Artificial Intelligence in Telecommunications

Rajesh Uppal May 26, 2023 AI & IT, Comm. & NW Comments Off on Artificial Intelligence in Telecommunications 110 Views

Artificial Intelligence technologies aim to develop computers, or robots that match or exceed the abilities of human intelligence in tasks such as learning and adaptation, reasoning and planning, decision-making and autonomy; creativity; extracting knowledge, and making predictions from data. This technology focuses on developing an intelligent machine with advancements in features such as speech recognition, visual recognition, picture identification, and language translation, all of which are driving market growth.

It has a wide range of applications in telecommunications, including customer service and network performance. Artificial intelligence allows the telecom industry to draw insights from large data sets, making it easier to manage daily operations, handle issues more quickly, and increase customer service and happiness.

The essence of communication is to pass the user information to the destination from starting point through various communication technologies (such as Mobile Communication, Satellite Communication, Fixed Network Communication, etc.). The quality of communication is measured by whether the information can be reproduced accurately and perfectly from the transmitter end to the receiver end. Compared with the natural intelligence of mankind and animals, AI makes computers or machines simulate human thinking and cognition, such as the capacity of “learning” and “problem-solving”, perceive the environment and take the corresponding actions to smoothly achieve the presupposed objectives with the maximum probability.

AI absorbs knowledge from data and makes decisions through deep learning in AI, without explicit mathematical modeling and analysis. The communication system easily breaks away from the actual condition in practical application in case of excessively exquisite and elegant mathematical assumptions. In turn, AI or deep learning, if applied to communication systems, has an excessively black-box learning process, it easily causes the communication and information model construction lacks physical meaning.

The transmitter and receiver of signal processing system can be decomposed into different processing units which are responsible for their respective functions, such as information encoding and decoding, channel encoding and decoding, signal modulation and demodulation, etc., very similar with the micro-service concept in current IT system.

AI in Mobile Communications

Mobile communications systems have evolved through wireless technology innovation into 2G, 3G, and then 4G to keep pace with ever-increasing voice and data traffic. Every generation of wireless technology brought many improvements including speed enabling many new applications. 1G was analog cellular. 2G technologies, such as CDMA, GSM, and TDMA, were the first generation of digital cellular technologies. 3G technologies, such as EVDO, HSPA, and UMTS, brought speeds from 200kbps to a few megabits per second. 4G technologies, such as WiMAX and LTE, have scaled up to hundreds of megabits and even gigabit-level speeds.

5G, short for 5th generation mobile networking or 5th generation wireless systems is the latest iteration of cellular technology that will provide seamless coverage, high data rate, low latency, and highly reliable communications. Part of the 5G spec allows 5G phones to combine 5G and 4G channels invisibly and seamlessly to the user. Initially, all 5G networks used 4G to establish their initial connections, something called “non-standalone.” We’re starting to move away from that now into “standalone” networks.

As mobile communication network becomes more complex and communication services become
more diverse, communication network infrastructure and service system need to confront many complex scenarios, including very complicated wireless environment which can’t be simulated with data model, exponential increasing complexity of IP switching and route selection, active network support and service guarantee, customized network service of “one customer with one strategy” and “one moment with one strategy”, etc.

The rapid convergence of 5th-generation mobile communication technology (5G) and AI is beginning to significantly transform the core communication infrastructure, network management, and vertical applications. All of them far outperform the processing and management system, which is predefined and executed by traditional artificial rules. Therefore, the current communication system needs a set of automatic and intelligent systems and methods to guarantee the operation and development of networks and services.

AI in Mobile Network Infrastructure

The development of AI in communication network infrastructure is expounded in four aspects: Radio Access Network, Core Network, Transport Network, and Terminal.

Radio Access Network

The physical carrier of radio access network is base station. 5G base station is divided into the central unit (CU) and distributed unit (DU), which are similar as traditional baseband unit (BBU), connects active antenna unit (AAU) through optical fiber. AAU contains traditional Remote Radio Unit (RRU) and antenna function, namely the active RF part and the passive antenna. AI is applied in the physical layer, MAC layer and network layer through intelligence functions oriented to CU, DU and AAU in radio access network

In respect of the physical layer and data link layer, the typical AI application includes the evaluation and prediction of the function of channel quality, orthogonal frequency division multiplexing (OFDM) symbolling, received signal detection, channel encoding and decoding, random access of dynamic frequency spectrum, etc.

The recent advances in communication network technologies, proliferation in the number of connected devices, and growing multimedia applications are leading towards a flourishing expansion in the data generation. The radio communication networks are not only the carriers but also a leading source of generation of data. Appropriate exploitation of big data analytics has a strong potential in facilitating the improvement in the performance of the communication systems as well as in maximizing the revenue generation opportunities for the stakeholders.

