Tag: Digis Squared team

2025 in Review: From Complexity to Cognitive Operations | Charting Our Course for 2026 

Posted on by Mira Abdulrahman

Reflections in 2025: Progress and Transformation at Digis Squared 

As we approach the end of the year, it is worth pausing to reflect on the significant technological shifts that have characterized 2025. At Digis Squared, our focus has been not only on innovation, but also on achieving measurable progress in addressing the ongoing challenges faced by Mobile Network Operators (MNOs) and the telecommunications sector. This year has marked a turning point, where the concept of intelligent networks has begun to materialise in practical operations. This transformation has been fuelled by the urgent need to manage increasingly complex systems, respond to mounting cost pressures, and navigate a rapidly changing threat environment. 

Operational Consolidation and Application 

In my role as Chief Technology Officer, I have witnessed 2025 becoming the year of consolidation and practical application. Our collective efforts have moved beyond the initial excitement surrounding emerging technologies such as generative AI. Instead, we have dedicated ourselves to the tangible development of networks that are capable of self-awareness and self-optimisation. The momentum established through the deployment of our solutions and the insights gained from real-world implementations now positions us strongly for an even more dynamic year ahead in 2026. 

Looking Back and Ahead: Addressing Core Challenges and Future Trends 

This review aims to summarize the fundamental challenges currently facing the telecoms industry, to highlight the ways in which our technologies are directly tackling these issues, and to share my perspective on the trends that are likely to shape the future of our sector. 

The Complexity Crisis in Modern Telecom Networks 

The modern telecommunications network presents a formidable challenge for operators due to its intricate composition. These networks are built from a vast array of equipment sourced from multiple vendors, layered with technologies that span generations—from 2G through to 5G—and encompass a diverse range of domains such as Radio Access Networks (RAN), Core networks, Transport, Business Support Systems (BSS), and IP Multimedia Subsystems (IMS). 

This inherent complexity has evolved into a significant crisis for Mobile Network Operators (MNOs). The interplay of different vendors and technologies, combined with the multifaceted nature of network operations, has resulted in a landscape where operational efficiency is frequently compromised. Critical challenges have emerged; each demanding the implementation of cognitive solutions to restore control, streamline processes, and ensure the ongoing viability and security of telecom operations. 

Challenge Area Description Impact  
Operational Inefficiency Sheer volume of alarms and data makes root cause analysis slow and manual. Long Mean Time To Resolution (MTTR) and high operational expenditure (OPEX)1. 
Budget Constraints Pressure to reduce OPEX while simultaneously investing in 5G, 6G R&D, and network modernization. Need for highly efficient, automated tools that minimize human intervention. 
Cybersecurity Exposure The vast attack surface of IoT devices and distributed networks, coupled with sophisticated AI-driven threats. Non-negotiable security requirements, demanding proactive, predictive defense and Zero Trust adoption3. 
Vendor Lock-in & Silos Disparate vendor systems create data silos, hindering end-to-end visibility. Costly, inflexible contracts and inability to optimize across the entire network4. 
Sustainability Mandate Massive energy consumption from 5G and data centers drives the need for a rapid transformation toward Net Zero. Economic and regulatory pressure to integrate energy efficiency into network design. 

Reflections on 2025: From Potential to Practice 

As I reflect on the entirety of 2025, it becomes evident that the year was marked not by a wave of groundbreaking inventions, but rather by the steady advancement and practical implementation of robust, established technologies. Over the past twelve months, the narrative shifted decisively from the anticipation of future possibilities to the tangible realisation of enterprise-ready solutions. 

Across several critical sectors, we observed a transition: technologies that once held only theoretical promise are now being deployed at scale and integrated into real-world business operations. This maturation has underscored the value of reliable, effective tools, demonstrating how innovation is as much about refinement and application as it is about invention. 

  1. Generative AI Democratizes Network Operations 

If previous years were about the novelty of Generative AI, 2025 was about its utility. We moved beyond simple automation to true cognitive operations by integrating GenAI directly into the operational workflow. 

  1. The Strategic Importance of Digital Twin Technology 

The concept of a Digital Twin—a virtual replica of a physical system—is not new, but its application in telecom reached a critical maturity point in 2025. This technology allows MNOs to simulate complex changes, test new configurations, and predict the impact of traffic surges before they occur on the live network. 

  1. Private 5G Powering Industry 4.0 

The private network market accelerated rapidly, with projections showing a 65.4% CAGR in private 5G connections through 2030. Industries moved beyond proof-of-concept to live deployment, particularly in logistics and manufacturing. 

  1. Cybersecurity as a Core Business Function 

The increasing complexity of our networks and the rise of sophisticated AI-driven threats made cybersecurity a top priority in 2025. The focus shifted from reactive defense to proactive, predictive security postures. We saw greater adoption of Zero Trust architectures and AI-powered threat detection systems. For telecom operators, securing the vast attack surface of IoT devices and distributed networks became a non-negotiable aspect of service delivery. 

  1. 5G Expansion and the Dawn of 6G Research 

Throughout 2025, 5G continued its global expansion, particularly in emerging markets where its impact on enterprise connectivity is most profound. 6G research also increased exponentially in 2025 targeting specific applications like CF-MIMO , RIS and ISAC .  

  1. Field Testing Takes to the Skies (Green Operations) 

2025 marked a turning point for field testing sustainability. Traditional drive testing, with its high fuel consumption and limited access to difficult terrains, began to give way to drone-based solutions. 

  1. AI-Automated Planning & The 15-Minute Optimization 

The era of manual network planning effectively ended in 2025. The complexity of multi-layer networks made manual parameter tuning obsolete, we also perceived massive increase in the demand for SMART CAPEX and OPEX solutions. 

  1. Active Probing & Service Assurance 

As networks became more complex, “passive” monitoring was no longer enough. 2025 saw a rise in Active Service Assurance. 

