Category: Technologies

Diagnosing and Resolving “FAILURE_MSG4_CT_TIMER_EXPIRED” in 5G Standalone Networks

Posted on by Mira Abdulrahman

In the deployment and optimization of 5G Standalone (SA) networks, ensuring the robustness of the Random Access Channel (RACH) procedure is critical. DIGIS Squared identified and resolved a recurring RACH failure – “FAILURE_MSG4_CT_TIMER_EXPIRED” – during performance testing using our proprietary tools: INOS™ and Katana™. This white paper outlines the nature of the problem, diagnostic process, root cause analysis, and optimization strategies applied to restore optimal network performance.

Background

The 5G NR contention-based RACH procedure is essential for initial access, handover, and beam recovery. It involves a four-message handshake:

  1. Msg1: RACH preamble from UE
  2. Msg2: Random Access Response (RAR) from gNB
  3. Msg3: MAC CE or RRC message from UE
  4. Msg4: Contention resolution from gNB

The failure occurs when the UE sends Msg3 but does not receive Msg4 within the contention resolution timer window, resulting in an aborted RACH attempt.

Problem Identification

Failure Type: FAILURE_MSG4_CT_TIMER_EXPIRED
Detection Tool: INOS™ (field testing)
Confirmation Tool: Katana™ (OSS KPI analysis)

During extensive drive tests in both urban and suburban environments, INOS flagged multiple instances of RACH failure where Msg1, Msg2, and Msg3 were correctly transmitted, but Msg4 was not received. This was corroborated through Katana’s analysis of OSS counters, revealing high contention timer expiries in cells with:

  • Low SS-RSRP values (< -110 dBm)
  • High load and scheduling delays
  • Specific PRACH configurations

Root Cause Analysis

The following contributing factors were identified:

  • Msg3 Decoding Failures: UL signal degradation or beam misalignment prevented gNB from decoding Msg3.
  • Delayed Msg4 Scheduling: Resource contention at gNB delayed the contention resolution message.
  • Timer Misconfiguration: The default timer (sf64) was too short for specific TDD configurations.

Standards Reference:

  • 3GPP TS 38.331: RRC Protocol for NR
  • 3GPP TS 38.321: MAC Protocol for NR

Optimization Actions

Network-Side Adjustments

  • Increased ra-ContentionResolutionTimer from sf64 to sf128.
  • Reviewed and optimized PRACH Configuration Index and ZeroCorrelationZone settings.
  • Prioritized Msg4 scheduling at MAC layer in high-load scenarios.

Coverage Optimization

  • Fine-tuned beamforming and UL power control.
  • Extended PRACH monitoring duration in gNB firmware.

Post-Optimization Results

MetricBefore OptimizationAfter Optimization
RACH Success Rate89%97%
Msg4 Timer Expiry Rate11.8%<1.2%
Initial Access Latency (avg)440 ms260 ms
RRC Setup Drop RateModerateNear Zero

Verification was conducted using both INOS (field KPIs) and Katana (OSS trends), confirming significant improvement across all measured metrics.

Conclusion

This case study highlights the necessity of cross-layer observability in managing 5G SA network performance. By leveraging both real-time field data from INOS and OSS intelligence from Katana, DIGIS Squared successfully diagnosed and mitigated a complex RACH failure. The resolution not only improved RACH success rates but also enhanced user experience and access reliability.

Author: Obeidallah Ali, R&D Director at DIGIS Squared

Obeidallah Ali leads the Research & Development efforts at DIGIS Squared, driving innovation in AI-powered telecom solutions. With deep expertise in 5G Network Design, Optimization, and Automation, he focuses on developing tools like INOS™ and Katana™ that help operators diagnose, troubleshoot, and enhance network performance worldwide.

For inquiries, please contact:
Email: info@digis2.com

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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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

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!

Is the Customer Always Right?

Posted on by Mira Abdulrahman

Understanding the Dynamics Between System Integrators, Vendors, and Customers

The age-old adage, “The customer is always right,” has been a guiding principle in the world of business for decades. However, when it comes to the complex realm of system integration and vendor interactions, this notion may not always hold true. In this article, we delve into the delicate balance of power and decision-making between system integrators, vendors, and customers, and explore when it may be necessary to say no to a customer’s requests.

The Customer’s Perspective

Customers play a vital role in the success of any business endeavor. Their needs, requirements, and feedback shape the products and services offered by vendors and system integrators. Customers often come with specific expectations and demands, driven by their unique goals and priorities. The customer-centric approach emphasizes the importance of listening to the customer, understanding their requirements, and delivering solutions that meet or exceed their expectations.

The Role of System Integrators and Vendors

System integrators and vendors serve as the bridge between customers and technology solutions. They possess specialized knowledge, expertise, and resources to design, implement, and support complex systems and solutions. While their primary goal is to satisfy customer needs, system integrators and vendors also have a responsibility to deliver high-quality, reliable products and services that align with industry standards and best practices.

Saying No: When Should System Integrators and Vendors Push Back?

Despite the emphasis on customer satisfaction, there are instances where it may be necessary for system integrators and vendors to say no to a customer’s requests. Some common scenarios include:

  • 1. Technical Feasibility: If a customer requests a solution that is technically infeasible or goes against industry standards, system integrators and vendors may need to push back and propose alternative approaches.
  • 2. Scope Creep: Customers may often expand the scope of a project without considering the potential impact on timelines, resources, and budgets. In such cases, system integrators and vendors may need to set clear boundaries and manage customer expectations.
  • 3. Security and Compliance: In today’s digital landscape, cybersecurity and data privacy are top priorities. If a customer’s request poses security risks or non-compliance with regulations, system integrators, and vendors must prioritize safeguarding sensitive information.
  • 4. Resource Constraints: Customers may demand quick turnaround times or customized solutions that strain resources and impact the quality of deliverables. System integrators and vendors may need to communicate effectively with customers to manage expectations and maintain service standards.

Resolving the Dilemma: Strategies for Effective Communication and Collaboration

To navigate the challenges of balancing customer demands with technical limitations and industry standards, system integrators and vendors can adopt the following strategies:

  • 1. Open Communication: Establishing clear channels of communication with customers is crucial. System integrators and vendors should actively listen to customer requirements, provide transparent feedback, and collaborate on finding mutually beneficial solutions.
  • 2. Educating Customers: System integrators and vendors can educate customers on best practices, emerging technologies, and industry trends. By sharing expertise and insights, customers can make informed decisions that align with their long-term goals.
  • 3. Setting Expectations: From the inception of a project, setting clear expectations regarding timelines, deliverables, and potential challenges is essential. System integrators and vendors should communicate proactively to avoid misunderstandings and scope creep.
  • 4. Collaborative Problem-Solving: When faced with conflicting priorities or technical constraints, system integrators, vendors, and customers can engage in collaborative problem-solving. By brainstorming alternatives and exploring different approaches, a consensus can be reached that satisfies all stakeholders.

In Conclusion

While the customer’s needs and preferences are paramount in the world of system integration and vendor relationships, there are situations where saying no is necessary to uphold standards, ensure security, and deliver value. By fostering open communication, educating customers, setting clear expectations, and engaging in collaborative problem-solving, system integrators and vendors can navigate this delicate balance effectively. Ultimately, the key lies in fostering a relationship built on trust, respect, and a shared commitment to success.