Staff Augmentation for Effective Project Delivery & Operations

Staff augmentation has become a popular strategy for delivering projects effectively while positively impacting project financials. This approach offers businesses the flexibility to scale their workforce up or down based on project needs, bring in specialized skills for short-term requirements, and optimize costs. 

Using Staff Augmentation for Effective Project Delivery & Operations 

1. Flexibility in Scaling:

Staff augmentation allows companies to quickly scale their team size according to project requirements. This flexibility ensures that projects can be handled efficiently without the need to hire full-time employees for short-term needs.

2. Access to Specialized Skills:

By leveraging staff augmentation services, organizations can access specialized skills and expertise that may not be available in-house. This is especially beneficial for projects that require niche capabilities or temporary support in specific areas.

3. Cost-Effectiveness:

One of the key benefits of staff augmentation is its cost-effectiveness. Rather than bearing the overhead costs associated with full-time employees, businesses can utilize external resources on a project-basis, reducing overall expenses.

4. Faster Project Delivery:

With the ability to quickly onboard additional resources through staff augmentation, projects can be completed faster and more efficiently. This accelerated turnaround time can lead to increased client satisfaction and competitive advantage.

Positive Impact on Project Financials

1. Reduced Overhead Costs:

Staff augmentation allows companies to avoid the costs associated with hiring and retaining full-time employees, such as salaries, benefits, training, and infrastructure. This cost-saving element directly impacts project financials positively.

2. Improved Budget Control:

By only paying for the resources utilized during the project duration, organizations can better control their project budgets. This results in more accurate cost estimation and allocation, reducing the risk of budget overruns.

3. Enhanced ROI:

With staff augmentation, businesses can allocate resources where they are most needed, optimizing project efficiency and ROI. The ability to access specialized skills and scale teams as required contributes to a higher return on investment for projects.

4. Mitigation of Employee-related Risks:

Engaging external resources through staff augmentation helps mitigate risks associated with full-time employees, such as turnover, training costs, and legal responsibilities. This risk mitigation positively impacts project financial stability.

The Future of Staff Augmentation Business

1. Continued Growth:

As businesses seek flexible workforce solutions and specialized expertise, the demand for staff augmentation services is expected to grow. This trend is fueled by the need for agility, cost-efficiency, and access to diverse talent pools.

2. Emphasis on Technology Integration:

The future of staff augmentation will involve a greater emphasis on technology integration, automation, and AI-driven solutions. This shift aims to enhance service quality, streamline processes, and deliver better value to clients.

3. Global Talent Pool Access:

Staff augmentation providers will increasingly tap into global talent pools, offering organizations access to a broader range of skills and capabilities. This globalized approach enables businesses to leverage diverse expertise regardless of geographic limitations.

4. Focus on Compliance and Security:

With data privacy regulations and cybersecurity concerns on the rise, the future of staff augmentation will prioritize compliance and security measures. Providers will invest in robust safeguards to protect client data and ensure regulatory adherence.

In conclusion, staff augmentation is a strategic approach that enables organizations to deliver projects effectively, optimize project financials, and adapt to evolving business demands. As the workforce landscape evolves, the future of staff augmentation businesses will be shaped by technology integration, global talent sourcing, and a steadfast commitment to compliance and security.

NFV deployment validation using INOS

Network Function Virtualization (NFV), is becoming increasingly important as mobile networks are being asked to handle an ever-growing number of connected devices and new use cases. In this article, Amr Ashraf, RAN and Software Solution Architect and Trainer, describes the benefits of NFV, capabilities and deployment considerations. Plus, we take a quick look at how Digis Squared’s powerful AI-tool, INOS, can help in the deployment validation of NFV.

Network Function Virtualization

Mobile virtualization – also known as network function virtualization (NFV) – is a powerful technology that has the capability to transform the way mobile networks are designed, deployed, and operated.

  • NFV enables the creation of virtualized mobile networks, and the isolation of different types of traffic on the same physical network infrastructure.
  • The creation of different virtual networks for different types of services or different user groups.
  • Multiple independent network operators to share a common infrastructure,
  • And improves the security of the network.

