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Category: Network Capacity

  • How to enhance accuracy and efficiency of Network Analytics for 5G

    How to enhance accuracy and efficiency of Network Analytics for 5G

    As 5G gains traction, CSPs will look to unearth its potential benefits. However, what 5G will also bring in is a compound effect of the challenges that CSPs face already.

    The Three Challenges CSPs face

    The first of these challenges pertains to the increasing capex intensity which is plaguing the telecom industry, which currently stands at 16%, the highest among any other sector in the world. The second challenge comes from the growing network complexity which is quickly as CSPs continue to pursue network-wide densification and virtualization. The final challenge comes from the fact that customer expectations, as it historically has, is rising in multiples, and with the number of connected devices and subscribers being added to the mix increasing, and leading to the an increase in data volumes, the expectations of customers is rising manifold.

    The above challenges are ones CSPs need to deal with in a 4G environment, and the 5G landscape is only set to accelerate these trends further.

    How will 5G change things

    One of the better-known advantages that come with 5G is the fact that it opens up CSPs to operator in a new arena beyond the usual consumer space: Enterprises. With 5G CSPs will play an integral instrument for driving newer use cases around IoT, industrial automation, smart cities, autonomous vehicles, robotic surgery and a lot more. What this means is that, for CSPs, 5G use cases will now bring in 3 larger use cases:

    1. eMBB (Enhanced Mobile Broadband) – which is designed for better user experiences on consumer devices, providing extreme network speeds and capacity

    2. mMTC (Massive Machine-type Communication) – which aims to provide connectivity to a huge number of devices whose traffic profile is typically a small amount of data (spread) sporadically.

    3. URLLC (Ultra-Reliable Low Latency Services) – which focusses on low latency, high reliability and high availability aspects

    With a focus around these three overarching use cases, 5G promises to deliver superior experience to not just subscribers but enterprises also. From a consumer angle, for close to 60% of smartphone users, the highest expectation for 5G is to deliver speeds faster than current mobile networks. Across the globe, data consumption is increasing by more than 40 percent every year. Video in particular will account for 75 percent of network traffic by 2024, up from 55 percent today. Networks will need to adapt from Standard and High Definition video resolution to 2K and 4K, placing higher requirements on speed and quality. Further into the future, services like AR/VR applications will put even higher requirements on network performance. This demand for better customer experience means that operators must continuously adapt and expand network capacity.

    From an Enterprise standpoint, with 5G having the capability to reduce latency to sub-milliseconds, businesses can start to consider the nexus between cloud, mobile and enterprise applications. More features and functionality can be extended to the mobile fleet and frontline employees. The use cases for ultralow latency apps can be vertical specific. This can include the financial services for high-frequency trading, as well as mining and energy companies rolling out autonomous fleets that are highly responsive to their environments. Moreover, 5G complements other emerging technologies such as AI/ML, robotics and IoT to further drive enterprise digital transformation both internally within an organization as well as externally in customer engagements.  Of course, at the end of the day, 5G is a telecom technology, and hence CSPs will be at the heart of driving 5G within the enterprise. However, this will also mean a new area where CSPs will need to direct capacity.

    What all the above add to is the need to build Superior experiences, which will be the main driver to generate new revenue stream in 5G era.

    The Need for Intelligent Capacity Management

    The main enabler to provide a superior experience will be an   approach based on Artificial Intelligence and Machine Learning. With capacity as the main enabler for raising speed to the 10x Gbps mark an Intelligent Capacity Management approach will in-turn help to meet the future growth requirements of massive device volumes.

    In line with this, an accurate capacity management process will help CSPs to differentiate themselves from the competition. Why Accuracy? Accuracy is key in any capacity management process as it directly impacts the Capex and ROI. An Intelligent Capacity Management solution built on Intelligent Data Management, Advanced Automation, and Domain-Driven Data Science, can help network planners achieve accuracy in each phase of capacity management process of Assessing, Planning and Optimizing capacity augmentations.

    Intelligent Capacity Management for Better Network Planning

    To succeed in the 5G journey, CSPs will need to make the right investments to gain competitive advantage and enhance end-user experience. However, such investments are highly cost intensive, both in terms of Capex and Opex. Any inaccuracies in the overall network investment planning can lead to unintended impacts on company financials and can create significant network inefficiencies. Hence, an Intelligent Capacity Management can help ensure accuracy to make the right Network Investment Plans.

    Intelligent Capacity Management for Better Customer Experience

    5G will also enable CSPs to increase revenues due to its potential of not only transforming mobile communications, but also completely replacing broadband internet. Considering most 5G deployments are focusing on FWA (Fixed Wireless Access) as the first use case, this is clearly the intent among most telecom operators. However, this will demand a dramatically broader capacity than CSPs are generally used to providing. This can only be achieved through an Intelligent approach to Capacity Management.

    Business Benefits of Intelligent Capacity Management

    An Intelligent Capacity Management Approach can bring about a:

    • 70%+ Increase in Accuracy
    • 80%+ Increase in Operational Efficiency
    • 7%+ Increase in Capex Efficiency
    • 10%+ Increase in ROI

    Read a case study on how we helped a Tier-I telcos increase their revenues by over 12% through intelligent network investment planning.

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  • 2021: The Year of accelerated network transformation

    2021: The Year of accelerated network transformation

    “The secret of change is to focus all of your energy, not on fighting the old, but on building the new.”
    – Socrates

    2020, a year that redefined many aspects of our lives and challenged every individual, industry, and nation to rethink, re-strategize, re-innovate, and revive the brutal impact of one of the most gruesome pandemics in the history of humanity. On the one side, the pandemic led to loss in various forms. On the other, it led us to shift focus towards building something new, which would better equip us in challenging times.

    As we look forward to 2021, it is essential to note that while COVID-19 accelerated digital transformation initiatives, the pandemic also reiterated that true digital transformation in the mobile communication industry could only happen through a holistic network transformation strategy execution.

    As we move forward, networks across the globe will witness an acceleration in the transition from LTE to LTE-advanced/pro to 5G-NSA (Non-Standalone) to 5G-SA (Standalone) deployments. The stage may vary from one CSP to another, but one thing is evident: the adoption of 5G is gaining momentum and shaping the telecom sector outlook like never.

    In many ways, 2021 will see some significant 5G milestones that will redefine a CSP’s quest to disrupt the digital economy. It does not matter where you are or will be in your network transformation journey; the below critical trends will drive innovations to help CSPs realize the true digital value derived from the network. It will redefine a CSP’s journey for the decades, not merely years, to come.

