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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 AshrafProduct Architect and Support Director at Digis Squared.

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

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

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

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

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

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

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

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

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

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

About ACES NH:

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

Optimizing LTE 450MHz Networks with INOS 

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 AshrafProduct Architect and Support Director at Digis Squared. With extensive experience in telecom solutions and AI-driven technologies, Amr plays a key role in developing and optimizing our innovative products to enhance network performance and operational efficiency.

Why Service Providers Should Go Vendor-Agnostic?

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

1. Flexibility and Customization for Clients

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

2. Improved Trust and Objectivity

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

3. Access to Best-of-Breed Technology

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

4. Reduced Vendor Lock-In Risks

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

5. Broader Industry Knowledge and Expertise

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

6. Enhanced Scalability and Future-Proofing

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

7. Cost Savings for Clients

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

8. Enhanced Service Continuity and Reliability

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

Summary

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

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

Cross-Sector Detection

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

Understanding Cross-Sector Problem

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

Why do we need such an algorithm?

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

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

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

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

The solution:

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

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

Benefits for Telecom Operators

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

Conclusion

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

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

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

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?

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.