Author: Mira Abdulrahman

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.

Mobile Private Network

Posted on by Mira Abdulrahman

Private networks are dedicated communication networks built for a specific organization or use case

Benefits

  • Enhanced security and data privacy
  • Improved network performance and reliability
  • Customized coverage and capacity
  • Integration with existing systems and infrastructure

A private (mobile) network is where network infrastructure is used exclusively by devices authorized by the end-user organization.

Typically, this infrastructure is deployed in one or more specific locations which are owned or occupied by the end-user organization.

Devices that are registered on public mobile networks will not work on the private network except where specifically authorized.

Formally these are known as ‘non-public networks’ however the term private network is more commonly used across vertical industries.

Drivers of having a 5G Private network

Network Performance: with eMBB, URLLC and MMTC, 5G is very capable in terms of network performance

5G Security: The fifth generation of networks is more secure than the 4G LTE network because it has identity management, privacy, and security assurance

New Spectrum in 5G: availability of shared and dedicated 5G spectrum in several bands

Network Coverage: With 5G network, you control where to deploy your gNB

Private Networks Deployment Models

SNPN, Standalone Non-Public Network

NPN is deployed as an independent, standalone network

Private company has exclusive responsibility for operating the NPN and for all service attributes

The only communication path between the NPN and the public network can be done optionally via a firewall

standalone network. Under this deployment model, all network functions are located within the facility where the network operates, including the radio access network (RAN) and control plane elements. Standalone, isolated private networks would typically use dedicated spectrum (licensed or unlicensed) purchased through a mobile network operator (MNO) or, in some cases, directly from government agencies.

PNI-NPN: Public Network Integrated – Non Public Network

  • NPN deployed with MNO support: hosted completely or partially on public network infrastructure
  • e.g. using Network Slicing
  • PNI-NPN has different variants we are going to explain some of them in the coming section

PNI-NPN: Deployment with shared RAN

Shared RAN with dedicated Core

NPN and the public network share part of the radio access network, while other network functions remain separated.

This scenario involves an NPN sharing a radio-access network (RAN) with the service provider. Under this scenario, control plane elements and other network functions physically reside at the NPN site.

This type of deployment enables local routing of network traffic within the NPN’s physical premises, while data bound for outside premises is routed to the service provider’s network. 3GPP has specifications that cover network sharing. (A variation of this deployment scenario involves the NPN sharing both the RAN and control plane functions, but with the NPN traffic remaining on the site where the NPN is located and not flowing out to the public network.)

PNI-NPN: Deployment with shared RAN and Control Plane

Shared RAN and core control Plane.

Both RAN and Core Sharing from control side, with the RAN and Core elements managed by the Public 5G network.

NPN only handles user plane connectivity.

This scenario involves an NPN sharing a radio-access network (RAN) with the service provider. Under this scenario, control plane elements and other network functions physically reside at the NPN site”

PNI-NPN: NPN Deployment in public network

5G Public-Private Network Slice

NPN hosted by the public network

Complete outsourcing of the network, where devices on the private network utilize the Public 5G network RAN.

This scenario can be implemented by means of network slicing

The third primary type of NPN deployment is where the NPN is hosted directly on a public network. In this type of deployment, both the public network and private network traffic are located off-site.”

Through virtualization of network functions and in a technique known as network slicing, the public-network operator of the private network partitions between the public network and the NPN, keeping them completely separate.

Challenges of Private Network

Spectrum and Regulations

Limited Spectrum Options: Securing suitable spectrum can be challenging, especially in densely populated or highly regulated regions where spectrum allocation is scarce.

Regulatory Hurdles: Navigating complex regulatory environments to acquire spectrum licenses can be time-consuming and costly, often requiring compliance with specific national or regional regulations.

High Initial Cost

Infrastructure Investment: Setting up a private network requires substantial upfront investment in infrastructure such as base stations, antennas, and network equipment.

Operational Expenses: Beyond initial setup, ongoing operational costs include maintenance, upgrades, and personnel training, contributing to the overall cost burden.