The number and variety of 5G links will increase by 100X over those supported in current 2G, 3G and 4G networks. This will both complicate network management, anomaly/ fault detection and optimization. The role of machine learning and advanced analytics will vastly increase in 5G networks.

The data-aware intelligence for extracting useful information from the data and enhancing the network performance for IoT applications have been discussed. Also, a collaborative processing framework while combining the benefits of edge and cloud computing for live data anaytics in IoT networks has been proposed. Along with the benefits offered by big data analytics, there are also various critical concerns being raised regarding the ethics of the analytics

The heterogenous nature of future wireless networks comprising of multiple access networks, frequency bands and cells – all with overlapping coverage areas – presents wireless operators with network planning and deployment challenges. Machine Learning (ML) and Artificial Intelligence (AI) can assist wireless operators to overcome these challenges by analyzing the geographic information, engineering parameters and historic data to:

Forecast the peak traffic, resource utilization and application types
Optimize and fine tune network parameters for capacity expansion
Eliminate coverage holes by measuring the interference and using the inter-site distance information

One of core values of 5G is the dedicated application for to-B enterprise. It can be predicted that, in the next decade, telecommunications AI will help enterprises achieve advance intelligent even completely intelligent private network function in vertical industry applications, such as Internet of Vehicles, Intelligent Manufacturing, High-definition Video /VR/AR, Telemedicine and Smart City.

Enhancing customer experience

AI can be used by telecom service providers to elevate customer experience at scale. From service and plan management to technical support, AI, specifically Conversational AI, can be used to deliver human-like conversations between the telecom company and their customers. Because AI can handle an unlimited volume of calls, customers will never have to wait to speak with an agent, but they will still receive top-notch customer service.

With the ability to seamlessly integrate into existing business systems, Conversational AI can also provide a personalized experience for customers so they do not have to repeat information that is already known by the company. This personalization also supports the ability to deliver proactive alerts about payment, product usage, and promotional offers.

Conversational AI can be used in telecommunications to provide consistent, quality customer service at scale. For example, with an Interactions Intelligent Virtual Assistant (IVA) powered by Conversational AI, customers can interact with the IVA just as they would a human.

This allows for an unlimited volume of calls to be handled, without increasing the need for more full-time agents. This is particularly useful to help handle seasonal volume demands or unpredictable volume increases.

However, there is always going to be a time in customer care when an actual human is needed to assist the customer. In this case, AI can be used to provide a seamless transition to a live agent so that the customer does not have to repeat any information that they have already said to the IVA

How does AI telecom customer service help businesses and customers?

AI can be used in telecom customer service to not only provide excellent customer care for customers, but also deliver extraordinary financial benefits to businesses.

The service for customers starts with providing effortless and productive conversations at scale. This means when a customer calls about a billing question or to update their phone plan, they will not have to wait on hold because the AI application can handle unlimited volume of calls. And once the customer is conversing with the AI application, they will not have to repeat any information or “robot speak” in order to be understood. This allows for telecom companies to deliver human-like customer service interactions at scale.

Using AI can also result in financial benefits for telecommunications businesses. First off, by providing a better customer experience, companies will create more loyal customers and more customer retention. Secondly, with its ability to handle an unlimited number of calls, an AI customer service application prevents the need to hire additional agents to handle extra call volume during peak seasons.

Business Operation

From the perspective of business operation, the telecommunication operator has basically accomplished end-to-end digitalization upgrade of the whole process, and is implanting big data, AI, etc. into the existing process to accomplish the intelligentization of business processing process and further improve business operation efficiency. With the introduction of RPA, intelligent business process management suites (IBPMS), etc., it is predicted that the business process with manual intervention will be further decreased, process operation efficiency will be further improved, and the cost will be further reduced in the future.

As AI is introduced to risk control system of the operator, the income guarantee capacity is further improved, and the arrear risk will be further reduced. The operators can combine own predicted risk control to carry out more innovative businesses, which will further promote more healthy and upward cash flow of telecommunication operator.

Artificial intelligence in the telecommunication market

The global artificial intelligence in telecommunication market size is projected to be valued at US$ 918.6 Mn in 2022 and is anticipated to reach US$ 10,399.9 Mn by 2032, with a rapid CAGR of 27.5% from 2022 to 2032.

The adoption of 5G technologies in mobile networks, as well as the growing demand for effective and efficient network management solutions, have been driving the artificial intelligence in the telecommunication market growth. Increased AI-embedded smartphone penetration and the increased acceptance of AI solutions in various telecom applications are expected to boost the demand for artificial intelligence in telecommunication.