In this article, Amr Ashraf describes the benefits of NFV, capabilities and deployment considerations. Plus, we take a quick look at how Digis Squared’s powerful AI-tool, INOS, can help in the deployment validation of NFV.

The future of mobile network functions is virtual

Mobile virtualization is becoming increasingly important as mobile networks are being asked to handle an ever-growing number of connected devices and new use cases.

NFV & Infrastructure Sharing. One of the main benefits of mobile virtualization is that it allows for multiple independent network operators to share a common infrastructure. This can help to reduce the costs and complexity of building and maintaining mobile networks, and can also help to improve coverage and capacity in areas where it would otherwise be difficult or expensive to deploy new infrastructure.

NFV & Security. Mobile virtualization also helps to improve the security of the network by isolating different functions and providing a secure environment for each virtual network. This makes it an ideal solution for enterprise customers who need to maintain high levels of security for their sensitive data.

Deployment flexibility. Mobile virtualization is supported by software-based virtualized network functions (VNFs), which can be run on standard servers and storage systems, rather than specialized hardware. This makes it easy to scale and adapt the network to changing requirements. Additionally, it also makes it possible to deploy mobile virtualization solutions in a variety of different environments, including on-premises, in the cloud, or at the edge of the network.

NFV & 5G customisations. It’s worth noting that mobile virtualization is a key technology in building the 5G network. 5G network standards are designed to support network slicing, which can create multiple isolated virtual networks on top of a common physical infrastructure. This makes it possible to create customized solutions for different types of users and use cases, such as providing high-bandwidth services for multimedia applications, or low-latency services for industrial automation and control.

NFV is the future, and the future is now. Mobile virtualization is a rapidly evolving technology with considerable potential to transform the way mobile networks are designed, deployed, and operated. In the coming years, we expect to see more and more operators turning to mobile virtualization to meet the growing demands on their networks and stay competitive in the fast-changing mobile landscape.


Implementing mobile virtualization can present a number of technical challenges, including the management and orchestration of virtualized network functions (VNFs) and ensuring network security. Managing and orchestration of VNFs is a complex task, which involves provisioning and configuring VNFs, as well as ensuring their availability and performance. This is complicated by the fact that VNFs are software-based and can be deployed on a variety of hardware and virtualization platforms.


As VNFs are software-based, they can be targeted by cyber-attacks just like any other type of software. Therefore, ensuring network security is vital when implementing mobile virtualization.

Additionally, virtualized networks may be vulnerable to new types of attacks that exploit the virtualization itself.

NFVO. One of the key solutions to these challenges is the use of network function management and orchestration (NFVO) systems. NFVOs automate the provisioning, configuration, and management of VNFs, and they help to ensure that the VNFs are highly available and perform well. They also play an important role in the orchestration of VNFs, which involves coordinating the actions of multiple VNFs to achieve a desired outcome.

Strong defences. Another key solution is the use of security solutions such as firewall, intrusion detection and prevention systems, secure VPN, and secure containers to protect the virtualized network, secure communication between virtualized functions, and protect virtualized infrastructure from unauthorized access.

Anomaly detection. Solutions based on artificial intelligence and machine learning can also be used to monitor and detect anomalies in the network, identify potential security threats, and take appropriate action to mitigate them.

Digis Squared recommend involving INOS Probe to undertake anomaly detection 24/7, and send these alerts to the CSP. Read more – Anomaly detection: using AI to identify, prioritise and resolve network issues.

Security strategy. In addition to these technical solutions, it’s also important to have a comprehensive security strategy in place to address any potential vulnerabilities and threats that may arise when implementing mobile virtualization. This can include implementing best practices for network design, conducting regular security assessments, and keeping systems and software up to date with the latest security patches and updates.

Skills & expertise. An often overlooked, yet important security consideration, is the need for skilled personnel who are well-versed in the technologies and best practices associated with mobile virtualization. As mobile virtualization is a complex technology that requires a deep understanding of network functions, security, and software development, it’s crucial to have a team of experts who can design, deploy, and maintain secure mobile virtualization solutions.


