5G Core Powering the Future of Connectivity

In the ever-evolving landscape of telecommunications, the advent of 5G stands as a monumental leap forward. Beyond just speed, 5G introduces a transformative architecture known as the 5G core (5GC), revolutionizing the way we integrate, connect, and communicate. Let’s delve deeper into this advanced infrastructure and the pivotal concepts that shape its functionality.

Figure 1: High Level 5G Network architecture

The 5G core represents a fundamental shift from its predecessors, embracing a software-defined network architecture, cloud-native virtual network functions, and service-based architecture as well as full separation between the user plane and control plane through the full implementation of CUPS. Within this core, network functions play a pivotal role. These functions, ranging from authentication and policy control to session management and database management, are decoupled and virtualized, allowing for flexibility and scalability previously unseen in telecommunications networks. The Service-Based Architecture (SBA) in 5G represents a paradigm shift in how telecommunications networks are structured and function. At its core, SBA redefines network architecture by organizing various functionalities into modular and reusable services. These services, such as network slicing, authentication, session management, and policy control, are designed to be independent and interact through well-defined interfaces. This modular approach enables flexibility, allowing service providers to dynamically compose and deploy services tailored to specific user needs and applications. SBA facilitates efficient resource utilization, scalability, and rapid innovation, enabling the seamless integration of diverse services and applications across the 5G network.

 Its emphasis on standardized interfaces and service-based components fosters interoperability and encourages an ecosystem where new services can be rapidly developed, deployed, and managed in a more agile and cost-effective manner, ultimately driving the evolution of 5G networks to meet the demands of an increasingly connected world.

Figure 2: 5G core network functions

This cloud-service-based approach in the 5G core revolutionizes how telecommunications networks operate, offering a level of flexibility and scalability crucial for supporting a wide array of services and applications in the 5G era.

Network slicing, a defining feature of 5G, enables the creation of isolated, end-to-end virtual networks tailored to specific services or customer requirements.

By partitioning the network resources, 5G can allocate bandwidth, latency, and other parameters on demand. For instance, a slice designed for autonomous vehicles may prioritize ultra-low latency, ensuring real-time responsiveness, while another slice optimized for IoT devices might emphasize massive connectivity. This ability to customize network characteristics within slices is pivotal in meeting the diverse needs of various industries and applications.

Figure 3: Network Slicing Concept 

As part of 3GPP release 16 the Service Communication Proxy (SCP) has been introduced as a non-mandatory but vital node to have strong SBA deployment in coordination with multi-access edge computing (MEC) and it serves as a pivotal component within the 5G network, facilitating service-based communication between various network functions. Acting as an intermediary, the SCP ensures seamless interaction and coordination between functions such as the policy control function (PCF), user plane function (UPF), and network exposure function (NEF). It enables efficient handling of service requests, ensuring that data and control flow smoothly across the network, ultimately contributing to a robust and responsive network infrastructure and reducing the load on NRF (Network Repository Function)

Figure 4: SCP Function Description

Digis Squared stands out as a pivotal force in the domain of 5G system integration, renowned for its extensive proficiency in deploying both standalone and non-standalone 5G networks. our expertise spans across the entire spectrum of 5G infrastructure, encompassing radio, transport, and core networks. Through a meticulous approach to integration, we at Digis Squared ensure a cohesive and harmonized ecosystem, emphasizing seamless interoperability and optimal performance across these network layers.

Their in-depth understanding of 5G architecture enables us to tailor solutions that precisely address the unique needs and challenges encountered across diverse industries. For instance, in industries like healthcare, manufacturing, automotive, and entertainment, we at Digis Squared customize the integration strategies to accommodate specific requirements, whether it’s ultra-reliable low-latency communication (URLLC) for critical applications or massive machine-type communication (mMTC) for IoT devices as well as for the application that needs the enhanced mobile broadband (eMBB).

Digis Squared’s contributions extend beyond mere integration; they actively shape the 5G landscape by pioneering innovative solutions and best practices. Their role in driving the transition toward a fully connected future is instrumental, as they continuously refine their methodologies to adapt to the evolving demands of the 5G ecosystem. This commitment to innovation positions Digis Squared as a key enabler of the transformative potential inherent in 5G technology, propelling industries, and societies toward a more connected and technologically advanced era.

KATANA IPM Analytics

Leveraging advanced analytics and AI engine, IPM predicts and prevents network performance issues before they happen, building Capacity growth models and forecasting user behaviour and traffic load on the network, giving proper recommendations that keep network performance on track with this growth and user behaviour changes.