Artificial intelligence in the telecommunication market is gaining traction and popularity as maintenance of the telecom network became the first priority for telecom companies. A network failure reveals the company’s lack of honesty and disregard for its clients. Network failure also results in financial losses for the company.

As a result, AI is being employed to solve this issue. The demand for artificial intelligence in telecommunication is rising as telecom companies can rapidly pinpoint the problem with AI. Finding the first place where maintenance is required takes up the majority of network maintenance time. It has become simple with the availability of AI. Furthermore, telecom companies are leveraging IoT, which is driving the adoption of artificial intelligence in telecommunication.

Latest trends in artificial intelligence in telecommunication market includes the development of context-aware AI systems which are clever and can swiftly determine their state. To make decisions, these systems use the observe-orient-decide-act model. As a result, the demand for artificial intelligence in telecommunication is anticipated to rise during the forecast period.

Downtime can be reduced with AI, which is another factor propelling artificial intelligence in telecommunication market growth. Furthermore, by utilizing context-aware technologies and IoT processes, maintenance work may be completed swiftly.

Drones are being used by many businesses to perform network maintenance. Comarch is one such company that uses AI-enabled drones to provide solutions for telecom network maintenance. To address various support requests for installation, maintenance, and troubleshooting, telecom vendors typically deploy AI for customer care apps like chatbots and virtual assistants. Moreover, telecom companies are implementing AI to improve customer experience which is projected to boost the adoption of artificial intelligence in telecommunication.

Telecom service providers develop advanced AI and machine learning technologies that can estimate network traffic for any region. AI system outputs are fairly accurate, and organizations utilize them to optimize network performance. The demand for artificial intelligence in telecommunication is anticipated to rise as service providers have free access to use data for any area, which they can utilize to their advantage.

Furthermore, the artificial intelligence in telecommunication market share is projected to grow as network performance can be improved by expanding a tower’s capacity and range during peak hours in high-traffic areas. It can also be reduced subsequently to accommodate lower traffic levels.

AI can be used to regulate network performance in the same way that a remote-controlled device can. Self-organizing network technologies are used by many telecommunication companies, including AT&T and other telecom heavyweights. The adoption of artificial intelligence in telecommunication is likely to grow as these AI solutions can operate well in high-traffic areas.

Key Artificial Intelligence in Telecommunication Market Dynamics?

The adoption of artificial intelligence in telecommunication is growing as telecom companies use AI in many aspects of their operations, including enhancing customer satisfaction and network stability. The demand for artificial intelligence in telecommunication is projected to rise as AI is primarily used in customer service applications by telecom firms.

Furthermore, use of chatbots and virtual assistants to handle a huge volume of installation, maintenance, and troubleshooting support queries are some of the key trends in the artificial intelligence in telecommunication market. Additionally, virtual assistants scale and automate responses to support requests, enhancing customer satisfaction and lowering costs for businesses. As a result, artificial intelligence in telecommunication market size is anticipated to grow through 2032.

End users, system integrators, and vendors of AI solutions make up the telecommunication value chain. The demand for artificial intelligence in telecommunication is anticipated to rise as machine learning and computer vision applications require a variety of technological tools, infrastructural facilities, and data formats to be trained and implemented. Machine learning (neural network) training tools are offered by organizations like the IBM Corporation, Microsoft, and Intel Corporation, among others.

In the artificial intelligence value chain, infrastructure developers and system integrators are key contributors. OEMs select a suitable system integrator with the necessary skills, resources, and knowledge to offer full customer support and preventative maintenance.

The application industries can gain from a service contract with manufacturers in terms of design, integration, and delivery. In the artificial intelligence in telecommunication market, factory-trained field technicians help with installation and use of these products. Additionally, manufacturers offer value-added services such programs for remote monitoring and self-diagnostics.

Governments all over the world are accelerating the rollout of 5G as they recognize how important it is for digital transformation and demand to drive automation and artificial intelligence in the telecommunication market. The Internet of Things (IoT), which is powered by edge computing and AI, and digital transformation are likely to be fueled by fifth-generation technologies across the wireless communication industry. The adoption of artificial intelligence in telecommunication is probably driven by rising consumer demand for better services and a smooth customer experience.

Growing 5G rollouts are probably going to open up significant growth opportunities for telecom operators to provide corporations process automation services powered by edge computing and AI as well as outsourced IT services. It is anticipated that a large number of well-known telecom service providers, such as Charter Communications, AT &T, and Verizon, are likely to spend heavily on AI.

Market Segments

The market for artificial intelligence in telecommunications can be divided into three categories: components, applications, and geography. The AI in telecommunications market can be divided into solutions/platform and services based on components. Professional and managed services are two types of services that can be found among them.