Knowledge acquisition or outsourcing

Technical Expertise: Establishing and maintaining a private network demands specialized knowledge in network design, integration, security, and optimization.

Outsourcing Challenges: Depending on internal resources versus outsourcing, finding capable vendors or partners with expertise in private network implementation can be challenging, affecting project timelines and quality.

Availability and Scope

Geographical Coverage: Ensuring adequate coverage across the desired operational area without compromising signal strength or reliability can be complex, particularly in challenging terrains or remote locations.

Scalability: Designing networks that can scale effectively as operational needs grow, without sacrificing performance or security, requires careful planning and sometimes iterative adjustments.

Integration with Existing IT/OT Systems

Legacy Systems: Many enterprises operate legacy operational technology (OT) systems that aren’t designed to interface with IP-based private networks.

Interoperability Issues: Ensuring seamless integration between IT/OT systems, existing network infrastructure, and the new private network requires careful system design and often bespoke solutions.

Data Flow & Security Consistency: Synchronizing real-time data and maintaining consistent security policies across heterogeneous systems can be complex.

Return on Investment (ROI) and Business Justification

Unclear Business Models: Enterprises often struggle to quantify the ROI of private networks, especially when benefits like reliability and security are intangible.

Cost vs. Benefit Uncertainty: Without clear use cases (e.g., predictive maintenance, robotics, digital twin), the business case can remain weak, delaying decision-making.

Our Private Networks SI Capabilities

Digis Squared provides Vendor Management & control, operator mindset, helicopter view, program governance, wide experience, class-efficient network solutions & design

We at Digis Squared provide E2E Private Network SI and managed Services journey that could be described as following  

This blog post was written by Obeidallah Ali, R&D Director at Digis Squared.

Revolutionizing Indoor Network Testing with INOS: A Deep Dive into the Enhanced Indoor Kit

Posted on by Mira Abdulrahman

Introduction

As mobile networks continue to evolve with 5G, ensuring optimal indoor connectivity is more critical than ever. INOS (Indoor Network Optimization Solution) is redefining how operators and engineers approach indoor testing with its advanced tools, robust features, and a newly upgraded Indoor Kit. Designed to tackle the unique challenges of indoor environments, the INOS Indoor Kit offers significant improvements in software, hardware, and overall functionality to deliver superior usability, reliability, and results.

The Importance of Indoor Testing

Indoor spaces like malls, airports, and office buildings pose unique challenges for network optimization due to:

  • Architectural complexity: Thick walls and multiple floors impede signal propagation.
  • User density: Crowded environments generate high network demand.
  • Interference: Co-channel interference can degrade signal quality.

These challenges make precise and efficient indoor network testing crucial for delivering seamless connectivity.

Enhancements in the INOS Indoor Kit

Software Improvements (Icons)

  1. Revamped User Interface (UI):
    The new UI offers an intuitive design for enhanced accessibility, streamlining control, and monitoring processes for users.
  2. Enhanced Connectivity Options:
    Supporting Internet, WLAN, and Bluetooth connections, the kit provides robust and flexible inter-device connectivity.
  3. Comprehensive Control Capabilities:
    The tablet serves as a central hub, allowing users to control every connected device and monitor KPIs directly.
  4. Centralized Alarm Notifications:
    Alarm notifications from all connected devices are displayed on the tablet in real-time, enabling prompt troubleshooting.

Hardware Upgrades

  1. Ergonomic and Lightweight Design:
    A portable, lighter design ensures ease of use in various indoor scenarios.
  2. Extended Battery Life:
    Powering up to 12 devices for 8 hours of continuous operation, the kit supports long-duration tasks without frequent recharging.
  3. Smart Cooling System:
    An intelligent cooling mechanism activates based on system temperature, ensuring consistent performance without overheating.