  1. Quantum Computing’s Cautious Breakthroughs 

While still in its early stages, quantum computing made tangible progress in 2025. We saw advancements in qubit stability and error correction, moving quantum systems closer to solving real-world optimization problems that are intractable for classical computers. For telecom, this holds immense promise for complex tasks like network routing optimization and spectrum management. Though widespread application remains on the horizon, the progress this year has solidified its potential as a game-changing technology. 

2026 Technological Outlook and Predictions 

As the telecommunications industry builds upon the momentum generated in 2025, the year ahead is set to deliver significant advances in network intelligence and integration. The following are detailed forecasts for the principal trends that are expected to shape 2026: 

  1. AI-Native Networks Become the Industry Standard 

The application of artificial intelligence will evolve beyond mere overlays. In 2026, the emergence of AI-native networks will see machine learning deeply embedded within the air interface and protocol stack. Key network elements, such as Radio Units and Distributed Units, will develop “Sense-Think-Act” capabilities, empowering them to make micro-adjustments in beamforming and spectrum allocation within milliseconds. 

  1. The Dark NOC as a Strategic Priority 

In response to ongoing budgetary pressures, the Dark NOC—where up to 80% of operational tasks are autonomously managed—will become a primary strategic objective for mobile network operators (MNOs). This shift will redefine the role of human engineers, transitioning their focus from routine monitoring and repairs to governance and design. The reliance on vendor-agnostic platforms will increase, aiding in the unification of data across fragmented ecosystems. 

  1. Transition to Post-Quantum Cryptography (PQC) 

The migration towards Post-Quantum Cryptography will progress from the planning stages to phased execution. Telecom operators are anticipated to begin upgrading encryption keys and hardware security modules, aiming to counteract the risk posed by potential adversaries who may harvest encrypted data now, with the intention of decrypting it in the future as quantum computing matures. 

  1. Intensification of 6G Standards Prototyping 

The development of 6G standards will move from theoretical discussions to tackling targeted engineering challenges. One of the central focuses will be Semantic Communications, which shifts the paradigm from transmitting raw bits to communicating actual meaning. By leveraging AI to filter out irrelevant data at the source, 6G networks are expected to realise substantial efficiency gains. 

  1. Agentic AI and the Advancement of Cybersecurity Autonomy 

2026 is poised to be recognised as the “Year of the Defender” due to the emergence of Agentic AI—autonomous systems capable of independent decision-making. These intelligent agents will prove indispensable in defending against AI-driven identity attacks. Unlike conventional static playbooks, Agentic AI can reason, adapt, and autonomously develop defence strategies to address vulnerabilities in real-time. 

  1. Private Networks Will Drive Enterprise Digitalization 

The demand for private networks will accelerate in 2026 as more enterprises recognize their value in achieving secure and reliable connectivity for mission-critical operations. 

2025: A Year of Achievement 

Throughout 2025, we played a pivotal role in driving technological advancements for our clients and customers. Acting as a cornerstone of progress, our team delivered a series of significant accomplishments that underscored our commitment to innovation and excellence. The following is a sample of the achievements realised during the year, reflecting our ongoing dedication to supporting our clients’ evolving needs and enabling their success in an increasingly complex technological landscape. 

  • Our KATANA platform proved essential here, offering a vendor-agnostic “Single Pane of Glass.” By integrating data from Huawei, Nokia, Ericsson, and new Open RAN vendors into one topology view, we solved the fragmentation issue. This enabled Zero Touch Provisioning (ZTP) via our iMaster module, allowing devices to be automatically configured and integrated upon connection, regardless of the hardware vendor. 

  • We introduced ConnectSphere, an extension of our testing capabilities that deploys static probes in VIP areas and strategic locations. Unlike traditional drive tests that are periodic, these probes run 24/7 service testing to assure quality in real-time. This allows for proactive fault detection in both the Core and Radio domains, ensuring that high-value enterprise clients receive the Quality of Service (QoS) they demand 

  • We doubled down on OctoMind, our AI-based planning platform. By leveraging modules like X-Planner (for PCI/RSI planning) and ACP (Automatic Cell Planning), we moved from manual processes that took days to AI-driven optimization that takes just 15 minutes. This automation improved network performance metrics from 68% to 94% while eliminating human error in parameter configuration. 

  • We saw a surge in specialized use cases where Wi-Fi was insufficient. For instance, in pharmaceutical warehouses, we deployed Private 5G to support mission-critical forklift automation, autonomous mobile robots (AMR), and collision avoidance systems that require ultra-low latency. By implementing Managed Services that include L1 maintenance and radio optimization, we ensured these networks met stringent SLAs for reliability and security, keeping data strictly on-premises 

  • Through our INOS Air solution (in collaboration with DJI Enterprise), we enabled operators to conduct 5G, 4G, and 3G testing at specific altitudes (5m to 20m). This allows for testing in recreational areas, dense industrial compounds, and over water (marinas and ports) where cars simply cannot go. This shift not only expanded accessibility but also slashed OPEX and carbon footprints, aligning perfectly with the industry’s Net Zero goals by utilizing electric drones instead of fuel-heavy vehicles. 

  • We introduced GenAI Chatbots within our KATANA platform to democratize data access. Engineers no longer need complex SQL skills; they can simply ask natural language questions like “Which sites have hardware faults right now?” or “Why is the throughput low in site X?” to get instant, actionable insights. This capability allows non-technical staff to create business intelligence reports and resolve performance issues faster, significantly reducing the barrier to entry for advanced network analysis.  

  • We are actively leveraging and integrating Digital Twin technology into our cognitive solutions. By integrating the Digital Twin with the Network Data, we provide MNOs with a powerful tool for predictive Coverage and Capacity. This capability is a game-changer for planning and optimization, allowing MNOs to move from simply reacting to network events to proactively optimizing their infrastructure, ensuring network slicing integrity, and managing resources with unprecedented foresight. 

Digis Squared Focus in the Next Chapter 

Our strategic focus for the coming year is clear: to leverage our cognitive solutions to solve the industry’s most pressing challenges. 

