Tag: network strategy

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

Posted on by Mira Abdulrahman

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Semantic Communications: Rethinking How Networks Understand Meaning

Posted on by Mira Abdulrahman

Traditional communication models, like Shannon’s theory, have always focused primarily on the accuracy of bit transmission from sender to receiver. But in today’s world, dominated by AI, IoT, and immersive experiences, this approach is becoming increasingly insufficient. The challenge isn’t just about transmitting data anymore; it’s about transmitting the right data, with the right context, at precisely the right moment.

At its core, semantic communication represents a model that prioritizes understanding over mere accuracy. Rather than sending every bit of information, semantic systems intelligently transmit only what’s necessary for the receiver to reconstruct the intended meaning. This represents a profound shift in how we conceptualize network communication.

Consider this practical example: a device needs to send the message “I need a glass of water.” In classical communication, this entire sentence would be encoded, transmitted, and decoded bit by bit, regardless of context. But in a semantic communication system, if the context already indicates the user is thirsty, simply transmitting the word “glass” might be sufficient to trigger complete understanding. This approach is inherently context-aware, knowledge-driven, and enhanced by artificial intelligence.

The necessity for semantic communication becomes increasingly apparent when we consider its practical benefits. It substantially reduces redundant data transmission, which conserves both bandwidth and energy, critical resources in our increasingly connected world. For latency-sensitive applications like critical IoT systems, autonomous vehicles, and holographic communication, this efficiency translates to meaningful performance improvements. Furthermore, it enhances machine-to-machine understanding, enabling more intelligent edge networks, while aligning communication more closely with human-like reasoning patterns, making our interactions with technology more natural and efficient.

When we examine these advantages collectively, it becomes evident that semantic communication isn’t merely a beneficial addition to our technological toolkit; it represents a fundamental paradigm shift in communications technology.

The enabler of this transformation is undoubtedly artificial intelligence, particularly in domains such as natural language understanding, knowledge graphs, semantic representations, and the ability to learn shared context between sender and receiver. When integrated with Digital Twins and Cognitive Networks, semantic communication becomes even more powerful, allowing systems to predict, understand, and take proactive action rather than simply reacting to inputs.

At Digis Squared, we view Semantic Communication as a cornerstone of future AI-native networks. I believe it will fundamentally reshape how we design, operate, and optimize telecom systems, not only by increasing efficiency but by making networks truly intelligent.

As Head of Product, I find myself increasingly asking a question that challenges conventional thinking: What if our networks could understand why we communicate, not just what we communicate? This perspective shifts our focus from merely building faster networks to creating smarter, more meaningful ones that truly understand human intent.

Author: Mohamed Sayyed, Head of Product at DIGIS Squared

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

Share:

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.

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.

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.