Key Features and Differentiators

The INOS Indoor Kit offers several standout features that set it apart from competitors:

  1. 5G Support Across All Devices:
    Fully optimized for 5G testing, supporting all devices within the kit to handle the latest network demands.
  2. Tablet as a Centralized Display:
    Displays real-time radio KPIs, with intuitive visualizations and insights for quick decision-making.
  3. Advanced Device Management via Tablet:
    • Control multiple phones directly.
    • Color-coded indicators highlight synced devices, poor KPIs, and ongoing logfile recordings, allowing users to focus on critical areas.
  4. Support for Large Layout Images:
    Unlike competitors, INOS excels at handling and displaying large indoor layouts, ensuring no testing area is overlooked.
  5. Automated Processes:
    • Logfile Uploading and Collection: Eliminates manual intervention, saving time and effort.
    • Post-Processing Automation: Simplifies report generation and routine tasks that traditionally require manual copy-paste workflows.
  6. Comprehensive Support Model:
    INOS provides end-to-end support for all product aspects, ensuring users have the help they need at every stage.
  7. Expandable Kit Design:
    Offers the flexibility to add more devices, making it adaptable to different indoor testing scales.
  8. Enhanced Connectivity:
    INOS leverages Internet, WLAN, and Bluetooth for device control, overcoming the limitations of competitors who rely solely on Bluetooth (limited to 8 devices and prone to connectivity issues).

Why INOS Stands Out in Indoor Testing

INOS combines cutting-edge technology with user-centric design to deliver a superior indoor testing experience. With its latest enhancements, it ensures that telecom operators and network engineers have the tools they need to achieve:

  •  Unmatched Accuracy: Collect and analyze data with precision.
  • Greater Efficiency: Streamlined workflows and automation save time and effort.
  • Enhanced Portability: Lightweight design and extended battery life make it perfect for demanding indoor environments.

Conclusion

The INOS Indoor Kit, with its latest software and hardware upgrades, is a game-changer for indoor network optimization. By focusing on usability, functionality, and reliability, it empowers operators to tackle even the most challenging scenarios with confidence.

Ready to elevate your indoor testing? Discover how the enhanced INOS Indoor Kit can revolutionize your network optimization strategy.

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.

AI-Driven RAN: Transforming Network Operations for the Future

Posted on by Mira Abdulrahman

Challenges Facing Mobile Network Operators (MNOs)

As mobile networks evolve to support increasing data demand, Mobile Network Operators (MNOs) face several critical challenges:

1. Rising CAPEX Due to Network Expansions

With the rollout of 5G and upcoming 6G advancements, MNOs must invest heavily in network expansion, including:

  • Deploying new sites to enhance coverage and capacity.
  • Upgrading existing infrastructure to support new technologies.
  • Investing in advanced hardware, software, and spectrum licenses.

2. Growing Network Complexity

As networks integrate multiple generations of technology (2G, 3G, 4G, 5G, and soon 6G), managing this complexity becomes a major challenge. Key concerns include:

  • Optimizing the placement of new sites to maximize coverage and efficiency.
  • Choosing the right hardware, licenses, and features to balance performance and cost.
  • Ensuring seamless interworking between legacy and new network elements.

3. Increasing OPEX Due to Operations and Maintenance

Operational expenditures continue to rise due to:

  • The increasing number of managed services personnel and field engineers.
  • The complexity of maintaining multi-layer, multi-vendor networks.
  • The need for continuous network optimization to ensure service quality.
  • Rising Energy Costs: Powering an expanding network infrastructure requires substantial energy consumption, and increasing energy prices put further pressure on operational budgets. AI-driven solutions can optimize power usage, reduce waste, and shift energy consumption to off-peak times where feasible.

4. Competitive Pressures in Customer Experience & Network Quality

MNOs are not only competing on price and service offerings but also on:

  • Network Quality: Coverage, speed, and reliability.
  • Customer Experience: Personalized and high-quality connectivity.
  • Operational Efficiency: Cost-effective operations that enhance profitability.

The Concept of AI in RAN

To address these challenges, AI-driven Radio Access Networks (AI-RAN) emerge as a key enabler. AI-RAN leverages artificial intelligence and machine learning to:

  • Optimize network planning and resource allocation.
  • Automate operations, reducing manual interventions.
  • Enhance predictive maintenance to prevent failures before they occur.
  • Improve energy efficiency by dynamically adjusting power consumption based on traffic demand.