Private 5G: Cognitive Operations at the Edge 

The successful deployment of Private 5G will hinge on the ability to deliver cognitive operations at the edge. Our focus will be on ensuring the success of these mission-critical networks by providing the necessary tools to manage unique demands. Tools like KATANA and ConnectSphere will be vital for ensuring real-time fault isolation, predictive capacity planning, and dynamic resource allocation for industrial automation. 

Net Zero: Energy Efficiency as a Strategic Priority 

The transformation toward Net Zero is a strategic priority. We will continue to refine our AI and cognitive tools to optimize network elements, dynamically power down resources based on predictive traffic patterns, and ensure that energy efficiency is a core design principle. 

  • Example: Our cognitive engine can predict low-traffic periods 72 hours in advance to automatically place specific 5G-enabled sites into an ultra-low power state, maximizing OPEX savings. 

Expanding the Cognitive Toolkit 

We will continue to expand the capabilities of our core platforms, further integrating the predictive power of the Digital Twin with the real-time insights of KATANA and the customer-centric metrics of INOS. Our goal is to provide a unified, vendor-agnostic platform that allows MNOs to achieve true operational autonomy and move confidently toward the Dark NOC. 

Closure  

The path forward is clear: the only way to manage the complexity, cost, and security demands of modern telecom networks is through intelligence. For telecom operators in emerging markets, these advancements offer a chance to leapfrog legacy systems and build agile, efficient, and intelligent networks. 

At Digis Squared, we are committed to turning these technological possibilities into operational realities for our partners. By focusing on cognitive operations powered by KATANA, INOS, OctoMind, and Digital Twin technology, we are not just optimizing networks; we are building the future of telecommunications—a future that is more efficient, more secure, and more sustainable. 

Author: Abdelrahman Fady | CTO | Digis Squared

The Road to 6G: Engineering Breakthroughs in the Terahertz Spectrum

Posted on by Mira Abdulrahman

While the theoretical possibilities are exciting, I’ve learned throughout my career that theory represents only one side of the equation. On the other side lies reality: signal loss, energy constraints, component limitations, and the unforgiving properties of our atmosphere. Today, I want to examine the engineering challenges that 6G must overcome to transform its spectral ambitions into practical, deployable technology.

Perhaps the most fundamental challenge we face is propagation loss. As frequencies increase, free-space path loss grows exponentially, a physical reality that cannot be engineered away. At 100 GHz, signal attenuation is already substantially higher than in traditional 5G bands. By the time we reach 1 THz, even a few meters of distance can drastically degrade signal strength. This isn’t merely an inconvenience; it fundamentally reshapes how we must approach network architecture. 6G will require advanced beamforming techniques, ultra-short-range cells, or reconfigurable intelligent surfaces (RIS) just to maintain basic communication links at these frequencies.

Atmospheric absorption presents another significant hurdle. In sub-THz and THz ranges, atmospheric gases—particularly water vapor and oxygen absorb electromagnetic waves in ways that create distinct challenges for wireless communication. Absorption peaks occur at specific frequencies: 183 GHz for water vapor and 325 GHz for oxygen, effectively creating “spectral dead zones” where long-range communication becomes impractical. Our strategy must therefore focus on identifying and utilizing transparency windows (such as 140 GHz) for viable communication links, while allocating other frequency bands for indoor or ultra-dense deployment scenarios where atmospheric effects are minimized.

The hardware requirements for THz communication represent perhaps the most immediate practical challenge. Today’s RF integrated circuits and front-end modules simply weren’t designed for terahertz operation. Silicon CMOS technology, the workhorse of modern wireless systems, begins to hit fundamental performance limits beyond 200 GHz. Alternative semiconductor technologies like Gallium Arsenide (GaAs) and Indium Phosphide (InP) show promise but remain expensive and less amenable to mass production. Beyond the semiconductors themselves, waveguide components, antennas, and packaging become highly lossy and mechanically delicate at these frequencies. Innovation pathways include hybrid integration approaches, nanophotonic technologies, plasmonic antennas, and metamaterials, all of which require substantial research investment before commercial viability.

Power efficiency emerges as another critical bottleneck. Power amplifiers operating at THz frequencies currently suffer from poor efficiency, generating excessive heat while delivering limited output power. In battery-constrained mobile devices, this inefficiency could render many theoretical applications impractical. Addressing this challenge will require multifaceted approaches: AI-driven energy management systems, novel energy harvesting techniques, and beam-aware hardware designs that minimize power consumption when full-power transmission isn’t necessary.

Precision timing and synchronization take on new importance at these frequencies. With the ultra-short wavelength characteristic of THz signals, even nanosecond-level timing errors can destroy link integrity. This impacts not just data transmission reliability but also the accuracy of sensing and positioning applications that 6G promises to enable. Meeting these requirements will demand high-stability clock sources, potentially including quantum timing references, and integrated sensing-transmission designs that maintain phase coherence across multiple functions.

The testing and simulation infrastructure for THz systems remains underdeveloped. Existing RF testbeds rarely extend beyond 100 GHz, creating a gap between theoretical models and practical verification. Simulation models for THz propagation are still evolving, and standards for THz-specific channel models are under development but not yet finalized. Without robust tools for repeatability and comprehensive test systems, mass deployment of THz technology remains speculative at best.

Finally, ecosystem fragmentation presents a strategic challenge. Unlike 5G, which benefited from relatively rapid ecosystem convergence around specific bands and technologies, 6G’s spectral frontiers are being explored in different frequency ranges across various countries and research institutions. Technical definitions and key performance indicators lack harmonization, and mainstream OEM and chipset vendor roadmaps have yet to fully incorporate these advanced frequency bands. This fragmentation could slow development and increase costs unless addressed through coordinated international efforts.

Despite these formidable challenges, I see tremendous beauty in the struggle to overcome them. These obstacles aren’t roadblocks; they’re invitations to innovate in ways that will transform not just telecommunications but multiple scientific and engineering disciplines.