Drive testing can be used to validate the performance of virtualized network functions and ensure that they are providing the desired level of service. This can help to identify and troubleshoot any issues that may arise, such as poor performance or dropped connections. Drive testing can also be used to compare the performance of virtualized network functions with that of traditional, hardware-based network functions, in order to ensure that the virtualized functions are providing an equivalent or better level of service.

Digis Squared’s AI-solution INOS is an essential tool in the implementation and ongoing optimization of NFV. It helps to validate and troubleshoot virtualized network functions and ensure that they are providing an equivalent or better level of service compared to traditional, hardware-based network functions. Additionally, drive testing provides key information about the environment in which the network is deployed that can be used to optimize the deployment of virtualized network functions.


Mobile virtualization is a powerful technology that has the capability to transform the way mobile networks are designed, deployed, and operated. Key benefits it enables include,

  • The creation of virtualized mobile networks, and the isolation of different types of traffic on the same physical network infrastructure.
  • The creation of different virtual networks for different types of services or different user groups.
  • Multiple independent network operators to share a common infrastructure,
  • And improves the security of the network.

However, implementing mobile virtualization can present a number of technical challenges, including the management and orchestration of virtualized network functions (VNFs) and ensuring network security.

The use of network function management and orchestration (NFVO) systems, security solutions, AI/ML-based monitoring and anomaly-detection systems, and a comprehensive security strategy can help to mitigate these challenges.

Finally, NFV is a powerful, yet complex technology – it’s essential to work with an experienced team with deep expertise who can design, deploy, and maintain mobile virtualization solutions.

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

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.

See INOS in action at LEAP, Riyadh & MWC Barcelona

Digis Squared will be at LEAP in Riyadh at the start of February, as part of the UK Pavilion H4.G30, undertaking cloud-based INOS demos. Plus the team will be at MWC Barcelona at the end of February, with a full suite of all the INOS solutions and form factors on a dedicated exhibition stand Hall 7 B13.

Get in touch to arrange a dedicated time to meet:

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Digis Squared ◦ Enabling smarter networks.

Business Insider: “Discover Five of the Most Innovative UK Telecoms and Technology Firms”

“Connecting the world. As the UK expands its investment in STEM sectors, these firms are developing cutting-edge technology and building new ways to stay connected.”

This text is taken from a post by Business Insider / Insider Studios, which lists Digis Squared, what3words, CommAgility, pureLiFi and Speechmatics as five of the most innovative UK telecoms and technology firms. Read their full article here.



The telecoms and technology industries are likely to keep up their rapid rate of expansion over the next decade. This presents an opportunity for investment and development in the companies that are driving progress forward.

These 5 UK-based firms are highly innovative in their fields, from using light to enable wireless internet, to deploying artificial intelligence (AI), to removing bias in speech recognition. These are the companies whose pioneering technology will capitalize on an increasingly connected world.


Digis Squared

Digis Squared enables telecom operators and communications service providers to optimize, upgrade, manage, and enhance their networks. Its clients include the likes of Vodafone, Telefónica, and Telecom Egypt, and it has offices in the UK, Egypt, and Dubai — as well as a new site in Luanda, Angola, which opened in October.

“Our new office in Luanda will on-board a team of over 40 engineers providing managed services for the entire Africell Angola network,” Mohamed Hamdy, the company’s chief commercial officer, said. The new office will also support “the commercial launch of [Africell Angola’s] new network to the public in 2022.”

Africa is a huge market for the firm, which also plans to open a new facility in Saudi Arabia in 2022. Key priorities for the year include expanding its footprint in its target markets and doubling its revenue. The rollout of 5G — and planning for 6G deployment — means the need for communication technology is likely to dramatically increase in both scale and complexity. This will lead to increased demand for Digis Squared’s services over the next five to 10 years, according to CEO Ziad Khalil.