Cloud-based and on-premise solutions/platforms are sub-segments of the solutions/platforms segment. Predictive maintenance, fraud mitigation, cybersecurity, and intelligent CRM systems are some of the applications of AI in the telecommunications business. Customer analytics, network operations management, and marketing virtual digital representative are some of the other applications available.

During the projection period, customer analytics is predicted to have the greatest market share. This is mostly due to the telecom industry’s increased demand for customer analytics to evaluate customer data, which aids in sales planning and strategies.

Which Segment is Likely to Lead the Artificial Intelligence in Telecommunication Market by Application?

Due to the increased demand for real-time behavioral insights, the customer analytics category is likely to have the largest market share with a valuation of US$ 2,294.4 Mn by 2032. Artificial intelligence enables operators to collect and evaluate consumer data from the perspective of a subscriber intelligencer. This information can then be used in a variety of circumstances, including adverts and personalized offers for the subscriber. This information can also be utilized by network operators to optimize network consumption.

As customer service automation generates significant savings for telecom firms, the virtual assistance segment is predicted to grow the fastest at a CAGR of 24.5% during the forecast period. Furthermore, customer service chatbots in the communication business can be effectively educated since machine learning algorithms can automate questions and send clients to the most appropriate person.

Regional Outlook

The AI in telecommunications market may be divided into five regions: the Middle East and Africa, Asia Pacific, North America, South America, and Europe. In the global AI in telecommunications industry, North America is predicted to have the greatest market share. In North America, artificial intelligence in telecommunications is successfully employed for a variety of applications, including network security, network optimization, and virtual support.

Europe is estimated to dominate artificial intelligence in telecommunication market by 2032 with a CAGR of 25.7% during 2022-2032. European telecom operators have been spotted prioritizing investments in order to make better data-informed judgments. According to a report issued in February 2022 by the European Telecommunication Network Operators’ Association (ETNO), Europe recorded its largest investment in 5G and Fiber to the Home (FTTH) networks in 2020, with capital expenditure (Capex) of around 72.21 billion.

Such large investments are likely to increase artificial intelligence (AI) adoption throughout the European telecom industry. On the contrary, the European telecom industry is highly regulated, which is expected to lead to consistent market expansion throughout the area.

What is North America Artificial Intelligence in Telecommunication Market Outlook?

North America is projected to account for the second largest share with a value of US$ 1,802.7 Mn by 2032. It is predicted to retain its dominance throughout the projection years due to the region’s status as an early adopter of modern technology. Furthermore, the region’s growth will be aided by an increasing number of telecom companies that use automation and artificial intelligence for customer service and network efficiency.

What is the Growth Outlook of Middle East and Africa (MEA) Artificial Intelligence in Telecommunication Market?

Middle East and Africa artificial intelligence in telecommunication market is anticipated to be valued at US$ 419.6 Mn and grow at a fastest CAGR of 31.1% during 2022-2032.

For telecom service providers, the Middle East and Africa are considered to be potential markets. In recent years, the area has emerged as a trailblazer in sophisticated networking, with Qatar, Saudi Arabia, Bahrain, and the UAE developing 5G networks. Middle Eastern telecom key players Zain Group, STC Group, Etisalat Group, du, and Mobily agreed to work together in July 2021 to adopt Open Radio Access Network (Open RAN) technologies on their existing networks. Such remarkable methods are projected to fuel demand for AI solutions in the Middle East’s telecommunications industry.

Industry

Key Players in the Artificial Intelligence in Telecommunication Market: IBM Corporation, Microsoft, Intel Corporation, Google, AT&T Intellectual Property, Cisco Systems, Nuance Communications, Inc., Evolv Technology Solutions, Inc., H2O.ai, Infosys Limited, Salesforce.com, Inc., NVIDIA Corporation

Recent Developments in the Artificial Intelligence in Telecommunication Market:

Google teamed with AT&T Intellectual Property in March 2020 to assist organizations harness Google Cloud’s technology leveraging 5G network connectivity. Both firms are creating 5G solutions by merging AT&T Intellectual Property’s 5G network capabilities and Google Cloud’s strengths in analytics, AI/machine learning, and networking.
Nokia released AVA Telco AI as a Service in May 2021, which offers cloud-based artificial intelligence solutions that enables communication service providers (CSP) to automate capacity planning, network management, and service assurance.
In June 2019, Vodafone Group launched ‘TOBi,’ a machine learning chatbot that allows human customer service representatives to focus on more difficult situations.

References and Resources also include:

https://arxiv.org/ftp/arxiv/papers/2101/2101.09163.pdf

https://www.futuremarketinsights.com/reports/ai-in-telecommunication-market