The development of 6G will require an unprecedented fusion of telecommunications engineering, quantum physics, and materials science. Those who successfully bridge these domains will lead the industry forward not just in products and services, but in establishing entirely new paradigms for how we understand and utilize the electromagnetic spectrum.

As we navigate these challenges, I believe we’ll discover that the limitations imposed by physics aren’t constraints but catalysts forcing us to think more creatively, collaborate more effectively, and ultimately develop solutions that extend far beyond telecommunications into healthcare, environmental monitoring, security, and countless other domains that will benefit from mastery of the terahertz frontier.

This blog post was written by Head of Products, Mohamed Sayyed, at Digis Squared.

Semantic Communications: Use Cases, Challenges, and the Path Forward

Posted on by Mira Abdulrahman

Today, I want to delve deeper into the practical applications of semantic communications, examine the challenges we face in implementation, and outline what I believe is the most effective path forward.

Let’s begin by exploring the transformative potential of semantic communication across various domains.

In the realm of 6G and beyond, semantic communication will enable significantly leaner, context-aware data exchange for ultra-reliable low-latency communications (URLLC). This isn’t merely an incremental improvement; it represents a fundamental shift in how we approach network efficiency and reliability.

For Machine-to-Machine (M2M) and IoT applications, the implications are particularly profound. Devices will be able to understand intent without requiring verbose data transmission, resulting in substantial savings in both spectrum usage and energy consumption. In a world moving toward billions of connected devices, this efficiency gain becomes not just beneficial but necessary.

Autonomous systems present another compelling use case. When vehicles and robots can communicate purpose rather than raw data, we see marked improvements in decision-making speed and safety. This shift from data-centric to meaning-centric communication could be the difference between an autonomous vehicle stopping in time or not.

The future of immersive experiences, including extended reality, holographic communication, and digital humans, will increasingly rely on shared context and compressed meaning. These applications demand not just bandwidth but intelligent use of that bandwidth, making semantic communication an ideal approach.

Finally, Digital Twins and Cognitive Networks will benefit tremendously from real-time mirroring and network self-awareness based on semantics rather than full datasets. This allows for more sophisticated modelling and prediction with less overhead.

Despite these promising applications, several significant challenges stand in our way.

Perhaps the most fundamental is what I call “semantic noise” errors in understanding, not just in transmission. This represents an entirely new category of “noise” in the communication channel that our traditional models aren’t equipped to address.

Context synchronization presents another hurdle. How do we ensure that sender and receiver share enough background knowledge to interpret messages correctly? Without this shared foundation, semantic communication breaks down.

From a theoretical perspective, modelling meaning mathematically remains a complex challenge. We need to move beyond bits to quantify and encode “meaning” in ways that are both efficient and reliable.

The dependence on advanced AI also presents practical challenges. Semantic communication requires deep integration with natural language processing, reasoning models, and adaptive learning technologies that are still evolving rapidly.

Finally, standardization poses a significant obstacle. Our current network protocols simply weren’t built for semantic intent exchange, requiring substantial rethinking of our fundamental approaches.

In the first phase, Awareness & Modelling, we need to define semantic entropy, capacity, and metrics while developing proof-of-concept systems in research settings. This foundational work should include embedding semantic layers into AI-enhanced protocols, establishing the technical groundwork for what follows.

The second phase, Prototyping in 6G Environments, involves integrating semantic communication with URLLC and mMTC (massive Machine Type Communications). We should test these integrations with Digital Twin networks and edge AI, while simultaneously establishing pre-standardization working groups to ensure alignment across the industry.

The final phase, Ecosystem Integration & Commercialization, will require embedding semantic modules into chipsets and network functions, deploying them in smart cities, Industry 4.0 environments, and immersive media applications. Standardization through bodies like 3GPP and ITU will be crucial during this phase to ensure global interoperability.

This journey toward semantic communication isn’t just a technical evolution; it’s a reimagining of how networks understand and transmit meaning. The challenges are substantial, but the potential rewards in efficiency, intelligence, and new capabilities make this one of the most exciting frontiers in telecommunications.

This blog post was written by Amr Ashraf, Product Architect and Support Director at Digis Squared.

The Evolution of Self-Organizing Networks: From SON to Cognitive SON to LTMs

Posted on by Mira Abdulrahman

As we approach 2030, the telecommunications industry is at a point where traditional network automation methods are merging with advanced AI technologies. Based on my experience over the past decade with network optimization solutions, I would like to share some insights on potential future developments.

Two Perspectives on SON Evolution

When discussing the future of Self-Organizing Networks (SON), it’s crucial to distinguish between two perspectives:

SON as a Conceptual Framework

The fundamental principles of self-configuration, self-optimization, and self-healing will remain essential to network operations. These core concepts represent the industry’s north star – autonomous networks that can deploy, optimize, and repair themselves with minimal human intervention.

These principles aren’t going away. Rather, they’re being enhanced and reimagined through more sophisticated AI approaches.

Vendor-Specific SON Implementations

The feature-based SON solutions we’ve grown familiar with – ANR (Automatic Neighbour Relations), CCO (Coverage & Capacity Optimization), MLB (Mobility Load Balancing), and others – are likely to undergo significant transformation or potential replacement.

These siloed, rule-based features operate with limited contextual awareness and struggle to optimize for multiple objectives simultaneously. They represent the first generation of network automation that’s ripe for disruption.

Enter Large Telecom Models (LTMs)

The emergence of Large Telecom Models (LTMs) – specialized AI models trained specifically on telecom network data – represents a paradigm shift in how we approach network intelligence.

Like how Large Language Models revolutionized natural language processing, LTMs are poised to transform network operations by:

  1. Providing holistic, cross-domain optimization instead of siloed feature-specific approaches
  2. Enabling truly autonomous decision-making based on comprehensive network understanding
  3. Adapting dynamically to changing conditions without explicit programming
  4. Learning continuously from network performance data

The Path Forward: Integration, or Replacement?