    Key trends towards the network transformation from LTE to 5G SA

    1. Cloud adoption: A focused cloud-led investment in business and technology domains where cloud platforms can enable increased revenues and improved margins will be a mandatory step towards the 5G journey. A telco can either adopt a public cloud or a private cloud or hybrid cloud to transit from their existing on-premise infrastructure. Companies that adopt the cloud well will bring new capabilities to the market more quickly, innovate more easily, reduce risks effectively, and scale more efficiently.

    One cannot realize the true potential of 5G without moving to the cloud. Some of the significance and relevance of the cloud in the 5G are:

    • 5G edge computing: 5G technology opens a new business segment for CSPs, i.e., enterprises. 5G edge computing will be quintessential to realize these opportunities as it will provide distributed cloud infrastructure resources closer to a location where it is required and will help CSPs tap into new business opportunities by supporting a variety of use cases in a distributed cloud environment.
    • 5G core: As per the new 3GPP standards for 5G core, most of the functions will be cloud-native and container-based. Cloud-native, a modern approach to building and running applications that fully exploit the advantages of the cloud computing model, will require a robust cloud infrastructure for successful 5G standalone deployment. The cloud-native approach of application development to support 5G functions will be established in containers, microservices, and dynamic orchestration and will leverage the scalability and high-availability characteristics of the cloud.
    • 5G experience: To offer a superior 5G experience to consumers and enterprises, CSPs need to consider the exponential growth in network, devices, and data volume, which will result in enormous complexities that can be significantly simplified and managed through cloud adoption. This will help CSPs roll out new services quickly, innovate, and scale faster to meet the customer demands in the shortest time.

    A recommended cloud adoption journey is shown below:

     

    “In order to be cloud-native, you need to first immerse yourself completely into the cloud”

    2. Open RAN Evolution: “Open RAN,” which is seen as a revolution in the mobile communication industry, disaggregates hardware and software and creates open interfaces between them, creating new avenues for innovation, eventually resulting in Capex as well as Opex efficiencies. Before I get into “why” open RAN is a crucial trend for network transformation, let’s look into below key predictions by some of the established firms:

    GSMA:

    • Globally, it is forecasted that CSPs will spend 80% of the sector’s Capex ($890 billion) on 5G networks over the next five years
    • 57% of CSPs intend to introduce new vendors into their network supplier roster

    Omdia:

    • 2021 will see 5G RAN investments will exceed those in LTE RAN, and it predicts that 5G will account for more than half of all RAN investments during the year

    Deloitte:

    • There are currently 35 active open RAN deployments across the globe
    • Many of these involve MNOs testing open RAN in greenfield, rural, and emerging markets.
    • Open RAN deployments could easily double in 2021.

    RAN (Radio Access Network) has always been an investment-intensive domain, consuming more than 70% of overall network investments. To ensure profitability, it is now time for CSPs to look for an alternative and innovative option. CSPs see open as a way to accelerate service introductions and potentially drive new service combinations. Open RAN could, in turn, unlock new business opportunities by enabling new ways of building networks.

    As per Heavy Reading’s 2020 Open RAN operator survey, 20% of operators with revenues greater than $5 billion will have 1000+ Open RAN macro-outdoor sites by the end of 2023.

    As we look ahead, Open RAN is not merely a concept anymore; it is now a reality. From 2021 onwards, most of the Open RAN commercial deployments will be centered around RUs (Radio Units) and DUs (Distributed Units). From 2022 onwards, RIC (RAN Intelligent Controller) commercial deployments will be expected to start.

    “Let’s revolutionize the most complex and cost intensive part of the network: the RAN.”

    3. AI/ML-driven Closed-Loop Automation: As CSPs plan to transit from 5G NSA to 5G SA, end-to-end (e2e) network slice management will become mandatory. Network slice complexity will be extremely high as it consists of RAN, transport, and core network slice subnets. Each of these subnets possibly consists of further subnets. For example, the RAN subnet can be decomposed further into fronthaul, midhaul, and RAN network functions. Managing such a complex network slice environment manually will not be a feasible option; it will require a hyper-level of automation.

     Network automation has evolved significantly over the last few decades. A simplified view of this evolution is shown below:

    “It is time to bring intelligence in automation across every part of the network.”

    Closed-loop automation powered by machine and deep learning models is an emerging and promising stage of automation. It will redefine the overall network management and orchestration ecosystem.

    The Open Network Automation Platform (ONAP) project, an open-source project, provides a platform for designing, implementing, and managing different network services. The latest ONAP release (Guilin release) has taken a significant step by implementing the fundamental aspects of the 3GPP-specified NSSMF (Network Slice Subnet Management Function) for the RAN, transport, and core subnets, and also connecting to an external RAN NSSMF for the RAN subnet. Enhancements were also made to NSMF (Network Slice Management Function) functionality in stitching together an end-to-end slice.

    A couple of basic closed-loop scenarios (one with Machine Learning done offline) involving the RAN subnet and initial steps to monitor KPIs by the operator/slice tenant have been implemented.

    We will see significant enhancements in upcoming releases, and ONAP, being an open-source project, will be an open ground for innovation, not just for incumbents but for start-ups as well.

    The COVID-19 pandemic has changed both the trajectory and the pace of digital transformation and will likely continue to do so into 2021. The key trends associated with network transformation are an open invitation to innovate and disrupt. It will see many organizations trying to solve some of the critical business challenges and helping CSPs realize the true potential of 5G. It offers a unique opportunity to bring a fresh change in the telecom industry by building something new, something never tried before, something radical and revolutionary.

    To understand the key pillars, and unlock the full potential of your network

    Read The Whitepaper

  • Planificación de capacidad de red inteligente para 5G

    Planificación de capacidad de red inteligente para 5G

    A medida que 5G gane tracción, los CSP buscarán descubrir sus beneficios potenciales. Sin embargo, lo que 5G también traerá es un efecto compuesto de los desafíos que ya enfrentan los CSP.

    Los tres desafíos que enfrentan los CSP

    El primero de estos desafíos se relaciona con la creciente intensidad de gasto de capital que está afectando a la industria de las telecomunicaciones, que actualmente es del 16%, la más alta entre cualquier otro sector del mundo. El segundo desafío proviene de la creciente complejidad de la red que se produce rápidamente a medida que los CSP continúan buscando la densificación y virtualización en toda la red. El desafío final proviene del hecho de que las expectativas de los clientes, como históricamente lo han hecho, están aumentando en múltiplos, y con el aumento de la cantidad de dispositivos conectados y suscriptores que se agregan a la combinación, lo que lleva a un aumento en los volúmenes de datos, las expectativas de los clientes se multiplican.