“The ability for our business to work internationally is vital — communication is both constrained and unlimited by boundaries,” Hamdy added. “Digis Squared was set up as a multi-country operation and international trade has been at the heart of our approach right from the start. Working with DIT has been a key pillar of this.”


As new technology and demand for innovation leap forward, new ways to communicate will also need to accelerate. An international approach for telecoms and tech companies is crucial, and with support from the public sector to build relationships and provide investment, there is a bright future ahead.



Digis Squared, independent telecoms expertise.

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.


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 .

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Digis Squared, independent telecoms expertise.


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 .

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Digis Squared, independent telecoms expertise.


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 .

Digis Squared, independent telecoms expertise.

Image credits

  • Digis Squared social media and blog banner image: NASA

Technology sunset ◦ Navigating a route from legacy networks to the future

In conversation with Digis Squared CTO, Abdulrahman Fady, we discuss mobile network technology sunset issues and opportunities.

As 5G rollouts gather pace globally, and new technology deployments continue their unstoppable march, many networks are also grappling with what to do about legacy technologies. In 1991 Radiolinja launched 2G in Finland, and 2001 brought the first 3G launch, achieved by NTT DoCoMo in Japan – both network technologies are still in active commercial use around the world, but for how much longer?

Abdulrahman Fady, CTO at Digis Squared, has worked in the technology sector for more than 20 years, and joined Digis Squared in 2018. In this blog post he shares his analysis of mobile network migration strategy and implementation in the context of network technology sunset issues and opportunities.

“Spectrum resources are finite, and operators wanting to launch new technologies need to either license new spectrum, if available, or re-allocate spectrum used for 2G and 3G. In most regions now re-allocation is the only option.”

“These old legacy technologies, 2G and 3G, they’ve been around for so long, that it’s tempting to think you could just switch them off and re-allocate the spectrum when utilisation drops below a certain threshold. But these old technologies continue to serve some really important markets. Firstly, low-income families often utilise older handsets which can only connect to 2G or 3G networks, and these provide a vital connection to the internet and mobile apps, across all geographic regions. These older handsets also tend to be simpler – appreciated by elderly users who don’t seek the complexity of smartphones.”

“And secondly, IoT. Early IoT deployments are often limited to only being able to connect to 2G or 3G networks, and physical replacement in many IoT use cases is frequently prohibitively expensive or geographically difficult.”

But some networks have already decommissioned their 2G networks – particularly Singapore, New Zealand and Australia. How did they achieve that?

“In Australia and New Zealand it’s the MNOs which have driven their 2G shut down. A low number of 2G M2M customers and the relative wealth of their consumer customers has mitigated most of their risks, but care still needs to be taken in this type of situation. If you are the first operator in a territory to switch off the old legacy network, you effectively force the churn of those 2G-only customers who won’t or can’t upgrade to your competitors – and so if you’re the last operator to switch off your 2G network, you might well have “acquired” all the low ARPU, low margin 2G consumers and IoT connections. Depending on the region, the regulator may intervene to force the maintenance of a rationalised legacy network, with lower capacity and coverage for low demand but critical IoT infrastructure, and vulnerable low income groups.”

“Other Asian countries have worked with regulator-led projects to decommission 2G, and reallocate network spectrum. But the commercial elements of these projects are not easy: M2M migration costs had to be negotiated in Singapore, New Zealand had to facilitate individual migrations, and continue to support a million smart meter connections.”

There are other commercial impacts too. “Mini-links and other microwave services on base stations were able to handle voice-only 2G and 3G demand, but when 2G and particularly 3G services are switched-off this drives an increase in demand for backbone services, and this in turn reduces the need for tower services, backhaul and transmission services.”

“In Europe, conversely, 3G networks are being turned off first, as there are a greater number of legacy M2M connections in the territory. These 3G devices can default down to the earlier 2G technology – a fallback strategy initially conceived to address coverage issues when 3G first launched, is now helping the more advanced technology become obsolete earlier!”