The relationship between traditional SON, Cognitive SON, and emerging LTMs is best seen as evolutionary rather than revolutionary.

  • Near-term (1-2 years): LTMs will complement existing SON features, enhance their capabilities while learn from operational patterns
  • Mid-term (3-4 years): We’ll see the emergence of agentic AI systems that can orchestrate multiple network functions autonomously
  • Long-term (5+ years): Many vendor-specific SON implementations will likely be replaced by more sophisticated LTM-driven systems

The most successful operators will be those who embrace this transition strategically – leveraging the proven reliability of existing SON for critical functions while gradually adopting LTM capabilities for more complex, multi-domain challenges.

Real-World Progress

We’re already seeing this evolution in action. SoftBank recently developed a foundational LTM that automatically reconfigures networks during mass events.

These early implementations hint at the tremendous potential ahead as we move toward truly intelligent, autonomous networks.

Prepared By: Abdelrahman Fady | CTO | Digis Squared

NWDAF: How 5G is AI Native by Essence

Posted on by Mira Abdulrahman

The evolution of telecommunications networks has always been characterized by increasing complexity and intelligence. As we’ve moved through successive generations of wireless technology, I’ve observed a consistent trend toward more adaptive, responsive systems. With 5G, this evolution has reached a critical inflection point by introducing the Network Data Analytics Function (NWDAF) a component that fundamentally transforms how networks operate and adapt.

NWDAF, introduced in the 5G Core architecture starting from Release 15 and continuing to evolve toward 6G, represents a pivotal element in the Service-Based Architecture (SBA). More than just another network component, it embodies a philosophical shift toward data-driven, intelligent network operations that anticipate the needs of both users and applications.

At its core, NWDAF serves as a standardized network function that provides analytics services to other network functions, applications, and external consumers. Its functionality spans the entire analytics lifecycle: collecting data from various network functions (including AMF, SMF, PCF, and NEF), processing and analyzing that data, generating actionable insights and predictions, and feeding decisions back into the network for optimization and policy enforcement.

I often describe NWDAF as the “central intelligence of the network”—a system that transforms raw operational data into practical insights that drive network behavior. This transformation is not merely incremental; it represents a fundamental reimagining of how networks function.

The necessity for NWDAF becomes apparent when we consider the demands placed on modern networks. Autonomous networks require closed-loop automation for self-healing and self-optimization—capabilities that depend on the analytical insights NWDAF provides. Quality of Service assurance increasingly relies on the ability to predict congestion, session drops, or mobility issues before they impact user experience. Network slicing, a cornerstone of 5G architecture, depends on real-time monitoring and optimization of slice performance. Security analytics benefit from NWDAF’s ability to detect anomalies or attacks through traffic behavior pattern analysis. Furthermore, NWDAF’s flexible deployment model allows it to operate in either central cloud environments or Multi-access Edge Computing (MEC) nodes, enabling localized decision-making where appropriate.

The integration of NWDAF with other network functions occurs through well-defined interfaces. The Np interface facilitates data collection from various network functions. The Na interface enables NWDAF to provide analytics to consumers. The Nnef interface supports interaction with the Network Exposure Function, while the Naf interface enables communication with Application Functions. This comprehensive integration ensures that NWDAF can both gather the data it needs and distribute its insights effectively throughout the network.

The analytical capabilities of NWDAF span multiple dimensions. Descriptive analytics provide visibility into current network conditions, including load metrics, session statistics, and mobility patterns. Predictive analytics enable the network to anticipate issues before they occur, such as congestion prediction, user experience degradation forecasts, and mobility failure prediction. Looking forward, prescriptive analytics will eventually allow NWDAF to suggest automated actions, such as traffic rerouting or slice reconfiguration, further enhancing network autonomy.

As we look toward 6G, NWDAF is poised to evolve into an even more sophisticated component of network architecture. I anticipate the development of an AI/ML-native architecture where NWDAF evolves into a Distributed Intelligence Function. Federated learning approaches will enable cross-domain learning without requiring central data sharing, addressing privacy and efficiency concerns. Integration with digital twin technology will allow simulated networks to feed NWDAF with predictive insights, enhancing planning and optimization. Perhaps most significantly, NWDAF will increasingly support intent-based networking, where user intentions are translated directly into network behavior without requiring detailed technical specifications.

The journey toward truly intelligent networks is just beginning, and NWDAF represents a crucial step in that evolution. By embedding analytics and intelligence directly into the network architecture, 5G has laid the groundwork for networks that don’t just connect—they understand, anticipate, and adapt. This foundation will prove essential as we continue to build toward the even more demanding requirements of 6G and beyond.

Prepared By: Amr Ashraf | Head of Solution Architect and R&D | Digis Squared

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ACES NH & DIGIS Squared Partnership Milestone

Posted on by Mira Abdulrahman

We are proud to announce the successful delivery and deployment of DIGIS Squared’s advanced cloud native testing and assurance solution, INOS, to ACES NH, the leading telecom infrastructure provider and neutral host in the Kingdom of Saudi Arabia.

As part of this strategic partnership, DIGIS Squared has delivered:

  • INOS Lite Kits for 5G Standalone (5GSA) testing and IBS testing.
  • INOS Watcher Kits for field / Service assurance.  
  • Full deployment of the INOS Platform over ACES NH cloud hosted inside the Kingdom, ensuring data localization and privacy compliance.

The ACES NH team is now leveraging INOS across all testing and assurance operations, with:

  • Comprehensive, detailed telecom network field KPIs & Service KPIs.
  • Auto RCA for field detected issues.
  • Full automation of testing and reporting workflows, that enables higher testing volumes in shorter timeframes.
  • AI-powered modules for virtual testing and predictive assurance.
  • A flexible licensing model that enables the support of all technologies.

This partnership highlights both companies’ shared vision of strengthening local capabilities and equipping ACES NH with deeper network performance insights—supporting their mission to provide top-tier services, in line with Saudi Arabia’s Vision 2030.