    Los desafíos anteriores son los que los CSP deben enfrentar en un entorno 4G, y el panorama 5G solo está configurado para acelerar aún más estas tendencias.

    ¿Cómo cambiará las cosas el 5G?

    Una de las ventajas más conocidas que vienen con 5G es el hecho de que abre los CSP al operador en una nueva arena más allá del espacio habitual del consumidor: las empresas. Con 5G, los CSP serán un instrumento integral para impulsar nuevos casos de uso en torno a IoT, automatización industrial, ciudades inteligentes, vehículos autónomos, cirugía robótica y mucho más. Lo que esto significa es que, para los CSP, los casos de uso de 5G ahora traerán 3 casos de uso más grandes:

    1. eMBB (banda ancha móvil mejorada): que está diseñada para mejorar la experiencia del usuario en los dispositivos de los consumidores, proporcionando velocidades y capacidad de red extremas
    2. mMTC (comunicación masiva de tipo máquina): tiene como objetivo proporcionar conectividad a una gran cantidad de dispositivos cuyo perfil de tráfico suele ser una pequeña cantidad de datos (propagación) de forma esporádica.
    3. URLLC (servicios de baja latencia ultra fiables), que se centra en aspectos de baja latencia, alta fiabilidad y alta disponibilidad.

    Con un enfoque en estos tres casos de uso generales, 5G promete brindar una experiencia superior no solo a los suscriptores sino también a las empresas. Desde el punto de vista del consumidor, para cerca del 60% de los usuarios de teléfonos inteligentes, la mayor expectativa de 5G es ofrecer velocidades más rápidas que las redes móviles actuales. En todo el mundo, el consumo de datos aumenta en más del 40 por ciento cada año. El video en particular representará el 75 por ciento del tráfico de red para 2024, frente al 55 por ciento actual. Las redes deberán adaptarse de la resolución de video estándar y de alta definición a 2K y 4K, lo que exige mayores requisitos de velocidad y calidad. En el futuro, los servicios como las aplicaciones AR / VR impondrán requisitos aún mayores en el rendimiento de la red. Esta demanda de una mejor experiencia del cliente significa que los operadores deben adaptarse y expandir continuamente la capacidad de la red.

    Desde el punto de vista empresarial, dado que 5G tiene la capacidad de reducir la latencia a menos de milisegundos, las empresas pueden comenzar a considerar el nexo entre la nube, las aplicaciones móviles y empresariales. Se pueden extender más características y funcionalidades a la flota móvil y a los empleados de primera línea. Los casos de uso de aplicaciones de latencia ultrabaja pueden ser verticales específicos. Esto puede incluir los servicios financieros para el comercio de alta frecuencia, así como las empresas mineras y energéticas que despliegan flotas autónomas que responden en gran medida a sus entornos. Además, 5G complementa otras tecnologías emergentes como IA / ML, IoT y robótica para impulsar aún más la transformación digital empresarial tanto internamente dentro de una organización como externamente en los compromisos con los clientes. Por supuesto, al final del día, 5G es una tecnología de telecomunicaciones y, por lo tanto, los CSP estarán en el corazón de la conducción de 5G dentro de la empresa. Sin embargo, esto también significará una nueva área en la que los CSP necesitarán dirigir la capacidad.

    Todo lo anterior se suma a la necesidad de crear experiencias superiores, que serán el principal motor para generar una nueva fuente de ingresos en la era 5G.

    La necesidad de una gestión de capacidad inteligente

    El principal habilitador para brindar una experiencia superior será un enfoque basado en Inteligencia Artificial y Aprendizaje Automático (IA / ML). Con la capacidad como el principal habilitador para aumentar la velocidad a la marca de 10x Gbps, un enfoque de gestión de capacidad inteligente ayudará a su vez a cumplir con los requisitos de crecimiento futuro de los volúmenes masivos de dispositivos.

    En línea con esto, un proceso de gestión de capacidad preciso ayudará a los CSP a diferenciarse de la competencia. ¿Por qué precisión? La precisión es clave en cualquier proceso de gestión de la capacidad, ya que impacta directamente en el Capex y el ROI. Una solución de gestión de capacidad inteligente basada en la gestión de datos inteligente, la automatización avanzada y la ciencia de datos basada en dominios puede ayudar a los planificadores de red a lograr precisión en cada fase del proceso de gestión de la capacidad de evaluación, planificación y optimización de los aumentos de capacidad.

    Gestión de capacidad inteligente para una mejor planificación de la red

    Para tener éxito en el viaje de 5G, los CSP deberán realizar las inversiones adecuadas para obtener una ventaja competitiva y mejorar la experiencia del usuario final. Sin embargo, estas inversiones son muy costosas, tanto en términos de Capex como de Opex. Cualquier inexactitud en la planificación general de la inversión en la red puede generar impactos no deseados en las finanzas de la empresa y puede crear importantes ineficiencias en la red. Por lo tanto, una gestión de capacidad inteligente puede ayudar a garantizar la precisión para realizar los planes de inversión de red correctos.

    Gestión de capacidad inteligente para una mejor experiencia del cliente

    5G también permitirá a los CSP aumentar los ingresos debido a su potencial no solo de transformar las comunicaciones móviles, sino también de reemplazar por completo la Internet de banda ancha. Teniendo en cuenta que la mayoría de las implementaciones de 5G se centran en FWA (acceso inalámbrico fijo) como el primer caso de uso, esta es claramente la intención entre la mayoría de los operadores de telecomunicaciones. Sin embargo, esto exigirá una capacidad mucho más amplia que la que suelen proporcionar los CSP. Esto solo se puede lograr mediante un enfoque inteligente de la gestión de la capacidad.

    Beneficios comerciales de la gestión inteligente de la capacidad

    Un enfoque de gestión de capacidad inteligente puede generar:

    • 70% + Aumento de la precisión
    • Más del 80% de aumento en la eficiencia operativa
    • 7% + aumento en la eficiencia de Capex
    • 10% + Aumento del ROI

    Lea un estudio de caso sobre cómo ayudamos a las empresas de telecomunicaciones de nivel 1 a aumentar sus ingresos en más del 12% mediante la planificación inteligente de inversiones en redes.