Partnering for change

“Whenever a network is switched off, the impacts on the remaining technologies will be considerable,” Abdulrahman explains. “Typically the switch off is more of a switch-over, as cell capacity is first reduced and then de-commissioned. Re-balancing and optimisation of the network loads is active and ongoing throughout the transition process, being undertaken with care to achieve minimum disruption. Working with strong, experienced partners in both the strategy and implementation phases, who can flexibly handle projects as unexpected issues arise, is crucial. Add multi-vendor network components into the mix, and the benefits of working with staff who have experience across all vendors and technologies can be vital to achieving a smooth network migration.”


“Whilst technology sunsets can initially seem complex, with careful consideration and planning, the process will deliver considerable benefits. The new network technologies reduce power usage and carbon footprint, and deliver enhanced speed, bandwidth and security. And, as day follows night, it is inevitable – it’s always better to be prepared and ready to make the most of the opportunity a new day brings!”

In conversation with Abdulrahman Fady, Digis Squared CTO.

Discover more

If you would like to learn more about how the Digis Squared team can help you with technology sunset and 5G strategy, deployment or optimisation, please use this link or email 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.



  • CAT-M1: see LTE-M.
  • NB-IoT: Narrowband Internet of Things. One of two data networking technologies available on 4G (the other is LTE-M, aka CAT-M1). Intended for narrow band (250 kbps) low power data applications and does not support voice communications.
  • LTE-M: LTE Machine Type Communication. Also known as Cat-M1. One of two data networking technologies available on 4G (the other is NB-IoT). Provides considerably higher bandwidth (1Mbps), supports voice and full mobility.

Image credit: Quino Al

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!”


“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 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.



  • 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

Regulators ◦ Now more than ever, use independent tools and expertise

Is this the perfect storm of telecoms technical complexity?

As network deployments get more complex, capacity management more difficult to predict, and customer demands rise, how can Telecoms Regulators help deliver the best customer experience?

Globally, 5G deployments are picking up pace, and 2G and 3G networks starting to be retired – engineers at MNOs and CSPs* are knee-deep in complexity, managing technology sunset strategies, IoT connectivity migrations, and adding new layers of 5G components into the patchwork of systems from multiple vendors. This activity brings with it more new operational systems and alarms to integrate (and disentangle), and extra work to try to bring everything together into a cohesive system.

On top of that, the operational teams within MNOs and CSPs have been working hard this year to reconfigure networks to handle shifts in demand, as the pandemic forces huge numbers of people to suddenly work and study from home, and unpredictable demand patterns are addressed as best as possible.

Is this the perfect storm of telecoms technical complexity? How should Telecoms Regulators respond and ensure customer Quality of Experience and Quality of Service are maintained? With so many technical changes occurring in a short space of time, how can technical regulatory staff keep pace with technology, anticipate the future, and ensure their knowledge-base remains unbiased?

Digis Squared has over 50 industry experts with 10 or more years’ multinational mobile operator and vendor experience.

Use our expertise to work alongside your teams and augment their skills and capability,

  • Independent tools for QoE & QoS network benchmarking
  • Band & spectrum strategy consultation
  • Competence development to keep pace with new technologies.

“The Digis Squared team has a depth of experience and knowledge of implementations that you only acquire through years of working on difficult projects and tricky technology deployments. The team bring these insights to all their work, whether that’s with MNOs, CSPs or Regulators.

Mohamed Hamdy, Digis Squared CCO

Independent tools for QoE & QoS network benchmarking

Regulatory coverage and performance concerns vary by market, but in general fall into 3 distinct areas,

  1. Many telecom network licenses have requirements to achieve specific KPIs: Geographical coverage, data throughput rates, QoS requirements.
  2. Legacy benchmarking solutions are often expensive, no longer supported by the vendor, and have a long and slow process to deliver the final report.
  3. When the report is eventually available, it is often a readout of dry statistics, with no clear recommendations on improvements. And with multiple solutions from multiple vendors implemented across the MNOs and CSPs in your territory, there is no standard process to rank and compare network operators.