Different AI-RAN Methodologies

  1. AI and RAN
    • AI and RAN (also referred to as AI with RAN): using a common shared infrastructure to run both AI and workloads, with the goal to maximize utilization, lower Total Cost of Ownership (TCO) and generate new AI-driven revenue opportunities.
    • AI is used as an external tool for decision-making and analytics without direct integration into the RAN architecture.
    • Example: AI-driven network planning tools that assist in site selection and spectrum allocation.
  2. AI on RAN
    • AI on RAN: enabling AI services on RAN at the network edge to increase operational efficiency and offer new services to mobile users. This turns the RAN from a cost centre to a revenue source.
    • AI is embedded within the RAN system to enhance real-time decision-making.
    • Example: AI-powered self-optimizing networks (SON) that adjust parameters dynamically to improve network performance.
  3. AI for RAN
    • AI for RAN: advancing RAN capabilities through embedding AI/ML models, algorithms and neural networks into the radio signal processing layer to improve spectral efficiency, radio coverage, capacity and performance.
    • AI is leveraged to redesign RAN architecture for autonomous and intelligent network operations.
    • Example: AI-native Open RAN solutions that enable dynamic reconfiguration of network functions.

Source is NVidia AI-RAN: Artificial Intelligence – Radio Access Networks Document.

Organizations and Standardization Bodies Focusing on AI-RAN

Several industry bodies and alliances are driving AI adoption in RAN, including:

  • O-RAN Alliance: Developing AI-native Open RAN architectures.
  • 3GPP: Standardizing AI/ML applications in RAN.
  • ETSI (European Telecommunications Standards Institute): Working on AI-powered network automation.
  • ITU (International Telecommunication Union): AI for good to promote the AI use cases
  • GSMA: Promoting AI-driven innovations for future networks.
  • Global Telco AI Alliance: A collaboration among leading telecom operators to advance AI integration in network operations and RAN management.

AI-RAN Use Cases

  1. Intelligent Network Planning
    • AI-driven tools analyse coverage gaps and predict optimal site locations for new deployments.
    • Uses geospatial and traffic data to optimize CAPEX investments.
    • Improves network rollout efficiency by identifying areas with the highest potential return on investment.
  1. Automated Network Optimization
    • AI-powered SON dynamically adjusts network parameters.
    • Enhances performance by minimizing congestion and interference.
    • Predicts and mitigates traffic spikes in real-time, improving service stability.
  2. Predictive Maintenance
    • AI detects anomalies in hardware and predicts failures before they happen.
    • Uses machine learning models to analyze historical data and identify patterns leading to failures.
    • Reduces downtime and minimizes maintenance costs by enabling proactive issue resolution.
  3. Energy Efficiency Optimization
    • AI adjusts power consumption based on real-time traffic patterns.
    • Identifies opportunities for network elements to enter low-power modes during off-peak hours.
    • Leads to significant OPEX savings and a reduced carbon footprint by optimizing renewable energy integration.
  1. Enhanced Customer Experience Management
    • AI-driven analytics personalize network performance based on user behavior.
    • Predicts and prioritizes network resources for latency-sensitive applications like gaming and video streaming.
    • Uses AI-driven call quality analysis to detect and rectify issues before customers notice degradation.
    •  
  2. AI-Driven Interference Management
    • AI models analyze interference patterns and dynamically adjust power levels and beamforming strategies.
    • Reduces interference between cells and enhances spectral efficiency, especially in dense urban areas.
  3. Supply Chain and Inventory Optimization
    • AI helps predict hardware and component needs based on network demand forecasts.
    • Reduces overstocking and minimizes delays by ensuring the right components are available when needed.
  4. AI-Driven Beamforming Management
    • AI optimizes beamforming parameters to improve signal strength and reduce interference.
    • Dynamically adjusts beam directions based on real-time user movement and network conditions.
    • Enhances network coverage and capacity, particularly in urban and high-density environments.

Conclusion

AI is revolutionizing RAN by enhancing efficiency, reducing costs, and improving network performance. As AI adoption in RAN continues to grow, MNOs can expect increased automation, better customer experiences, and more cost-effective network operations. The journey toward AI-driven RAN is not just an evolution—it is a necessity for the future of mobile networks.

