Network and IT nodes it’s connected to. Using automated rules, root cause is identified against a library of recommended actions and troubleshooting steps, thereby overcoming native compatibility issues.”

Yasser ElSabrouty, Co-Founder, and Director of Sales & Business Development ME & Africa

See the full picture

Digis-One delivers,

  • One platform: rich, intuitive web interface, with a fully customisable single view.
  • Technology: all system components are Cloud Native Computing Foundation certified with a mesh of micro-services components.
  • Agility: setup can be changed on the fly, no loss of service with its static predefined load balancing rules.
  • Automation: fully-fledged library of “Generic / Vendor-specific” correlation and automation rules, network alarms, plus their recommended actions, and all troubleshooting steps pre-loaded. RPA ensures faults are consistently identified and swiftly resolved, minimising failure recovery time. Additionally, stable services outside standard working hours can be achieved by automating network operations and monitoring.
  • Cost efficiency: efficient system architecture and a single interface = simplified operations, administration and maintenance.  Efficiency also increases customer satisfaction due to prompt network failure response, which in turn helps retain customers and improve brand image.

Additional Digis-One benefits include,

  • Rapid integration, and elastic scalability: as components continue to be added to your network, the additional NMS they come with can be easily integrated into Digis-One.
  • Alarm grouping and pivoting
  • AutoPilot: the first step towards zero-touch operation
  • Commercially deployed: now.

Ensuring your network works

As mobile networks are enhanced with IoT, edge-computing and 5G, the ever-increasing complexity of fault management, demands that AI and automation are used to swiftly help your teams identify and resolve faults, and optimise network performance. Now more than ever, ensure your network works.

Digis Squared, independent telecoms expertise.

This blog post is also available as a stand-alone white paper.

To discuss how Digis-One can help your business, please use this link or email sales@DigisSquared.com to arrange a convenient time for an informal conversation.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Sources,

Abbreviations

  • CSP: Communications Service Providers
  • IoT: Internet of Things
  • NOC: Network Operations Centre
  • NMS: Network Management System
  • SOC: Service Operations Centre
  • UFM: Unified Fault Management

Image credits: Nathan Bang (patchwork), Jackson David (telecoms tower)

LTE 600MHz ◦ Network benchmarking & optimisation with INOS

The background: why is the 600MHz band being used for LTE?

Mobile data usage continues to grow throughout the world, and the pandemic has massively impacted forecasts and expectations, causing telecom operators and CSPs to bring forward their deployment decisions.

“The limited amount of spectrum available below 1 GHz will ultimately run out of capacity. This puts mobile broadband at risk in emerging markets, rural areas and inside buildings. Therefore, long-term
planning is key to enable countries to offer great mobile services for everyone.”

GSMA, October 2019

So what can be done to identify more spectrum for mobile broadband? Countries working on the digital TV switchover can consider including 600MHz for mobile broadband. North America is leading the way – USA auctions were completed in April 2017, Canada in April 2019, and Mexico in 2020!

GSMA [1]

600MHz LTE benefits

We asked Amr Ashraf, RAN and Software Solution Architect and Trainer at Digis Squared, to give us his insights into LTE 600MHz band.

“Over the last couple of years we’ve been starting to hear about the deployment of very low band for mobile communication.  Now, we have commercial networks working on one of the most important low bands, 600MHz.”

Halberd Bastion [2]: Band 71 600 MHz LTE coverage prediction

“600MHz is likely to need about 0.8 cells to cover the same area as a 700MHz cell. So 600MHz will be excellent for providing coverage over a given area. And, as an added bonus, the 600MHz signal is likely to penetrate most buildings – great for indoor coverage.”

“Ideally, an operator will have a selection of low band (600MHz, 700MHz and 90MHz) spectrum to provide wide coverage and in-building coverage together with higher bands (1.8GHz, 2.1/2.6GHz, etc.) to provide capacity at specific locations with small cells, including in-building distributed antenna systems. The trick is in deploying the bands efficiently and economically to meet the market needs.”

… and issues

“On other hand, I don’t think that the 600MHz band will be that useful for 5G implementation, as we can’t use all the new transmission techniques with a low band like Massive MIMO.”

“In order for MIMO to work effectively, the antennas need to be spatially separated such that they are uncorrelated. And, the lower the band, the larger the antenna and the required separation between them. At the 600MHz band, it would be incredibly difficult to physically fit more than two uncorrelated antennas inside handsets, given their current sizing. Our calculations therefore assume that 5G and 4G in the 600MHz band will only make use of 2×2 MIMO.”