Since the inception of Digis Squared the leadership team decided to invest and develop its own in-house, vendor-agnostic, multi-technology and scalable automated solutions, to ensure its staff and clients have access to vendor-independent assessment and testing of networks. Today, we are able to provide our clients with these tools to ensure they have an independent assessment of network capabilities. MNOs use our tools to help them accelerate network upgrades and network transformation, ensuring they are able to manage their network traffic growth and network complexity efficiently. Regulators use our tools to ensure they have the insights they need to assess KPIs independently.

INOS is the AI-led QoS and QoE benchmarking tool developed in-house at Digis Squared, using no network vendor tools.

  • Automated and efficient solution for fast and accurate reporting
  • Analysis and recommendations on improvements
  • Cross-check performance against the license to help regulators identify the gaps
  • Proven in the field with MNOs and Regulators
  • Interacts with all major vendors’ platforms, including Ericsson, Huawei and Nokia
  • Covid19 safe solution: 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.

When used by Telecoms Regulators, INOS delivers,

  • One independent, vendor and network agnostic solution
  • Fully automated reports, just 15 minutes after tests end
  • Failure insights: empowered by automation and analytics, we can deliver detailed insights into failure reason
  • INOS BM score – rank and benchmark all operators, by all services tested, across all network technologies and vendors
  • Independent and transparent scrutiny: Operators can access INOS platform, with limited and agreed privileges to review their log files and reports.

INOS KPIs include, but are not limited to,

  • Coverage & quality radio conditions
  • Field KPIs: CST, CSSR, HOSR, CDR
  • Throughput DL & UL: FTP, HTTP, HTTPs
  • Voice quality: POLQA
  • Video Quality: PEVQs
  • OTTs KPIs
  • Adopted optimisation strategy
  • Overlapping and needed neighbours’ optimisation
  • UE Happy Index

Get in touch to talk with us informally about how we can help your Regulatory teams with independent tools and expertise for network benchmarking, and discover INOS here.

Band & spectrum strategy consultation

The Digis Squared team have decades of experience working in telecoms operators and telecom equipment providers, with huge experience across many countries, implementations, technology deployments, and vendor solutions. We can work alongside your teams, or independently, to share our insights and assess innovative and commercial uses of your spectrum, to ensure optimum utilisation in your market.

  • Assess the utilisation of all existing bands
  • Evaluate service usage and importance with stakeholders
  • Policy & procedure updates
  • Spectrum audit and redeployment strategies: identify, complement, and refine all data on national spectrum use
  • Future policy: balancing the needs of end-users and spectrum-users are met to encourage investment
  • Emerging technologies: implementation scenarios and spectrum allocation recommendations for 5G, WLAN, LPWA and more.

Competence development & training

We recognise the difficulties in identifying the vendor-independent training necessary to ensure your teams are not unconsciously biased towards specific solutions.

Our team of experienced staff are well placed to deliver a broad range of technical and non-technical training.

Our approach for competence development utilises different methods to best suit the client, their culture and team needs, with an emphasis on on-job training as well as classroom training, delivered as active, participatory workshops and webinars by our technology experts. We can deliver training on-site, remotely via video link, or in your own time via online material.

Get in touch to arrange a no-obligation discussion with our team, or request a copy of our Technical Training Catalogue:

“In my view, it’s more important than ever that Telecoms Regulators use independent expertise and tools in their assessments, to ensure they have a complete view of their ecosystem, and prepare for whatever storms are on the horizon.”

Mohamed Hamdy, Digis Squared CCO

Now more than ever, use independent tools and expertise in regulatory assessments.

To discuss how our independent tools and vendor-agnostic expertise can help your Regulatory Teams, please use this link or email to arrange a video call.

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

Digis Squared, independent telecoms expertise.


  • CSP: Communications Service Providers
  • INOS: Intelligent Network Optimisation Solution, one of Digis Squared’s AI-led automated tools.
  • MNO: Mobile Network Operator
  • QoE: Quality of Experience
  • QoS: Quality of Service

Image credit: Michael D.

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 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