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  • Building a Case for Advanced Analytics in the Capacity Planning process

    Building a Case for Advanced Analytics in the Capacity Planning process

    There has been significant data growth in the world of telecommunications during the modern era of mobile technologies, i.e., 4G and 5G. While data growth has often been marketed as a massive window of opportunity, in reality, the ever-expanding volumes of data are overwhelming for existing systems, thereby indirectly leading to issues concerning operational efficiency and network performance. To utilize the vast volume of structured and unstructured data, organizations must have the right innovation-driven solutions to maximize their revenue potential.

    As a part of the network capacity management team, I would like to elaborate on the above from the perspective of capacity planning. In network capacity planning, it is essential to identify potential shortcomings or, in other words, factors that can impact network efficiency and performance. Some of these factors include the need for capacity expansion due to the unavailability of adequate network resources for handling the traffic demand, or an optimization requirement in the network investment plan for better utilization of existing resources. Traditionally, most of these decisions are made manually, which we all can agree, is not practical, efficient, or even error-proof. An AI-based capacity management system is the need of the hour to exploit the full potential of resources and network data.

    How AI can enhance Capacity Management

    AI and advanced analytics are terms that get used a lot, across industries, especially by marketing teams. However, beyond the ‘buzzword,’ these technologies have an essential role to play in enabling improvements from current, traditional processes. Advanced analytics-driven capacity management solutions can help users decrypt relevant information to derive useful insights by applying data analysis, predictive analytics, forecasting, and optimization models. These insights, backed by data, and fine-tuned through Machine Learning, can help organizations make the best possible decisions by anticipating and implementing necessary network changes ahead of the demand curve.

    A significant component for providing reliable Network Investment Planning (NIP henceforth) recommendations towards Capacity Enhancement/Optimization is through the accurate forecasting of capacity KPIs. Forecasting is the foundation on which NIP recommendations are based.

    A pool of forecasting models can be used to predict network performance. These models can be broadly classified as qualitative methods and quantitative methods. Qualitative methods are used when historical data is not available or when data is noisy. Quantitative methods, on the other hand, make forecasts based on mathematical models rather than subjective judgement. Choosing the right forecasting model depends on many factors like the context of forecast, relevance, availability of historical data and the desired level of accuracy.

    Which is the right forecasting model to adopt for your business?

    Different forecasting methods have distinct advantages and disadvantages. Therefore, selecting the right forecasting method is of critical importance to all decision-makers.

    My data science team at Subex and I have built a proprietary machine-learning model for forecasting called CapMan4C. During this experience, we also collaborated with a team of domain experts to have clarity on the problem which businesses are trying to resolve and to understand data specific to the network domain.

    I bring up this experience to highlight that the thoughts involved behind building a proprietary framework for forecasting are based on two criteria:

    • What does our expertise in the telecom domain tell us?
    • What do our customers say?

    Based on these insights, it is evident that traditional forecasting time series models are observed to not perform well on network data. Using my prior data science experience in the healthcare industry, I applied different machine learning models like decision tree, random forest, etc., for forecasting on telecom network data. I observed that plugging in these existing models do not provide the desired level of accuracy. The approach of leveraging a domain-driven model eliminates the problems mentioned above. Its framework is designed so that the selection of a base model for forecasting does not solely depend on traditional time series models.

    To further enhance the accuracy of the model, this kind of framework also needs to include three key components:

    1. Recency factor – The general assumption while working with time series data is that all the historical data points have equal weightage. However, when the event ‘recency’ is accounted for, this notion does not always hold. In theory, an event that has taken place more recently is more indicative of the current situation than a distant past.
    2. Feature engineering –Feature engineering is a creative process in which we experiment and extract features that can be useful for enhancing the model. Several features, if extracted at a granular level from the data (including data from external sources), can help identify hidden trends and seasonality in data and make more insightful forecasts from the machine learning model.
    3. Auto-hyperparameter tuning– Hyperparameters are parameters of ML-models specified by “hand” based on expertise and domain experience. These parameters can be used to control the learning process, which can significantly impact the performance of the trained model. The more flexible and powerful an algorithm is, the more design decisions and adjustable hyper-parameters it will have. Traditionally, Grid Search or Random search is used to perform hyperparameter optimization. Grid Search works by searching exhaustively through a specified subset of hyperparameters. This can be very time consuming and computationally expensive. Random search is slightly better but is similar to Grid Search because it pays no attention to past evaluation. As a result, network teams often spend a significant amount of time evaluating “bad” hyperparameters.

    In summary, operating a network is a complicated endeavor, and regardless of how much planning takes place, problems do occur. Identifying the right kind of AI-driven approach toward capacity management provides network planners the ability to analyze “what-if scenarios” based on forecasts from ML models. It helps them take calculated decisions on Network Investment Planning based on AI-based recommendations. This will help them to mitigate risks proactively and maximize their revenue potential.

    To understand these key pillars, and unlock the full potential of your network

    Read This Whitepaper

  • Network Infrastructure – Embracing disruption through innovation

    Network Infrastructure – Embracing disruption through innovation

    There are few critical junctures where industry changes its course, transforms, and readies itself for future growth. Such junctures often coincide with technology disruptions, followed by a long period of organic adoption. In this post, we will look at key disruptions that are steadily metamorphosing the telecommunications industry in modernizing their network infrastructure.

    The Virtualization Chasm

    Nearly a decade ago, industry initiatives spawned the promising technologies Software Defined Networks (SDN) and Network Function Virtualization (NFV). These technologies enable software-driven management of networks from the core networks to aggregation to access networks. The network workloads no longer reside on OEM-supplied proprietary hardware. Instead, they are packaged into virtual network functions (VNFs) and hosted on virtual environments. Over time, the virtualization concept has taken different shapes and slowly adapted to market needs. As in any technology adoption cycle, SDN/NFV adoption has encountered a “chasm” towards achieving a wider reach. The chasm, in this case, is due to transformation challenges, unproven success stories, internal system & process challenges, business prioritization, etc.

    The chasm, however, hasn’t stopped a majority of Telcos from adopting the virtualization principles to meet their current needs. One of the key developments that happened during the last decade is the virtualization of “low-hanging” network workloads such as firewalls, OSS/BSS systems, DNS, DHCP, IMS, etc. Telcos started adopting “private clouds,” i.e., virtualization infrastructure, and started hosting network workloads (VNFs) in virtual machines. This has given the Telcos a tremendous ability to optimize costs, control security policies, and scale up/down the infrastructure on a need basis.