We look forward to continued collaboration and delivering greater value to the Kingdom’s digital infrastructure.

About ACES NH:

ACE NH, a Digital infrastructure Neutral Host licensed by CST in Saudi Arabia and DoT in India. ACES NH provide In-Building Solutions, Wi-Fi-DAS, Fiber Optics, Data Centers and Managed Services. We at ACES NH design, build, manage and enables Telecom-Operators, Airports, Metros, Railways, Smart & Safe Cities, MEGA projects. With its operations footprint in countries from ASIA, Europe, APAC, GCC and North-Africa with diverse projects portfolio and with focus on futuristic ICT technologies like Small-cells, ORAN, Cloud-Computing. ACES NH is serving nearly 2 billion worldwide annual users.

Optimizing LTE 450MHz Networks with INOS 

Posted on by Mira Abdulrahman

Introduction 

The demand for reliable, high-coverage wireless communication is increasing, particularly for mission-critical applications, rural connectivity, and industrial deployments. LTE 450MHz (Band 31) is an excellent solution due to its superior propagation characteristics, providing extensive coverage with fewer base stations. However, the availability of compatible commercial handsets remains limited, creating challenges for operators and network engineers in testing and optimizing LTE 450MHz deployments. 

To overcome these challenges, DIGIS Squared is leveraging its advanced network testing tool, INOS, integrated with ruggedized testing devices such as the RugGear RG760. This article explores how INOS enables efficient testing, optimization, and deployment of LTE 450MHz networks without relying on traditional consumer handsets. 

The Challenge of LTE 450MHz Testing 

LTE 450MHz is an essential frequency band for sectors such as utilities, public safety, and IoT applications. The band’s key advantages include: 

  • Longer range: Due to its low frequency, LTE 450MHz signals propagate further, covering large geographical areas with minimal infrastructure. 
  • Better penetration: It ensures superior indoor and underground coverage, crucial for industrial sites and emergency services. 
  • Low network congestion: Given its niche application, LTE 450MHz networks often experience less congestion than conventional LTE bands. 

However, network operators and service providers face significant hurdles in testing and optimizing LTE 450MHz due to the lack of commercially available handsets supporting Band 31. Traditional methods of network optimization rely on consumer devices, which are not widely available for this band. 

Introducing INOS: A Comprehensive Drive Test Solution 

INOS is a state-of-the-art, vendor-agnostic network testing and optimization tool developed by DIGIS Squared. It allows operators to: 

  • Conduct extensive drive tests and walk tests with real-time data collection. 
  • Analyze Key Performance Indicators (KPIs) such as RSRP, RSRQ, SINR, throughput, and latency. 
  • Evaluate handover performance, coverage gaps, and network interference. 
  • Benchmark networks across multiple operators. 
  • Generate comprehensive reports with actionable insights for optimization. 

INOS eliminates the dependency on consumer devices, making it an ideal solution for LTE 450MHz testing. 

How INOS Enhances LTE 450MHz Testing 

1. Seamless Data Collection 

INOS allows seamless data collection for LTE 450MHz performance analysis. Engineers can conduct extensive tests using professional-grade testing devices like the RugGear RG760. 

2. Comprehensive Performance Monitoring 

INOS enables engineers to monitor key LTE 450MHz performance metrics, including: 

  • Signal strength and quality (RSRP, RSRQ, SINR). 
  • Throughput measurements for downlink and uplink speeds. 
  • Handover success rates and network transitions. 
  • Coverage mapping with real-time GPS tracking. 

3. Efficient Deployment & Troubleshooting 

Using INOS streamlines the LTE 450MHz deployment process by: 

  • Identifying weak coverage areas before commercial rollout. 
  • Troubleshooting network performance issues in real-time. 
  • Validating base station configurations and antenna alignments. 

4. Cost-Effective & Scalable Testing 

By using INOS instead of expensive proprietary testing hardware, operators can achieve a cost-effective and scalable testing framework. 

Real-World Applications 

1. Private LTE Networks 

Organizations deploying private LTE networks in critical industries (e.g., mining, utilities, emergency services) can use INOS to ensure optimal network performance and coverage. 

2. Smart Grids & Utilities 

With LTE 450MHz playing a key role in smart grids and utilities, INOS facilitates efficient network optimization, ensuring stable communication between smart meters and control centers. 

3. Public Safety & Emergency Response 

For first responders relying on LTE 450MHz for mission-critical communications, INOS ensures that networks meet the required service quality and reliability standards. 

4. Rural & Remote Connectivity 

Operators extending connectivity to underserved areas can leverage INOS to validate coverage, optimize handovers, and enhance user experience. 

Conclusion 

Testing and optimizing LTE 450MHz networks have historically been challenging due to the limited availability of compatible handsets. By leveraging the powerful capabilities of INOS, DIGIS Squared provides a cutting-edge solution for network operators to efficiently deploy and maintain LTE 450MHz networks. 

With INOS, operators can conduct extensive drive tests, analyze network KPIs, and troubleshoot issues in real-time, ensuring seamless connectivity for industries relying on LTE 450MHz. As the demand for private LTE networks grows, INOS represents a game-changer in network testing and optimization. 

For more information on how INOS can enhance your LTE 450MHz deployment, contact DIGIS Squared today! 

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This blog post was written by Amr Ashraf, Product Architect and Support Director at Digis Squared. With extensive experience in telecom solutions and AI-driven technologies, Amr plays a key role in developing and optimizing our innovative products to enhance network performance and operational efficiency.

Why Service Providers Should Go Vendor-Agnostic?

Posted on by Mira Abdulrahman

Being a vendor-agnostic managed services provider (MSP) offers several strategic advantages, particularly in today’s diverse and rapidly changing technology landscape. Here are some key benefits:

1. Flexibility and Customization for Clients

  • Tailored Solutions: Vendor-agnostic MSPs aren’t bound to specific hardware or software brands, allowing them to provide tailored solutions that best meet each client’s unique needs.
  • Seamless Integration: This approach allows MSPs to integrate diverse technologies, which is especially beneficial for clients with existing systems from various vendors. It ensures compatibility across different platforms and systems.