“There will be some problems to be faced in the reallocation of systems currently utilising this band, like DTV, and also some wireless devices like MICs. However, 600MHz LTE will be one of the most important bands during the next 10 years for full 4G coverage, particularly for rural areas.”

What problems are encountered deploying the 600MHZ band?

With any new network deployment, testing and optimisation are vital to ensure network performance, and also address any inadvertent impacts on existing networks. Whilst a limited number of activities can be undertaken centrally, drive testing, and in-building testing are critical to understanding the real customer experience in the field.

Developed in-house by Digis Squared, INOS is an intelligent, automated testing, benchmarking and analysis platform for network operators and service providers, delivering drive testing (DT), in-building solution (IBS) capability, and much more, whilst decreasing both the time taken to complete the work and opex cost.

Using cloud-controlled mobiles mounted in cars or taken around buildings, INOS collects and uploads data to the cloud, and eliminates the need for a laptop or engineers in the car, or out and about inside buildings. INOS can receive updated test scripts in the field to instantly re-analyse live network configuration changes, avoiding expensive follow-up field trips. It minimises the sometimes chaotic nature of drive tests, and ensures your staff can work alone at Covid-19 safe distances.

One of the key issues with any drive testing tool, such as INOS, is that there are very few mobile phones available for drive testing in this 600MHz LTE frequency, and where there are, drive test solutions don’t use them.

The good news: uniquely, INOS supports LTE 600MHz band

The Digis Squared team have extensively tested a large range of mobile phones, and the best-performing mobile in the LTE 600MHz band that we have found so far is the Google Pixel 5.

After detailed testing in specific locations where 600MHz LTE is in the live network, our teams have found a significant enhancement in capability using this device in our testing portfolio.

Digis Squared’s INOS tool assessing LTE 600MHz band: Coverage (RSRP)
Digis Squared’s INOS tool assessing LTE 600MHz band: Quality (SINR)
Digis Squared’s INOS tool assessing LTE 600MHz band: MIMO performance (spatial rank)
Digis Squared’s INOS tool assessing LTE 600MHz band: Internet speed (DL PDCP throughput)

LTE 600MHz optimisation with INOS

We’ve already started drive testing this capability with live networks. If you or your team would like to discover more about LTE 600MHz optimisation, or how INOS can help you in your network deployment or benchmarking, please get in touch: use this link or email sales@DigisSquared.com to arrange an informal chat.

In conversation with Amr Ashraf, Digis Squared 5G & LTE RAN & Software Solution Architect, and Trainer.

Digis Squared, independent telecoms expertise.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Sources

  1. GSMA
  2. Halberd Bastion
  3. For more information about INOS, click here.

Abbreviations

  • CSP: communications service provider
  • DT: drive testing
  • DTV: digital TV
  • IBS: in-building solution
  • INOS: Intelligent Network Optimisation Solution, a Digis Squared tool
  • MICs: wireless microphones
  • MIMO: multiple-input and multiple-output. A method for multiplying the capacity of a radio link using multiple transmission and receiving antennas to exploit multipath propagation.

Image credit: Gurwinder Singh

5G ◦ Why is it so complex to deploy?

In conversation with Digis Squared CTO AbdulRahman Fady, we explore some of the complexities and opportunities.

5G is a hot topic, with new handsets coming to market, and networks expanding globally. Abdulrahman Fady, CTO at Digis Squared, has worked in the technology sector for more than 20 years, and in this blog post he shares his views on how the deployment of this latest generation of telecom technologies will bring new problems to solve, and new opportunities to grasp.

So please share with us Abdulrahman, why is 5G so complex to deploy?

“By 2025, 5G networks are likely to cover one-third of the world’s population.”

Source: GSMA [1]

5G rollout, complexity and issues

“Everyone is talking about 5G and how important it is for the ICT industry. Deploying 5G will change and benefit our societies, however, to deliver the real benefits of 5G a lot of challenges need to be addressed, starting with infrastructure and security, and expanding across all spheres into people culture and anthropology, and far from the expertise and competencies of the average ICT engineer.”

“I don’t think this will be an easy journey! It will be a really tough but exciting journey, where people have to learn how to implement adequate automation and AI techniques to make use of the data 5G delivers – it simply won’t be possible to assess the volume of data without AI. Technically, I believe there will be a strong competition between legacy RAN vendors and O-RAN vendors as they compete for market leadership – this will deliver benefits for operators and CSPs, and drive innovation and identification of new efficiencies.”