    While a few operators have started working on agile, software-controlled, and service orchestration platforms, wider adoption still has a ways to go. The challenges of handling both physical network functions (PNF) and virtual network functions (VNFs) in the same platform, with the additional complexity of interfacing with legacy networks, persist. This is where a collaborative partnership with vendors like Subex, could bring in the expertise that enriches such transformation programs.

    Cloud Next

    Let’s move from network workloads to the non-network application infrastructure. The recent news of Microsoft’s $2 bn cloud deal with AT&T has created quite a stir in the telecom world. The move from AT&T to migrate all non-network OSS/BSS workloads to public clouds will usher in hyperscale cloud benefits such as lower TCO, higher agility, faster time to market, and scalability. We expect few major Telcos also to follow suit in the near term. But here is the interesting statistic from the TMForum study: only a minor segment of players is either already on the public cloud or ready to migrate OSS/BSS onto the public cloud. As expected, a large percentage of operators are either cautious or have decided not to move to the public cloud.

    Will the vendors start supporting cloud-native architectures immediately? The answer is yes, and no. There is a small set of vendors like Subex who are already on this path and will be able to support Operators in their quest towards a cloud-first strategy. At the same time, there is a more significant number of vendors who rely on the “lift and shift” strategy in near to medium term and look forward to making their offerings truly cloud-native in the longer run. This is an interesting development to watch out for.

    Open RAN

    A few years ago, the big OEMs introduced single RAN or proprietary vRAN products to market, which failed to catch the true spirit of open interfacing that Telcos were looking for. This motivated a few startups to work collaboratively on a novel idea – to run their products on commodity servers, enable software control via open interfacing protocols, and make seamless future upgrades just by pushing software patches. The outcome of this exercise is the Open RAN disaggregated networking model from the Telecom Infra Project (TIP) forum. The primary goal is to optimize the huge Capex / Opex that goes into building RAN networks in brownfield environments. Moving from access towards virtualization of aggregation and core networks, service orchestration, workload containerization, and zero-touch provisioning is slowly becoming a reality and is one of the key areas of interest for technology teams in Telcos.

    Exciting times

    Over the last decade, network infrastructure has indeed been disrupted. The trend has accelerated in the last few years with the adoption of SDN WAN, NFVi infrastructure, public cloud, Open RAN, etc. Network managers will need to make technology transformation decisions based on key factors like network agility, scalable architecture, faster time to market, superior customer experience, adaptive operations, Capex/Opex reduction, and simply being future-ready. The industry ecosystem is already witnessing the green shoots of virtualization and cloud transition. It’s an exciting time to be part of this development.

    Explore more about Network Infrastructure solution of Subex.

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  • State of the Network and a Shout-Out to Network Heroes

    State of the Network and a Shout-Out to Network Heroes

    On display in my house is a 1926 Kellogg wall phone.  I can’t use it to place a call, but it looks great.  It’s crafted from beautifully finished oak with a candlestick earpiece and a small platform for taking notes.  It still has the original internal components, including a magneto and dry cell batteries.  The antique phone connects me to a proud lineage of engineers, technicians, field personnel, customer service staff, and network professionals (among many others) who have built this industry.  I’ve worked in telecom my entire career and have been awed by the response from operators around the globe to the current situation.

    While so many service businesses have shed workers and are fighting for survival, other sectors are faring better.  Streaming services like Netflix and online retailers like Amazon and Walmart are seeing demand surge.  We’re all familiar with the ascension of Zoom from an enterprise video-conferencing provider to a social media sensation.

    What do each of these online services have in common?  They are dependent, of course, on ubiquitous, reliable, and relatively cheap communications services. Our digital devices connect us to the people we care about, summon food, keep us entertained and allow many to continue their education.   Communications services have enabled the new “normal”: Do laundry, watch children, stroke a pet, finish a meal… all while joining a conference call!  We’ve perhaps never leaned so heavily on global communications networks.  And by extension, we’ve never been so dependent on the people building and operating these networks.

    Yet both networks and communication service providers (CSPs) are under stress.  Core networks are quite resilient and tend to be robustly engineered with the capacity to accommodate sustained surges in demand.  Edge networks, on the other hand, particularly where there is a legacy copper plant and coax, are often running hot.  No doubt, this is leading to frustration among many as their streams of Tiger King drop packets.  Wireless networks have seen changes in usage patterns and subscriber locality, double-digit increases in peak traffic, and some erosion in average data speeds in many regions.  Broadly speaking, mobile operators have been handling the workload without significant impacts on customer experience.  This is a testament not just to good planning, but to the ongoing work of network professionals who are monitoring and continually optimizing network performance.

    On the business side for CSPs, it’s been a mixed bag.  Demand for consumer services is holding relatively steady.  However, the overall economic climate will lead to an erosion of business services revenue.  Before the crisis, Analysys Mason was projecting a modest 0.7% increase in telecoms revenue.  Their revised forecast predicts a 3.4% decline across developed markets in 2020.  Despite this, operators are adding workers to their rolls to handle frontline services and are increasing salaries for essential workers in some situations. CSPs are stepping up in many cases to offer deferred payment plans, increased data caps, and contributions to various charitable causes.

    As quoted in the Financial Times, Hans Vestberg, the CEO of Verizon, said, “staff who have continued to work in the field to support customers — be they engineers or shopworkers — deserve credit. Those are the heroes.”

    I could not agree more.  Certainly, we all owe a big dose of gratitude to the frontline workers who are staffing health care facilities, delivering groceries, and looking after public safety (among so many other essential services).

    At the same time, frontline workers in the communications industry continue to be dispatched to customer locations (wearing PPE!), enter manholes, climb towers, and staff the few retail outlets that are open.  Behind them are network professionals who are staffing NOCs, data centers, and war rooms to provide critical operations support.  They are so essential that CSPs in India are making temporary living arrangements for them near telco facilities. No doubt, such accommodations are being made worldwide, with communications workers rising to the challenge of keeping global networks running.

    So here’s a shout-out to the Network Heroes!