2. Improved Trust and Objectivity

  • Unbiased Recommendations: Without vendor affiliations, MSPs can provide impartial advice focused solely on the client’s business goals rather than pushing products from specific vendors.
  • Enhanced Credibility: Clients often see vendor-agnostic MSPs as more credible partners, as they know recommendations are based purely on quality and suitability, not vendor relationships.

3. Access to Best-of-Breed Technology

  • Greater Variety of Options: Vendor-agnostic MSPs have access to a broad spectrum of technologies, enabling them to choose the best-in-class products for any given solution.
  • Rapid Adaptation to Industry Trends: They can quickly adopt new and emerging technologies, providing clients with up-to-date solutions without being locked into a single vendor’s product lifecycle.

4. Reduced Vendor Lock-In Risks

  • Enhanced Flexibility for Clients: By working with a vendor-agnostic MSP, clients avoid becoming dependent on a single vendor, which reduces risks associated with vendor-specific limitations, such as pricing changes or service discontinuation.
  • Easier Transition and Upgrades: Clients can transition to new technology or upgrade their systems without having to overhaul their entire infrastructure, preserving both continuity and cost efficiency.

5. Broader Industry Knowledge and Expertise

  • Cross-Vendor Knowledge: A vendor-agnostic MSP is typically skilled in managing and troubleshooting a wide range of technologies, offering clients a broader knowledge base and deeper expertise.
  • Continuous Skill Development: MSPs that work with multiple vendors stay current across different technologies, tools, and standards, ensuring that they bring industry-wide best practices to each engagement.

6. Enhanced Scalability and Future-Proofing

  • Adaptable Scaling Options: Vendor-agnostic MSPs can scale services up or down, choosing the most effective tools and vendors for each stage of growth, enabling clients to expand or streamline without limits.
  • Future-Proof Solutions: Without a commitment to specific vendors, MSPs can more readily integrate cutting-edge technologies as they emerge, helping clients future-proof their operations and remain competitive.

7. Cost Savings for Clients

  • Optimized Pricing Structures: Vendor-agnostic MSPs can select the most cost-effective solutions for each situation, maximizing value without unnecessary expenses tied to specific vendor pricing models.
  • Elimination of Unnecessary Licensing Fees: By evaluating multiple vendor options, they can choose solutions that reduce or eliminate redundant licensing costs, allowing clients to optimize their budgets.

8. Enhanced Service Continuity and Reliability

  • Improved Vendor Alternatives: In case of vendor issues or service interruptions, vendor-agnostic MSPs can provide alternative solutions more easily, maintaining continuity without significant disruption.
  • Better Risk Mitigation: By using multiple vendor solutions, MSPs can create redundancies and implement failover options, reducing the impact of any single vendor failure.

Summary

A vendor-agnostic MSP can offer unbiased, flexible, and future-proof solutions, giving clients greater control over their technology stack while maximizing cost-efficiency and operational resilience. This approach builds trust, meets diverse client needs, and provides a competitive edge by adapting to market changes and emerging technology with agility.

Author: Ahmed Zein, Digis Squared’s COO, and expert in Managed Services excellence and Operations.

Cross-Sector Detection

Posted on by Mira Abdulrahman

In today’s fast-paced telecom industry, delivering optimal network performance is essential to ensuring seamless user experiences. One significant challenge operators face is cross-sector and other issues that are affecting the overall performance of the network these issues may be related to Antenna configurations, this type of issues includes but is not limited to, wrong or shifted azimuths and other wrong configurations, or maybe hardware problems that cause down sectors. At Digis Squared, we’ve taken a bold step forward by developing an advanced AI-based algorithm that detects these kinds of issues using data gathered from drive tests in no time compared with the traditional ways. This cutting-edge solution promises to significantly reduce the time it takes to improve network performance and streamline operational costs.

Understanding Cross-Sector Problem

The cross-sector problem occurs when a mobile device connects to a sector of a cell tower that is not intended to serve its location. This typically happens due to antenna misalignment, hardware problem, or wrong configuration. As a result, the device experiences degraded performance such as signal interference, increased latency, or reduced data throughput. Additionally, the network resources of the unintended sector may be strained, impacting overall efficiency. Resolving this issue is essential for improving coverage availability and enhancing user experience in mobile networks.

Why do we need such an algorithm?

The detection of cross-sector and other problems currently requires a lot of resources (time, skilled engineers, and for sure that costs a lot of money), it may take multiple hours or days for a team to be able to investigate a drive test from one cluster, and this time is proportional to the size and complexity of the network and the surrounding environment.

In addition to that, operators are trying to solve these issues as fast as possible because by solving such issues the operators can ensure solving their consequences like:

  • Network Congestion: Too many users connected to a single sector can cause overloading, reducing data speeds and overall network performance.
  • Interference: Cross-sector interference happens when neighboring sectors overlap in coverage, causing signal degradation.
  • Inefficient Resource Use: If users are connected to a less optimal sector, network resources such as bandwidth and power are not used efficiently.

Our tool aims to ensure fast and accurate detection and reporting of cross-sector and other issues to accelerate solving related network problems to enable the users to receive the best quality of service and use the network resources.

The solution:

At Digis Squared, we have developed a novel AI-based algorithm specifically designed to detect issues that we have mentioned earlier by analyzing data collected from drive tests. This algorithm leverages AI, advanced signal processing techniques, and fast processing and analytics to automatically identify when a device is connected to a suboptimal sector.

in less than a few minutes, you can have an accurate and comprehensive report about the cross-sector and other issues found in the network.