5G & IoT: “many of its technical capabilities have been designed with Industry 4.0 applications in mind:

  • Ultra-Reliable Low Latency Communication (URLLC) is vital for real-time communications between machines
  • Greater bandwidth and support for higher device density enables use cases that generate more data traffic and host a greater number of devices or sensors
  • Network slicing allows virtual separation of networks, enhancing security and reliability
  • Mobile Edge Computing allows critical network functionality to be retained at the edge, further enhancing resilience and operational continuity”
Source: GSMA [2]

“In the field of IIoT and C-IoT, I think there will be a lot of new ideas generated as nerds and ICT people get their hands on 5G tech. As these different approaches come together – the nerds exploring what the new tech and new devices can do, and ICT staff searching for solutions to address specific issues – they will bounce ideas of each other, and there will be real energy and dynamism as they race to bring new innovations to market.”

“5G will be a huge opportunity for the big cloud providers like Amazon, Google and Microsoft to change the way MNOs work, delivering massive real-time analysis capability, new opportunities for collaborative international teams to work together, system resilience and efficiency.”

“However, it’s not all good news! I think 5G security will be a showstopper in many countries, limiting the deployment of all its functions in some places. These issues will in turn bring great opportunities for third parties and SIs to play a far bigger role in the ICT ecosystem.”

The biggest issue

“But do you want to know the biggest issue I see? The number one challenge limiting 5G spreading swiftly worldwide, and blocking the real benefits of 5G deployments, is the complexity of handsets, the UEs and terminals.”

MIMO (Multiple Input Multiple Output) “MIMO has been used in wireless communications for a long time now — it’s common for both mobile devices and networks to have multiple antennas to enhance connectivity and offer better speeds and user experiences. MIMO algorithms come into play to control how data maps into antennas and where to focus energy in space. Both network and mobile devices need to have tight coordination among each other to make MIMO work.”

Source: Qualcomm [3]

5G uses Massive MIMO and expands on the existing MIMO systems, by adding a much higher number of antennas on the base station – this helps focus energy, which brings massive improvements in throughput and efficiency. As well as all the additional antennas, both the network and mobile devices implement more complex designs to coordinate MIMO operations.

  • 5G utilises different parts of the radio spectrum to deliver performance, capacity and coverage
  • mmWave spectrum: best for dense urban areas and crowded indoor environments. Doesn’t travel very far, so an array of antennas is used for beamforming, which concentrates the radio energy to extend the range.
  • sub-6 GHz spectrum: best for broad 5G coverage and capacity with faster, more uniform data rates both outdoors and indoors for more users, simultaneously.

“5G handsets are super-sophisticated: they need to support Massive MIMO techniques, along with beamforming, sub-6GHZ bands, and mmWave for mobile. Designing all of this to work together is putting real pressure on antenna and RF designs – and then the ultimate challenge, physically fitting all of this into a beautiful handset design!”

“And if that’s not complex enough, we all expect our mobile devices to have incredibly efficient batteries, and yet remain small and lightweight, and deliver performance enhancements across 4G, 3G and GSM. You need very strong modems and processors deployed inside 5G handsets – and all of this in addition to the complexity 5G adds to software, OS and Kernel layers. That’s why it is not an easy job to deliver high-end 5G handsets!”

Opportunity

“There are many challenges, opportunities and battles to come as 5G rollout continues, and it will also create real opportunities and big returns if you have positioned yourself and your company right within the ecosystem.”

In conversation with Abdulrahman Fady, Digis Squared CTO

If you would like to learn more about how the Digis Squared team can help you with 5G strategy, deployment or optimisation, please use this link or email sales@DigisSquared.com to arrange an informal chat.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Digis Squared, independent telecoms expertise.

Sources

Abbreviations

  • C-IoT: Consumer Internet of Things (typically, consumer devices and applications in the consumer electronics space such as smartwatches or smart thermostats)
  • CSP: Communications Service Providers
  • ICT: Information and communications technology
  • IIoT: Industrial Internet of Things (interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management)
  • Massive MIMO: a set of multiple-input and multiple-output technologies for multipath wireless communication, in which multiple users or terminals, each radioing over one or more antennas, communicate with one another.
  • O-RAN: Open RAN – via standardised radio interfaces and interoperability, hardware and software components from multiple vendors operate over network interfaces that are “open and interoperable”
  • SIs: System Integrators
  • URLLC: Ultra-Reliable Low Latency Communication

Image credit: Denys Nevozhai

INOS 5G ◦ Now more than ever, test and optimise your 5G network

INOS ◦ now with 5G & multi-vendor chipset support

Enhanced 5G benchmarking and testing capability, OpenRAN functionality testing, and multi-vendor 5G chipset support, the latest major new features added to INOS ensure clients have access to valuable commercial capability.