    To understand the challenges faced by CSPs in offering quality video content over mobile devices

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  • A través de los ojos de un profesional de redes, cómo lidiar con la tormenta de incertidumbre

    A través de los ojos de un profesional de redes, cómo lidiar con la tormenta de incertidumbre

    El mundo de las telecomunicaciones está lleno de incertidumbre. Es durante estas situaciones de incertidumbre cuando el aumento de la demanda de los clientes y la carga en la red aumenta exponencialmente, el verdadero papel de un profesional de la red entra en juego. Durante estos momentos, cuando la red está bajo tensión, los profesionales de la red deben hacer un gran esfuerzo para garantizar la continuidad del servicio 24 × 7.

    Pero, ¿qué pasa con la “incertidumbre” que podría surgir de una calamidad natural, o interrupciones masivas del sitio, causa estrés en la red?

    En general, una red móvil sufre numerosos cambios. Estos podrían ser cambios planificados como ajuste de parámetros, optimización física, prueba / implementación de nuevas funciones, actualizaciones de software, etc., la mayoría de los cuales están dirigidos a mejorar el rendimiento de la red y la experiencia del usuario final.

    Sin embargo, por otro lado, son los cambios “no planificados” los que tienden a arrojar una llave inglesa al sistema. Estos cambios podrían ser estacionales o debidos a fluctuaciones a nivel del sistema en los elementos de la red que provocan una degradación severa en términos de rendimiento. Si tal incidente ocurriera en la hora más ocupada del día, entonces el impacto también se sentirá en el negocio.

    En ambos casos, planificados o no planificados, la acción para resolver un problema podría estar dirigida a cualquier dominio de una red, por ejemplo, radio, transporte, núcleo, etc. También requeriría la resolución de problemas de extremo a extremo y el análisis de la causa raíz.

    Tradicionalmente, los profesionales de la red manejan el impacto de tales cambios de manera reactiva, lo que a menudo resulta en casos en los que el “tiempo medio para reconocer” (MTTA) y el “tiempo medio para responder” (MTTR) potencialmente toman horas o, en algunos casos, días. De hecho, algunos casos complejos entre dominios pueden incluso quedar sin resolver.

    Sin embargo, a lo largo de los años, gracias a los avances en la automatización, los profesionales de la red han podido evolucionar hacia el manejo proactivo de dichos problemas. Al aprovechar las herramientas integradas con la experiencia en la materia, junto con los métodos estadísticos, los profesionales de la red pueden lograr una reducción significativa en MTTA y MTTR. Sin embargo, este enfoque está lejos de ser perfecto, ya que las acciones necesarias solo se pueden tomar después de que el impacto en la red ya haya ocurrido.

    Para comprender el enfoque correcto que debe adoptarse, demos un paso atrás y profundicemos en la vida de un experto en planificación y optimización de redes involucrado en la gestión del rendimiento diario de la red.

    Convencionalmente, un profesional de la red realiza un análisis del impacto de la red en un conjunto de KPI críticos y toma las medidas necesarias para mejorar el rendimiento de la red. Se invierte mucho esfuerzo en otra ronda de análisis para concluir si la acción tuvo un impacto en cascada en cualquier otro KPI importante o no. Una vez que el impacto de la acción tomada se considera exitoso, el análisis pasa al siguiente conjunto de acciones. Muy a menudo, tales acciones son tomadas por múltiples partes interesadas para mejorar el desempeño de sus respectivas regiones / grupos / sitios.

    En una red 4G, podría haber miles de contadores / KPI y cientos de parámetros, lo que hace que sea casi imposible para un ser humano analizar el impacto de un cambio de un conjunto de parámetros en todos los contadores / KPI relevantes, de extremo a extremo, diariamente. Los servicios como la transmisión de video y VoLTE a menudo requieren un análisis entre dominios para garantizar la continuidad del servicio.

    Es en tales escenarios donde Machine Learning (ML) comienza a surgir como la respuesta para manejar estas complicaciones desde una perspectiva de extremo a extremo.

    Sin embargo, el término “Aprendizaje automático” tiene una forma de introducirse en tales debates. Es, de hecho, casi en su propio detrimento a veces, ya que el aprendizaje automático casi se ve como un trabajo de magia, en el mejor de los casos, o simplemente un truco de marketing, en el peor de los casos. Casi todos lo mencionan en algún momento.

    Entonces, permítame tomarme un momento para defender por qué lo menciono ahora y por qué creo que el análisis avanzado basado en el aprendizaje automático puede resolver significativamente algunos de los problemas críticos que enfrentan los profesionales de la red.

    La respuesta corta de dos palabras sería “Agilidad” y “Precisión”. Permítanme dar más detalles.

    Un CSP puede aprovechar los modelos de ML que capturarán todos los tipos de impactos de red de manera consistente a partir de los datos de red generados en forma de contadores / KPI y parámetros a diario y ayudará a proporcionar información procesable a los profesionales de la red, que de lo contrario pasarían desapercibidos.

    Además, estos conocimientos son especialmente útiles cuando se utilizan en la planificación y gestión de eventos especiales, como la proyección de un evento deportivo mundial. En tal caso, donde se espera que aumente el aumento del tráfico, el modelo aprenderá del desempeño del evento anterior y proporcionará aportes críticos para la planificación del evento.

    Según la misma lógica, los modelos de ML generarán ideas procesables cuando se produzca una crisis y equiparán a los profesionales de la red para hacer frente a cualquier evento anormal que ocurra en tiempos de incertidumbre. Además, al aprovechar los modelos ML, los profesionales de la red pueden estar equipados para ejecutar simulaciones múltiples para realizar con precisión el análisis de impacto de la red para cualquier acción que tengan la intención de realizar en el futuro. Algunos escenarios clave que podrían gestionarse con mayor agilidad en cualquier momento, pero que adquieren una dimensión adicional de importancia durante una situación de “red bajo tensión”, serían:

    • Pronosticar con precisión el tráfico peak, la utilización de recursos y el uso de aplicaciones para planificar con anticipación
    • Optimización y ajuste de los parámetros de red para la expansión de la capacidad y la mejora de la calidad en tiempos de tendencias anormales de la red.
    • Mejora de la experiencia del cliente al realizar una correlación de calidad de experiencia (QoE) con QoS de extremo a extremo para servicios de transmisión como video Full HD.
    • Mantener la continuidad continua del servicio VoLTE de extremo a extremo.

    Como dicen, las situaciones únicas requieren medidas únicas, y durante los momentos en que la red está bajo estrés, los profesionales de la red deben hacer un gran esfuerzo solo para mantener la red a flote. Es un esfuerzo que a menudo pasa desapercibido, pero ayuda a mantener a los clientes conectados y a mantener intacta la reputación de la empresa. Machine Learning puede asumir un papel esencial en la transformación de estos “héroes de la red” en “superhéroes de la red” al equiparlos con poderes de agilidad y precisión mejoradas.