Benefits for Telecom Operators

  • Improved Network Performance: By accurately detecting and resolving these issues, operators can enhance network efficiency and provide a better user experience by minimizing interference and improving data throughput.
  • Cost Efficiency: Automating the detection of cross-sector and other problems reduces the need for manual analysis and network intervention, which can significantly lower operational expenses (OPEX).
  • Faster Optimization: With the ability to process data and generate insights with that speed, operators can implement network changes more rapidly, ensuring that the network performs optimally at all times.

Conclusion

At Digis Squared, we are committed to pushing the boundaries of network optimization technology. Our algorithm for antenna issues detection represents a major leap forward in network management, offering telecom operators a more efficient, automated, and accurate method for resolving issues and ensuring a better user experience. By harnessing AI, and multi-metric analysis, we are enabling smarter, more resilient networks that are ready to meet the demands of the future.

Stay tuned for more updates on how this algorithm is transforming networks around the globe.

INOS VMOS Assessment Tool: Redefining Video Quality Assessment for OTT Video

Posted on by Mira Abdulrahman

The INOS Video Mean Opinion Score (VMOS) Assessment Tool represents a groundbreaking advancement in evaluating both User Quality-of-Experience (QoE) and Network Quality of Service (QoS) for adaptive video streaming on Facebook. By seamlessly merging these critical aspects, the tool delivers unparalleled benchmarking and optimization capabilities. Built upon an innovative architecture, it integrates high-performance analysis with a user-centric design, ensuring top-notch video quality evaluation across various platforms. Specifically designed for mobile phone testing, the VMOS Assessment Tool integrates seamlessly from the client side, making it ideal for efficient evaluation of mobile video performance.

Features:

Real-Time Analysis at Unprecedented Speed: Experience instantaneous, precise assessments with our tool’s advanced algorithms, ensuring rapid feedback and swift resolution of performance issues.

Enhanced QoE with ITU-T P.1204.3 Compliance: Aligned with the latest ITU-T P.1204.3 standards, the VMOS Assessment Tool offers refined evaluations that adhere to the most current benchmarks for perceptual video quality.

High-Quality Database Integration: Support for up to 8K resolution and 60 frames per second ensures comprehensive analysis of high-definition video content, enabling optimal performance and clarity.

Network QoS Optimization: Improve video playback with our tool’s focus on optimizing start-delay and buffering frequency, leading to smoother viewing experiences.

Integrated QoE and QoS Evaluation: The VMOS Assessment Tool seamlessly combines QoE and QoS metrics, providing a holistic analysis that ensures both user experience and network performance are optimized for superior video quality.

Flexible Device Compatibility and Viewing Distance: The VMOS Assessment Tool is designed to adapt to different streaming device dimensions, including PC, laptop, and mobile phone, and various viewing distances, ensuring optimal video quality regardless of the device or viewing conditions.

Seamless Platform Integration: Designed for effortless compatibility, the VMOS Assessment Tool integrates smoothly with existing video platforms, ensuring a hassle-free transition and minimal operational disruption.

Zero Client-Side Integration Required: The VMOS Assessment Tool manages the entire process, from video playback and network statistics recording to the final MOS score assessment, eliminating the need for any client-side integration.

Architecture Overview:

The INOS VMOS Assessment Tool encompasses multiple stages. Initially, it interacts with the video platform to obtain various encoded files, which are transmitted to the user network based on bandwidth availability. Subsequently, in the packet capturing phase, network packets are recorded into a PCAP file, along with the corresponding SSL decryption log key. During the packets processing phase, network packets are filtered to isolate only those related to video playback and player events. The final stage involves predicting the VMOS score by integrating video playback quality fluctuations, which reflect user QoS, with player events, which indicate network QoS.

INOS Facebook VQA Output Sample:

These output samples are derived from our Facebook quality testing on a mobile network operator in the United Kingdom. The results display a range of evaluation metrics utilized for the final VMOS assessment. Each performance metric is accompanied by geospatial testing locations on the map, time-domain values, and histogram values. The performance metrics will be discussed in the following points:

  1. Facebook Streaming Success:

This metric measures the success rate of logging into Facebook and streaming the video.

  • Facebook Streaming Start Delay:

This metric measures the time interval between the initiation of video loading and the commencement of video playback.

  • Facebook Streaming Buffer VMOS: 

This metric assesses the Network QoS VMOS, estimated from platform player events such as start delay, rebuffering event frequency, and rebuffering event duration relative to the original video duration.

  • Facebook Streaming Resolution per Second: 

This metric indicates the video playback resolutions per second, highlighting that Facebook frequently reduces the resolution to 540 pixels for mobile users.

This metric reflects the quality VMOS of video playback per second as a result of video quality fluctuations.

  • Facebook Streaming Quality VMOS:

This metric assesses the User QoE VMOS, indicating the Quality VMOS for the entire playback sequence, calculated from the Quality VMOS per second.

  • Facebook Streaming Final VMOS:

This metric represents the final VMOS score by merging both Network QoS and User QoE into a single score that encapsulates the overall experience.

INOS Tool Summary:

  • The INOS VMOS Assessment Tool is a Comprehensive Video Quality Evaluation tool for adaptive video streaming on Facebook, ensuring optimized user experience and network performance.
  • The Tool Features Innovative System Architecture by processing stages from obtaining encoded files, capturing and filtering network packets, to predicting the VMOS score.
  • The Tool Offers Advanced Real-Time Analysis with instantaneous, precise assessments and support for high-definition video content up to 8K resolution and 60 frames per second.
  • The Tool Provides Seamless Client-Side Integration for Mobile Testing, requiring no client-side integration and adapting to various device dimensions and viewing distances for efficient evaluation of mobile video performance.
  • The Tool Produces Detailed Output Samples for Comprehensive Evaluation.
  • The Tool Ensures Compatibility with Other Video Platforms, including YouTube, Shahid, TikTok, and Instagram.

We would like to extend our sincere thanks to Obeidallah Ali, our R&D Director at Digis Squared, for his invaluable contribution to this white paper. His expertise and insights have been instrumental in shaping this content and ensuring its relevance!