Figure 1: KATANA Platform Modules

IPM Analytics is the heart of KATANA IPM module, and it offers the below different uses cases.

Figure 2: IPM Main Functionalities sub-modules

Anomaly Detection:employs a machine-learning algorithm to understand the patterns of Key Performance Indicators (KPIs), making comparisons and autonomously recognizing deviations. The resulting scores are presented for each instance, facilitating straightforward identification of anomalies and their deviation from the typical cluster or common behavioral patterns within the network.

Forecasting Analysis: iPM encompasses various forecasting techniques within a unified interface, granting users the capability to analyze anticipated future trends in network usage for any counter and Key Performance Indicator (KPI).

Figure 3: DL Traffic Volume Forecast

Capacity Management: As networks expand and experience heightened traffic, there is often a decline in network performance. To prevent this deterioration, iPM Capacity management function becomes crucial to enhance performance and restore it to its initial levels. iPM is Addressing traffic shifts requires the strategic rebalancing of network traffic, ensuring even utilization across the network, thereby deferring capital expenditures on new equipment.

Worst Cell List: The Worst Cell List Report, an integral component of our iPM capabilities, is robustly supported by ranking conditions tied to specific periods for designated Key Performance Indicators (KPIs). This functionality empowers users to assess nodes with the poorest performance through detailed maps and charts.

Worst Degraded List: This module, seamlessly integrated into our iPM suite, efficiently troubleshoots and compiles a list of nodes with degraded performance over a specified period. It conducts in-depth analyses through maps and charts, facilitating immediate examination at the work area for detailed troubleshooting

Figure 4: Creation Criteria for WDL

Benchmark: After implementing an optimization action, users have the flexibility to initiate a benchmark across a set of Key Performance Indicators (KPIs). This benchmarking can be conducted on a Day-to-Day, Week-to-Week, or Month-to-Month basis, allowing for comprehensive performance evaluation over various timeframes.

Swap & Acceptance: In Swap Projects, users are required to compare Key Performance Indicators (KPIs) before and after the swap. iPM provides users with the convenient option to effortlessly compare the performance of vendors, facilitating a streamlined assessment of the impact of the swap on network performance.

Eagle Eye: Revolutionizing Mobile Network Testing with INOS


In the world of mobile network testing, efficiency and accuracy are crucial for optimizing network performance. The “Eagle Eye” feature in INOS is a powerful tool that enables users to analyze large data sets in logfiles, extracting valuable insights through geofencing. This feature facilitates data-driven decision-making for network optimization.

Unveiling “Eagle Eye”

The “Eagle Eye” feature in INOS allows users to effortlessly search through logfiles containing millions of samples, making it a reality. Unlike the traditional method that required meticulous effort in recalling file names, dates, and locations, “Eagle Eye” offers an intuitive solution. Users can define their area of interest using geofencing, and the feature retrieves all relevant data within that area, saving both time and effort.

Figure 1

Optimizing Search Results

“Eagle Eye” offers various settings to streamline the search process, allowing users to optimize and narrow down their results efficiently. These settings include:

  • Time Aggregation: Users can pick time granularity (hourly, weekly, or monthly) to analyze data over specific periods, aiding trend identification.
  • Distance Aggregation: Users can set the desired distance aggregation for a detailed location-based network performance analysis.
  • Operator MNC/MCC: Users can filter their search results based on specific Mobile Network Code (MNC) and Mobile Country Code (MCC).
  • KPI Selection: Users can choose the Key Performance Indicators (KPIs) they wish to extract from the logfiles.
  • Date Range and Time Clustering: Users can set a date range and cluster data in time to better understand network performance changes in specific periods.

Interpreting the Results

Once the search parameters are set, “Eagle Eye” presents users with an interactive map accompanied by a timeline.


“Eagle Eye” offers a user-friendly visual representation for easy navigation, providing insights into network performance across locations and timeframes. It also includes benchmark tables and histogram charts for comparative analysis and trend identification.

Use Case Scenarios

The application of “Eagle Eye” in INOS extends to various use cases like;

  • Pre-action Network Assessment
  • Performance Benchmarking
  • Team Performance Assessment
  • Investment Impact analysis


“Eagle Eye” in INOS is a game-changer for mobile network testing, with geofencing, result optimization, and visual representations that empower efficient insights extraction. It enhances decision-making, network performance, and operational excellence in mobile network testing.