    La verdad es que la industria de las telecomunicaciones se encuentra en un punto de inflexión en el que los responsables de la toma de decisiones deben tomar una decisión sobre las capacidades que deben aportar a la red y cómo pueden equipar a sus héroes de red hoy en día. Estas decisiones críticas tomadas ahora proporcionarán beneficios en los próximos años.

    Para comprender los desafíos que enfrentan los CSP para ofrecer contenido de video de calidad a través de dispositivos móviles

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  • How to ride the storm of uncertainty through the eyes of a network professional

    How to ride the storm of uncertainty through the eyes of a network professional

    The telecom world is one that is fraught with uncertainty. It is during such situations of uncertainty when the surge in demand from customers and load on the network increases exponentially, the true role of a network professional comes into the fray. During these moments, when the network is under stress, network professionals need to put in a tremendous effort to ensure 24×7 service continuity.

    But what about ‘uncertainty,’ that might emerge from the likes of a natural calamity, or massive site outages, causes stress on the network?

    Generally, a mobile network undergoes numerous amounts of changes. These could be planned changes like parameter tuning, physical optimization, new feature trial/implementation, SW upgrades, etc., most of which are targeted towards improving the network performance and end-user experience.

    However, on the other side, it is the ‘unplanned’ changes that tend to throw a monkey wrench into the system. These changes could be seasonal or due to system-level fluctuations in network element(s) bringing severe degradation in terms of performance. If such an incident was to happen in the busiest hour of the day, then the impact will be felt in the business as well.

    In both the cases, planned or unplanned, the action to resolve a problem could be targeted towards any domain of a network, e.g., radio, transport, core, etc. It would also require end-to-end troubleshooting and root-cause analysis.

    Traditionally, network professionals manage the impact of such changes reactively, which often results in instances where the ‘mean time to acknowledge’ (MTTA) and ‘mean time to respond’ (MTTR) potentially take hours or, in some cases, days. In fact, some complex cross-domain cases can even remain unresolved.

    Over the years, however, thanks to advancements in automation, network professionals have been able to evolve towards handling such issues proactively. By leveraging tools embedded with subject matter expertise, coupled with statistical methods, network professionals can bring a significant reduction in MTTA and MTTR. However, this approach is far from perfect as necessary action can only be taken after the impact on the network has already occurred.

    To understand the right approach that needs to be taken, let us take a step back and delve into the life of a network planning and optimization expert involved in day-to-day performance management of the network.

    Conventionally, a network professional performs network impact analysis on a set of critical KPIs and takes necessary steps to improve the network performance. A lot of effort is invested towards another round of analysis to conclude if the action had a cascading impact on any other major KPIs or not. Once the impact of the action taken is deemed successful, the analysis moves to the next set of actions. Very often, such actions are taken by multiple stakeholders to improve the performance of their respective regions/clusters/sites.

    In a 4G network, there could be thousands of counters/KPIs and hundreds of parameters, making it almost impossible for a human to analyze the impact of a change of a set of parameters on all relevant counters/KPIs, end-to-end, daily. Services like video streaming and VoLTE often require cross-domain analysis to ensure seamless service continuity.

    It is in such scenarios where Machine Learning (ML) starts cropping up as the answer to handling these complications from an end-to-end perspective.

    However, the term ‘Machine Learning’ does have a way of creeping into such discussions. It is, in fact, almost to its own detriment at times, as machine learning is almost looked at as a work of magic, at best, or just another marketing gimmick, at its worst. Almost everyone brings it up at some point.

    So, let me take a moment to defend why I bring it up now, and why I believe machine learning-driven advanced analytics can significantly solve some of the critical problems faced by network professionals.

    The short two-word answer would be ‘Agility’ and ‘Accuracy.’ Allow me to elaborate.

    A CSP can leverage ML models that will capture all types of network impacts consistently from the network data generated in the form of counters/KPIs and parameters on a daily basis and will help in providing actionable insights to the network professionals, which would otherwise go unnoticed.

    Furthermore, these insights come in especially handy when used in planning and managing special events like the screening of a global sports event. In such an event, where the surge in traffic is expected to rise, the model will learn from the previous event’s performance and will provide critical inputs for event planning.

    By the same logic, ML models will generate actionable insights when a crisis strikes and will equip network professionals to deal with any abnormal events happening in times of uncertainty. Furthermore, by leveraging ML models, network professionals can be equipped to run multiple simulations to accurately perform network impact analysis for any actions they intend to perform in the future. A few key scenarios that could be managed with better agility at any point of time, but take on an additional dimension of importance during a “network under stress” situation, would be:

    • Accurately forecasting the peak traffic, resource utilization, and application usage to plan well in advance
    • Optimizing and fine-tuning network parameters for capacity expansion and quality improvement in times of abnormal network trends
    • Improving customer experience by performing Quality of Experience (QoE) correlation with end to end QoS for streaming services like full HD video.
    • Maintaining seamless end to end VoLTE service continuity.

    As they say, unique situations call for unique measures, and during times when the network is under stress, network professionals need to put a whole lot of effort just to keep the network afloat. It is an effort that often goes unnoticed, but it is one that helps keep customers connected and the company’s reputation intact. Machine Learning can take on an essential role in transforming these ‘network heroes’ into ‘network superheroes’ by equipping them with powers of enhanced agility and accuracy.

    The truth is that the telecom industry is at an inflection point where decision-makers need to take a call on what capabilities they must bring into the network and how they can equip their network heroes today. These critical decisions taken now will provide benefits for years to come.

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  • From 4G to 5G, keys to digital transformation

    From 4G to 5G, keys to digital transformation

    The journey of digital mobile communications began with the inception of 2G technology in the 90s. In retrospect, the technology evolved multi-fold, mainly due to the promising growth the industry witnessed. One of the prime reasons for evolution was the constant progression of technological enhancement to overcome legacy technology challenges. One such challenge that has served as an impediment for a smooth technological transformation has been the need to do a hard transition from one standard to the next. I personally witnessed it as the technology evolved from 2G to 3G, as well as 3G to 4G, throwing multiple challenges to network professionals as we sought to ensure service continuity while maintaining user experience targets.