To further illustrate these advancements, incorporating graphs that highlight AI’s impact on OPEX reduction, predictive maintenance efficiency, and energy savings will help visualize the benefits AI brings to RAN operations.

Prepared By: Abdelrahman Fady | CTO | Digis Squared

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.

AI and Machine Learning Integration in 6G: DIGIS Squared’s Role in Shaping the Future

Posted on by Mira Abdulrahman

As the journey from 5G to 6G unfolds, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is not just a feature—it’s a game-changer for wireless networks. With 6G poised to redefine connectivity, DIGIS Squared is at the forefront, driving innovation to unlock the potential of AI-powered networks.

The Critical Role of AI in 6G

6G networks aim to deliver not just faster speeds but smarter and more adaptive communication. AI is the key enabler for these advancements, addressing the complexity of next-generation networks by providing:

  • Autonomous Optimization: AI enables networks to self-learn and adapt in real-time, ensuring optimal performance even under rapidly changing conditions.
  • Dynamic Spectrum Management: Efficient use of spectrum resources is critical in 6G. AI-driven algorithms analyze and allocate frequencies dynamically, maximizing capacity and minimizing interference.
  • User-Centric Experiences: Personalization will reach new heights as AI tailors network resources to individual user needs, supporting applications like AR, VR, and holographic communication.

DIGIS Squared’s Role

DIGIS Squared is leveraging its expertise in AI and telecommunications to pioneer innovative solutions for 6G networks. By integrating domain-specific AI models with advanced network infrastructure, DIGIS Squared is working on:

  • AI-Driven Network Automation: Developing tools to automate configuration, monitoring, and troubleshooting for future networks.
  • Predictive Analytics: Enhancing network reliability by predicting and addressing potential issues before they impact users.
  • Enhanced IoT Connectivity: Creating intelligent frameworks to manage the explosive growth of IoT devices seamlessly.

This commitment ensures DIGIS Squared remains a leader in the global 6G ecosystem.

New Horizons for AI-Integrated Networks

With AI at its core, 6G is set to unlock transformative use cases:

  • Holographic Telepresence: Imagine lifelike, three-dimensional communication that feels as real as being there in person.
  • Self-Healing Networks: AI will enable networks to diagnose and resolve issues autonomously, ensuring uninterrupted connectivity.
  • Sustainable Connectivity: Energy-efficient AI models will align with 6G’s goal of reducing environmental impact while delivering superior performance.

Challenges to Overcome

While the opportunities are vast, challenges remain. These include ensuring data privacy, developing energy-efficient AI models, and achieving global standardization. DIGIS Squared is addressing these challenges by collaborating with industry partners, contributing to standardization efforts, and innovating in sustainable AI-driven technologies.

The Future Awaits

The integration of AI in 6G is more than a technical evolution; it’s a revolution that will transform industries and everyday life. DIGIS Squared is proud to play a pivotal role in this transformation, shaping a smarter, more connected future for all.

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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.

Intelligent Reflecting Surfaces (IRS)

Posted on by Mira Abdulrahman

Paving the Way for 6G Connectivity. As we are only a few years away from the 6G era, one of the transformative technologies shaping the future of wireless communication is Intelligent Reflecting Surfaces (IRS). But what exactly is IRS, and why is it so critical for 6G? Let us dive in.
 
What is IRS?
An Intelligent Reflecting Surface is a planar structure composed of programmable, passive elements (often metasurfaces) that can reflect and manipulate electromagnetic waves. Unlike traditional antennas, IRS is not active device and doesn’t emit or amplify signals. Instead, it reconfigures the wireless environment by dynamically adjusting the phase, amplitude, and polarisation of reflected signals creating an optimized communication pathway between the transmitter(gNB) and receiver
(Handset).
In Real-World Context: Imagine IRS as a “smart mirror” for wireless signals, capable of bending and redirecting communication waves with unprecedented precision.
 