INOS – the independent telecoms network benchmarking, drive-test and in-building solution developed in-house at Digis Squared – has just been enhanced to deliver major new features to our telecom operator, CSP* and Regulatory clients, including those managing Private Networks. These new features deliver significant new capability to uncover and resolve even more telecom network issues, and enhance customer QoE and network QoS.

“The new 5G INOS features announced today will help MNOs better understand and optimise their network performance, including in deployments with complex multi-vendor architectures and OpenRAN. This is great news for our clients needing Covid-19 safe solutions to optimise their 5G infrastructure, for Regulators working to obtain an independent view of total network performance, and ultimately to the end customer seeking a better connection.”

AbdulRahman Fady, Digis Squared CTO

New 5G capability

  • Extending the range of network testing capabilities, the new enhancements add 5G to our 2G, 3G, 4G and IoT (CAT-M, NB-IoT 1, NB-IoT 2) network capability, across voice, video, data and OTT
  • The new INOS 5G complete testing set gives you visibility of more than two hundred different network KPIs
  • 5G benchmarking solution gives you full visibility of network QoS and customer QoE
  • 5G L3, L2 and L1 signalling capability
  • 5G fully automated single site verification drive testing solution
  • 5G indoor (in-building survey) testing capability.

Extended handset support

  • Now supporting multi-vendor 5G chipsets: Huawei, Samsung and Qualcomm flagship mobiles.

O-RAN support

  • OpenRAN functionality testing, end to end, from radio through to interoperability and benchmark testing between OpenRAN and Legacy RAN
  • Ensures you can pin-point which component in your multi-vendor ecosystem needs to be optimised or investigated further.

Cloud control – for instant updates, and Covid-19 safety

  • Our cloud-controlled INOS automated testing platform delivers both drive testing, and in building survey data, enabling operators and service providers to efficiently obtain the insights needed for key decisions.
  • Our tools need just one person in the vehicle or building – no engineers are needed on-site, ensuring that they can do their work safely and together we can keep our communities connected.
  • Detailed, actionable automated reports are generated within just 15 minutes after tests are completed.
  • Additionally, our real-time-view ensures you can immediately take action to address performance issues, and optimise your capability whilst engineers are still in the field. Make adjustments, OTA update test parameters and re-run your analysis swiftly.

Independent telecoms network analysis and benchmarking just got smarter.

Know your strengths, and weaknesses, across all network technologies. Now more than ever, ensure you know the capability, performance, quality of experience and coverage of your voice and data networks, and that of your competitors, so that you can optimise your assets efficiently. Discover more about how INOS can help you, here.

Now more than ever, test and optimise your 5G network.

To discuss how our independent tools and vendor-agnostic expertise can help your business, please use this link or email sales@DigisSquared.com to arrange an informal chat.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Digis Squared, independent telecoms expertise.

Abbreviations

  • CSP: Communications Service Providers
  • INOS: Intelligent Network Optimisation Solution, one of Digis Squared’s AI-led automated tools.
  • MNO: Mobile Network Operator
  • OpenRAN: via standardised radio interfaces and interoperability, hardware and software components from multiple vendors operate over network interfaces that are “open and interoperable”
  • QoE: Quality of Experience
  • QoS: Quality of Service

Image credit: Tim Trad

INOS ◦ Now more than ever, know your network strengths, and weaknesses

Understand what has changed, then invest

As work patterns continue to change, operators struggle to model their network capacity and investment plans. Understanding current network coverage, performance and quality of experience, and that of competitors, is vital before investment decisions are made.

Our cloud-controlled INOS automated testing platform delivers both drive testing, and in building data, enabling operators and service providers to efficiently obtain the insights needed for key upgrade decisions. [Our tools need just one person in the vehicle or building – no engineers are needed on-site, ensuring that they can do their work safely and together we can keep our communities connected.]

Know your strengths, and weaknesses. Now more than ever, ensure you know the capability, performance, quality of experience and coverage of your voice and data networks, and that of your competitors, before you invest. Discover more about how INOS can help you, here.

Now more than ever, use INOS to benchmark coverage, performance & QoE.

To discuss how our network benchmarking expertise can help your business, please use this link or email sales@DigisSquared.com to arrange a convenient time for an informal conversation.

Keep up to speed with company updates, product launches and our quarterly newsletter, sign up here.

Digis Squared, independent telecoms expertise.

Image credit: Klavs Taimins