    More recently, with the inception of 5G technology, the approach of network standard replacement has vanished away.  Early adoption of 5G technology doesn’t need 4G replacement. As a matter of fact, it gradually builds upon the LTE network. Let’s get into a little depth on this:

    • 5G technology primarily has two deployment strategies, refer to figure 1. Non-Standalone (NSA) is the first version currently being used by all existing 5G networks. NSA has its own limitations when compared to full standalone or SA, the other version of 5G.
      Setting aside the limitations and noting the fact that the time span taken for complete transition of previous generation technologies was quite high, I see the introduction of NSA deployment strategy as a good pragmatic move. 5G NSA lays a strong foundation to prevent such a repeated flaw in standards development, facilitating smooth transition from 4G to the full 5G SA standard.

    standalone deploymentFigure 1: Non-standalone and standalone deployments

    In practical terms, the early implementations of 5G will use a combination of 4G and 5G technologies to function. This combination will evolve across geographies and the ratio will vary across the regions, depending mainly upon the availability of spectrum and CSPs’ digital transformation strategies.

    • The latest GSMA mobile economy 2019 report predicts that by 2025, 4G will remain the predominant technology with a market share of 59%. 5G will continue to grow but will only cover a market share of 15% by that time. The fact that 4G will remain the dominant technology by 2025 is not surprising, considering 3GPP standards for 4G is being developed and enhanced to LTE-advanced pro, which is often termed 4.9G (or the more dramatic “Road to 5G”).

    This will lead us to a very interesting phase in the mobile communication industry and eventually to a very important question: “How should a CSP plan for 5G networks or most importantly how should it plan the transition from 4G to 5G technology?”. Frankly, there is no fool-proof guiding principle but there are some real-world use cases that I would like to take you through.

    • Case 1: Rakuten network in Japan will not initially offer 5G services. Instead, it will leverage its 4G LTE network with macro and small cells. Most interestingly, it will be fully virtualized from the radio access networks (RAN) to the core, with end-to-end automation for both network functions and services. This approach is intended to construct a network quickly and cost-effectively, which should put them in the pole position when it comes to launching and monetizing 5G. Obviously, Rakuten has had the luxury of being a greenfield operator, however, there are still some key lessons that other CSPs can take from their approach. These will come handy towards balancing the Capex lift of 5G while fighting an uphill ARPU battle.
    • Case 2: US operator AT&T, from the current state of 5G deployments, is encouraging the upgrade of 4G networks to its most advanced versions. LTE advanced and LTE advanced-pro have been available for some years now and CSPs like AT&T in the US are referring to it as “5G Evolution” (5GE), even though it is most definitely not an official 5G standard, but rather the latest version of 4G.

    Each of the above cases demonstrates that consolidating the existing LTE network and gradually making it 5G ready may be the most optimal and cost-effective way of evolving from 4G to 5G. Moreover, as the technology itself provides this smooth and seamless transition capability, CSPs should leverage it.

    The bottom line is, while 5G clearly points the way forward, it’s the foundation of 4G that will be a critical part of its planning, operation, and success. Additionally, the strength of the current 4G foundation depends on how seamlessly and smoothly content on LTE networks (primarily data-driven) is delivered to the end-users. Mobile users want both smooth experiences and obvious incremental improvements in network speeds to feel satisfied with network performance. Evolving networks toward 5G is crucial to keeping up with the appetite for higher speeds and quality user experience.

    Subex network analytics offerings take into account the key aspects of digital transformation and help a CSP consolidate its current 4G LTE network and plan for 5G network in a most cost-effective way with focus on enhancing the customer experience and maximizing returns on network investments.

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  • Network Capacity Management and how AI/ML can improve Network Investment Planning

    Network Capacity Management and how AI/ML can improve Network Investment Planning

    The telecom world as we know it is constantly evolving, with technology innovation and enhancements coming on a regular basis. And today, with mobile technology evolving from 4G/LTE to 5G, there is an evolution in terms of customer experience demand and competition landscape as well. This puts CSPs in a precarious position to deliver the best experience to their consumers while keeping costs low, and competitive advantage intact.

    This is where Intelligent Network Capacity Management comes in:

    What CSPs need today are network capacity management solutions that can significantly help them enable smart network investment planning to maximize return on network investments.

    However, in reality, this is far from as straightforward.

    Strategically, CSPs leverage capacity management investments to gain competitive advantage and enhance end-user experience. However, such investments are highly cost intensive, both in terms of Capex and Opex. Any inaccuracies in the overall network investment planning can lead to unintended impacts on company financials and can create significant network inefficiencies.

    How do CSPs come up with network investment plans which are accurate, has optimal cost, meets current & future requirements of users and gives competitive advantage over a long term?

    In our view, the overall Network Investment Plan has 3 prime enablers which can help CSPs to formulate an efficient and robust Network Capacity Management framework comprising of: Network Capacity Assessment, Network Capacity Planning, Network Capacity Optimization, Network Capex Optimization, and a lot more.

    Key Enablers:

    • End to end domain expertise: CSPs with capability to proactively manage end-to-end network would ensure high flexibility, reliability and availability of network service continuity. Deep network domain and service expertise to address broad set of use cases deployment fitting across CSPs key business objectives will ensure superior network and business performance.
    • Strong data management capability: As per GSMA mobile economy report, by 2025 around 130 exabytes of data would be generated every month, four times increase in volume as compared to current data volume, which is around 30 exabytes per month. Seamless handling of such a massive amount of data would ensure CSPs to efficiently extract relevant and meaningful insights at the right time for analysis.
    • Leveraging advanced analytics: Complexity of running a 4G and 5G network with plenty of data points in IoT will make it impossible for human hands for network planning, capacity management, quality optimization and predictive capacity analytics to enhance the customer experience. CSPs leveraging machine learning and deep learning models today will reap significant benefits in their overall network management, an ecosystem comprising of 2G, 3G, 4G and 5G technologies.

    The three major enablers are quite significant in today’s digital era and will be extremely crucial in the 5G ecosystem. Delivering extraordinary customer experiences, staying ahead of competition and most importantly being profitable should be part of every CSPs strategy for evolution towards 5G technology.

    Predictive network management modules considering the key aspects of digital transformation can help CSPs consolidate current 4G LTE network and plan for 5G network in a most cost-effective way with focus on enhancing the customer experience and maximizing returns on network investments.

    Read a case study on how we helped a Tier-I telcos increase their revenues by over 12% through intelligent network investment planning.

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    This blog was originally published on Whatech