IRS Architecture
IRS typically consists of three key components:
• Metasurface: Comprising numerous sub-wavelength elements, each capable of independently tuning the reflected signal.
• Controller: A central unit that dynamically configures the metasurface based on real-time channel conditions.
• Communication Link: A connection to the base station or network orchestrating the IRS behaviour in response to the environment.
 
Key Advantages Of IRS in 6G:
1- Enhanced Signal Coverage: By intelligently reflecting signals, IRS helps overcome obstacles and dead zones in challenging environments.
2- Noise Mitigation: the reflectors work on noise suppression beside their work on signal amplification
3- Beamforming simplification: with IRS beamforming became much easier than before
4- Throughput improvement: as a direct result of coverage improvement, noise mitigation amd beamforming efficiency improvements the user data rates are significantly better than before.
5- Energy Efficiency: IRS is a passive system, significantly reducing power consumption compared to active communication devices.
6- Improved Spectral Efficiency: By dynamically steering signals, IRS enhances the overall system capacity.
7- Sustainability: Its low power usage aligns with the green communication goals of 6G.
8- CAPEX reduction : boosting the single site coverage will lead to less number of needed sites and consequently this will reduce the overall CAPEX of 6G deployment.

Now let’s see where we can deploy the IRS,
Infrastructure Deployment Locations:
– Buildings and Structures
– High-rise office complexes
– Shopping malls
– Hospitals and healthcare facilities
– Industrial campuses
– Data centers
– Smart city infrastructure

Aerial and Mobile Platforms
– Unmanned Aerial Vehicles (UAVs)
– Autonomous vehicles
– Public transportation systems
– Maritime vessels
– Satellite communication links

Urban and Environmental Contexts
– Streetlamp posts
– Traffic signal infrastructure
– Building facades
– Public transportation hubs
– Underground transit systems
– Bridges and overpasses

Specialized Deployment Zones
– Remote research stations
– Military and defense installations
– Emergency communication networks
– Disaster response infrastructure
– Agricultural monitoring systems
– Renewable energy monitoring sites
 
It is obviously clear that IRS deployment options are diversified and versatile now let’s discuss more the deployment considerations, here you are some Key Factors for IRS Placement:
1- Signal propagation characteristics
2- Environmental obstacles
3- Population density
4-Existing communication infrastructure
5-Cost-effectiveness of implementation
6- Long-term maintenance requirements

Use Cases of IRS
•Urban Connectivity, overcome obstacles in dense urban areas where signal blockage is common.
•Indoor Networks, Boost signal strength in offices, malls, and homes by managing reflections.
•IoT Application, Provide reliable connectivity to low-power IoT devices in complex environments.
•Smart Cities, Enable seamless connectivity for autonomous vehicles, drones, and smart infrastructure.
•Ubiquitous NTN coverage, extension of satellite D2C / D2D coverage and enhance the coverage provided by HAPs
•Terahertz Enablement, by boosting the coverage of extremely high frequency range signals IRS consider as a real enabler for terahertz connectivity.

While promising, IRS technologies are not without challenges:
1- Complex channel modeling requires advanced computational techniques

2- Initial deployment costs can be significant
3- Potential interference issues in dense multi-user environments
4- Ongoing research needed to optimize performance across varied scenarios
5- Mobility managment will be one of the big challenges of IRS deployment
6- Meticulous design and where exactly to deploy the IRS avoiding EHS issues
 
As we embrace 6G, IRS offers an exciting opportunity to reimagine wireless networks. By transforming passive environments into active contributors to communication, IRS isn’t just an enhancement—it’s a revolution.

A 2023 study by Nokia Bell Labs demonstrated IRS can improve network coverage by up to 40% in urban environments, showcasing its transformative potential.

RIS (reconfigurable intelligent surfaces) is an advanced modern form of IRS where in RIS we have the capability to dynamically change the phase and current of the propagated wave in sub-millisecond period

MIT Media Lab Research (2023) developed dynamic metasurface with sub-millisecond reconfiguration, created IRS capable of adapting to changing wireless environments in real-time, reduced energy consumption by up to 60% compared to traditional signal amplification methods.

Prepared By: Abdelrahman Fady | CTO | Digis Squared

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.