Tag: network testing

Diagnosing the Invisible: How We Enhanced CDN Caching Visibility to Prevent 404 Failures

Posted on by Mira Abdulrahman

Milliseconds matter in today’s hyper-connected digital world, and content delivery must be seamless, reliable, and globally scalable. At DIGIS Squared, we’re committed to going beyond surface-level metrics to detect and resolve the subtle issues that impact end-user experience at scale.

One such challenge we’ve recently tackled involved intermittent 404 errors and browsing failures caused by CDN (Content Delivery Network) caching problems. What appeared to be random access issues turned out to be symptoms of deeper inefficiencies in how content was cached—and more importantly, how that caching was monitored.

The Hidden Problem: When the Cache Misses

CDNs are the unsung heroes of modern web performance. By distributing content across global edge servers, they reduce latency, offload origin traffic, and enable resilient access for users worldwide. But when caching fails, whether due to misconfigured TTLs, cache-busting headers, or regional edge node discrepancies the impact can be significant:

  • End-users encounter 404 errors or content that fails to load
  • The origin server receives unnecessary load, reducing scalability
  • Diagnostics become harder due to lack of cache-level transparency

We noticed these exact patterns in our browsing analytics: certain requests, particularly through Akamai and Cloudflare, were returning failures that didn’t align with backend health or application logic. This pointed to a cache-layer issue, not an application bug.

The Solution: A New Dashboard to Measure CDN Caching Effectiveness

To combat this, we built and deployed a new internal dashboard that focuses on one core KPI: CDN Caching Hit Success Rate.

Here’s what it includes:

CDN Hit/Miss Analytics:

We track whether content is being successfully served from the cache or fetched from the origin, giving us clear indicators of performance degradation.

Provider-Specific Breakdown:

The dashboard separately monitors:

  • Akamai
  • Cloudflare

…two of the world’s most widely used CDN providers, with distributed edge networks and high cache sensitivity.

Unified KPI:

To give a macro-level view, we also calculate a global hit ratio that consolidates data across all CDN providers we observe in browsing sessions, helping us detect broader trends or cross-provider anomalies.

Root Cause Visibility:

Combined with error codes like 404, we can now correlate browsing failures directly to cache misses. This has already enabled us to:

  • Identify content types with poor caching behavior
  • Advise clients on improving their CDN TTL, cache-control headers, and edge rule configurations
  • Proactively alert when hit ratios drop below optimal thresholds

Why This Matters to Telecom & Digital Experience Teams

For operators, OTT providers, and enterprises relying on global content delivery, cache efficiency is no longer a back-end concern; it’s a frontline performance metric. Here’s why this matters:

  •  A single percent drop in cache hit ratio can significantly increase origin load, affecting cost and latency
  • In telecom, real-time browsing quality KPIs are vital to SLA monitoring and customer retention
  • Cache failures often go unnoticed because traditional monitoring tools don’t surface them unless there’s a full outage

By adding this caching intelligence into our performance analytics suite, we’re enabling smarter diagnostics, better QoE benchmarking, and deeper insights across the full delivery chain from device to content edge.

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.

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.

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.

Cross-Sector Detection

Posted on by Mira Abdulrahman

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

Understanding Cross-Sector Problem

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

Why do we need such an algorithm?

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

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

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

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

The solution:

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

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

Benefits for Telecom Operators

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

Conclusion

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

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

PS Core Configuration Audit Use Case: 4G Quality of Service (QoS) Scheme

Posted on by Mira Abdulrahman

Problem

  • Referring to QCI counters and captured traces, all subscribers have QCI 7, which is NOT designed for normal data traffic and TCP-based applications, due to its nature specifications and high bit error rate.
  • The QCI values 1–4 are allocated for traffic that requires dedicated resource allocation for a GBR, while values 5–9 are not associated with GBR requirements.

Actions

  • Reviewing all QCI-8 RF parameters on 4G Cells.
  • A New Profile has been created on HSS with QCI=8 and assigned to test SIMs on it for testing and evaluation
  • AFC SL management staff SIMs were provisioned with QCI-8 for evaluation

Results

  • The recommended action was implemented for test SIMs. Core traces has been validated that the
  • subscriber’s actual QCI is QCI 8 (from MME Attach Accept Message sent to UE)
Before
After

Drive test was conducted before and after the change on two different sites to benchmark trial in terms of the LTE downlink throughput which had around 10% increase after the trial for the test SIMs.

Event Assurance Use Case

Posted on by Mira Abdulrahman

As a part of preparation activities for event assurance in a touristic region with extremely high demand and annual traffic growth, band strategy was reviewed to address the best allocation for each technology to achieve the best out of the existing band. Following this review, it was recommended to have new spectrum strategy

Pre-launching Activities

  1. Capacity assessment and utilization studies
  2. Hardware readiness study for 2nd U900 carrier addition
  3. Re-farming for 5MHz of the GSM900 band
  4. 2nd U900 cells activation

Post-launching Activities

  • Benchmark drive tests
    • 3G/4G camping and mobility strategy adjustment including interoperability settings

Results

  • Most of KPIs enhanced across all technologies 2G/3G/4G, especially 3G/4G throughput

INOS now available over AWS

Posted on by Gwen Edwards

INOS, Digis Squared’s vendor-agnostic, multi-network-technology solution delivering automated assessment, testing, post-processing and field optimisation of mobile networks, across all technologies, is now available as an AWS cloud-native tool.

The availability of INOS over AWS will enable Digis Squared’s clients to utilise the powerful mobile network testing and analytics capabilities with enhanced performance.

Abdelrahman Fady, Digis Squared CTO explained, “We’re excited to announce the deployment of INOS, our flagship product for network drive testing, benchmarking, post-processing and field optimisation over AWS. This deployment offers a host of benefits to our customers in the MNO industry, including a highly scalable and reliable infrastructure that can handle even the most demanding testing requirements.”

“With INOS’ advanced AI capabilities, our customers can gain predictive insights and automated optimizations that drive network efficiency and improve user experiences. This means that Digis Squared’s customers can proactively identify and address network performance issues, reducing downtime and improving overall network efficiency.”

“INOS’ deployment over AWS also means that Digis Squared’s customers can take advantage of a range of powerful tools and services that streamline network management, freeing up resources to focus on delivering value to their business. We’re thrilled to offer such a powerful and comprehensive solution to our customers and look forward to seeing the impact it has on their businesses.”

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

What is INOS?

Digis Squared’s INOS is a vendor-agnostic, multi-network-technology solution delivering automated assessment, testing, benchmarking, post-processing and field optimisation of networks. Generating actionable reports in just 15 minutes, combined with live-view for instant adjustments, INOS significantly reduces the time taken to complete the work and opex cost, to deliver optimum customer experience.

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

Discover more

Digis Squared ◦ Enabling smarter networks.