5G and the Evolution of Telecom

5G and the Evolution of Telecom

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • 5G
  • Connected Intelligence
  • Sustainable Growth and Tech Trends
  • Emerging Technologies

Publication | Update: Sep 2020

According to James Andrew Lewis, CSIS Senior Vice President and Director, Technology Policy Program at the Center for Strategic and International Studies in Washington, D.C., fifth-generation mobile technologies (5G) have become one of the most high-profile domains where this competition is playing out. Each competitor has different plans for 5G. 5G will play a central role in the development of smart and self-driving cars, and all countries with an automotive industry will compete in this. Germany intends to use 5G for industrial applications as part of its Industry 4.0, and its strong manufacturing sector may give it an advantage. South Korea also benefits from having a strong app economy, but its main advantage in 5G comes from Samsung, the telecommunications equipment and semiconductor giant. Chinese companies have already created valuable consumer apps, like WeChat, and a strong developer base, and they will also pursue industrial and enterprise applications. China had an advantage in developing apps for the internet of things since its companies are the source of many of these products. But Chinese companies also face trust issues, since any Chinese-made device that connects to the internet could be exploited by Chinese intelligence agencies.

Understanding the economic benefits of 5G requires understanding where it fits in the tech ecosystem. It is important to divide the 5G market into segments and look at each segment both in terms of profit and security. There are the producers of 5G technologies, including telecom hardware—the leaders here are Ericsson, Huawei, Nokia, and Samsung. There are the companies that make the chips and software that are the essential components for telecom hardware. These are mainly American and Japanese. There are the consumer-facing networks and apps we know from our smartphones and there will be an increasing number of enterprise applications such as smart seaports and factories.

The real competition will be in writing the software applications that take advantage of 5G so that companies with access to 5G services more are profitable. Consumer applications are less important in this space—people can already watch videos on their phones and are unlikely to pay more for a slightly faster speed. In contrast, the enterprise and industrial applications that 5G can support will be the space for growth. The German firm Trumpf is developing Axoom, an industrial app designed to let companies manage smart factory solutions.

5G (and Wi-Fi) will enable connections between sensors, data, and powerful internet computing resources. Innovators can take advantage of these connections to create new services and applications. These will include new enterprise and industrial applications such as smart hospitals or factories. Self-driving cars are part of this and 5G will speed up their use. U.S. companies are strong here, but so are European and Chinese companies.

Europe and China have announced they intend to dominate 5G the way the United States dominates 4G, and U.S. companies face new competition, but success depends on making products and offering 5G products and services that appeal to the market. It is not credible to expect the nimble, well-resourced, and entrepreneurial U.S. tech sector to be squeezed out of profitable markets where they currently lead.

The initial standardization efforts of 5G technology were completed in June 2018, and preparations have begun to upgrade networks globally. While some carriers have been discussing the potential for 5G technology for three to five years, there remains a skepticism on its use cases among investors.


 Telecom companies that focus on wired solutions could see new competition from 5G wireless networks. Possible areas of competition include a broadband replacement service for consumers as well as more-flexible voice and data solutions for small businesses and branch offices of large enterprises. Other companies that could face disruption from new 5G networks include:

– Fixed broadband companies, including telecom companies but also cable operators, and alternative telecom providers could be displaced by wireless broadband solutions that can provide similar technical attributes with the benefit of mobility.

– Network equipment companies that produce technology that could be displaced, such as Wi-Fi and Ethernet, would be at risk if they do not successfully pivot to 5G.

Telecommunications technology is now going through a transition similar to the transition in computing that began 30 years ago with the introduction of the internet.

Telecom technology used to be static, changing slowly. It relied on specialized hardware. New technologies like cloud computing6 were layered on top of existing equipment and protocols. This is now changing as software-based, “open” network technologies begin to offer the same functions as conventional telecom technology. The shift has major implications for security and business as this disruptive technology can provide cheaper and more agile services using a supply chain open to any supplier.7

One way to think about this is to compare it with computer networks. At their cores, these are essentially a combination of semiconductors and software. In very simple terms, your computer connects to servers and routers that then connect to the internet. These connections work irrespective of the manufacturer, so one company can make the computer, another can make the server, a third the connecting software, et cetera.

A simplified (and arbitrary) portrayal would divide 5G networks into four parts: device, RAN, core, and cloud. An “end device” such as a mobile phone or a car, connects to a “Radio Access Network” (RAN) cell towers RAN connects devices to the telecom networks. The core of these networks use specialized routers, switches, and other packet handling technologies to aggregate and manage billions of calls. Some core processing (along with billing and other functions). Most of this is now done in the cloud (the cloud refers to managed computing resources that can be accessed over the internet). In 5G, some processing will move to the RAN, creating both opportunities and risks for security by providing hostile actors better access to data unless the 5G network is carefully designed to manage this—something that some say is impossible.

The companies that make the modular components for an open architecture telecom network involve both familiar names and new startups. Qualcomm, Intel, and Samsung make chips. Microsoft (which has built a huge 5G lab in Redmond) writes operating system software. Cisco, Sienna, Xilinx, Nokia, Fujitsu, and NEC make other essential components, as do a number of new companies, such as Altiostar, or InnoEye, and firms that are well-established in the telecom space, such as Airspan. Many of these companies along with telecom service providers have banded together in the O-RAN Policy Coalition (or other groups) to develop common approaches to the new technologies.

It is important that we do not underestimate the difficulties posed by integrating new technologies into telecom networks so as to be able to serve tens of millions of customers with the same level service they get now. One advantage of existing technologies is that they are proven to deliver this level of service.

Another is that they pose little challenge for integration, as opposed to multiple technologies developed by multiple vendors. Integration and scalability are major issues and suggest that the next generation of telecom technology is still some ways off. However, the supply chain for telecom will depend on semiconductors and specialized software.


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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

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  • Experian Economics (EE)

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As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.