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Artificial Intelligence in Telecom – Industry Adoption Analysis

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The telecom industry is at the forefront of Artificial intelligence (AI) innovation and adoption. AI offers tremendous opportunities for operators to overcome network management and optimization complexities, traditional hardware dependencies, and to reduce costs. By automating decisions around resource allocation, virtualization, traffic management, and network maintenance, AI can enable more intelligent network planning. In addition, AI will be instrumental in helping operators capitalize on 5G through better planning and network capacity utilization. Given the exploding demand for speedier and more efficient data connectivity, there’s no question that success in the telecom industry will belong to companies that best utilize the power of AI.

This report evaluates the state of AI adoption in the telecom industry, while revealing the companies that are leading the charge. By buying this report, you’ll obtain keen insights into the applications of AI in telecom, investment opportunities, market gaps, and emerging expectations from telecom companies and AI-based solution providers.

To purchase the full report, write to us at info@netscribes.com

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Artificial Intelligence in Telecom – Industry Adoption Analysis

  1. 1. SAMPLE REPORTSAMPLE REPORT 1 June 2019 ARTIFICIAL INTELLIGENCE IN TELECOM Industry Adoption Analysis SAMPLE REPORT
  2. 2. SAMPLE REPORT AI in Telecommunication SAMPLE REPORT Table of Content ▪ Overview of the Telecom Value Chain Participants ▪ Role of AI in the Telecom Ecosystem ▪ Use Case 1: AI in Network Optimization ▪ Use Case 2: AI for 5G Networks ▪ Use Case 3: AI in Cloud ▪ Use Case 4: AI Security ▪ Use Case 5: Other AI Implementations ▪ Business Model 1: AI Managed Services ▪ Business Model 2: AI Powered Chipsets ▪ Business Model 3: Data Monetization ▪ Business Model 4: Open Source Platforms ▪ Business Model 5: AI-Enabled Cloud Services Executive Summary Impact of AI in the Telecom Ecosystem Use Cases of AI in the Telecom Industry How is AI Transforming Telecom Business Models ? Overview of AI Adoption by Key Telecom Entities ▪ Adoption Status of Telecom Companies How is AI Driving M&A Activity in Telecom Sector? ▪ Technology Drivers of the Deals ▪ Technology Distribution ▪ Overview of the Acquired Technologies ▪ Prominent Acquirers in the Space ▪ Post-deal Integration Examples ▪ Netscribes Analysis and Insights ▪ Overview of the Deals How are Startups Innovating in the AI Telecom Space? ▪ Key Challenges Targeted by Startups ▪ Startups Focusing on: ▪ SON ▪ Cloud Native Networks ▪ Network Security and Analytics ▪ Interference Cancellation ▪ AI Hardware ▪ AI Assistant Insights & Recommendations ▪ Current Adoption Status of AI in Telecom ▪ Recommendations for Telecom Companies ▪ Recommendations for Other Industry Players 2
  3. 3. SAMPLE REPORT AI in Telecommunication 3 Executive Summary – (1/2) Over the years, mobile network operators (MNOs) have struggled to manage network complexities with the growing demand of higher data rates. Artificial Intelligence (AI) is …. , a primary concern in the telecom sector. Such models are thus gaining traction owing to the necessity. The initial adoption of AI can be observed for deriving intelligence related to network operations by applying machine learning (ML) algorithms to massive network data. Such solutions allow MNOs gain a competitive advantage by proactively monitoring network parameters and taking corrective actions to reduce downtime, while maintaining the network quality. With the advent of 5G, the need for………………………. …………………………………………………………………………………………………..without manual intervention. Presently, SON is a key strategy for a telecom entity to transform RAN processes. The telecommunication industry is also witnessing a shift towards cloud native architecture that harnesses cloud capabilities to build digital telecom networks. …………………………………. …………………………….. and software to eliminate traditional hardware dependencies. The road to network automation is pushing telecom companies to adopt new business models and develop core AI skills. AI-managed services, intelligent chipsets, data monetization, open source and …………………………………………………………………………………in the telecom sector. ………………………… virtualized 65.5% of its network and aims to virtualize 75% by 2020. Ciena’s ………………………………………. CTNet 2025 initiative show that all the key telecom companies are globally gearing for the future autonomous self-healing network.
  4. 4. SAMPLE REPORT AI in Telecommunication 4 Executive Summary – (2/2) M&A trends suggest that 2018 witnessed the maximum number of acquisitions in different domains, while the early deals in 2019 were majorly related to security and cloud native. Also, these two technology areas were amongst the major high value deals. The M&A transactions in the AI telecom ecosystem highlight …………………………………………………………………………………………..as the prominent acquirers. CSPs …………………………….leading acquirers. Deals by ………………………..are focused on network analytics and cloud native, respectively. Telecom operators are inclined toward AI security solutions. Telefonica’s strategic acquisition highlights a unique value proposition ……………………... While startups are ……………………………………………………………………capitalizing by investing in AI solutions for CSPs that are seeking to transform to secure digital networks. The adoption of AI/ML solutions is accelerating growth of startups that are addressing key challenges of the telecom industry. The major challenges being ………………………………………………………………………………….. Altiostar is the startup with maximum funding of USD 325 Mn providing cloud native solutions for end to end RAN virtualization. The company is working with …………………………………………………………………………………………disrupting the telecom space with self-interference cancellation technologies for full-duplex communication. In competition with Cambricon’s AI chipsets…………………………………………………………………….following a distinctive approach that includes the usage of metamaterial beamforming technologies for 5G systems. According to Netscribes’ analysis, to realize the ultimate goal of self-driving networks, MNOs, CSPs and other participants in the telecom ecosystem should consider ………………………………………………………………………………………………………….. form a strong R&D roadmap for next-generation autonomous networks. Additionally, companies from other sectors (semiconductor, IT companies, startups and investors) can also capture new revenue streams by tapping into the evolving AI telecom market. For instance, IT companies can ……………………………………………………………………….. with their cloud native expertise.
  5. 5. SAMPLE REPORT AI in Telecommunication 5 Methodology for the Industry Adoption Study This study covers the industry adoption analysis of AI technologies in the telecommunication industry. In order to understand the adoption status of AI and ML tools in the telecom environment and to assess the different opportunities available in the domain, different factors have been considered. Use Cases of AI in the Telecom Sector Transformation in Business Models Impact of AI in the Telecom Ecosystem Implementation Strategies of Key Telecom Companies Key Challenges Addressed by AI Solutions M&A Analysis for AI Technologies in Telecom Industry Role of Startups for Enabling the AI Solutions Opportunities in the AI Telecom Market Parameters Used to Assess the Adoption Status
  6. 6. SAMPLE REPORT AI in Telecommunication 6AI in Telecommunication SAMPLE REPORT 6 Impact of AI in the Telecom Ecosystem
  7. 7. SAMPLE REPORT AI in Telecommunication 7 xx Network Planning Network Optimization Network O&M ▪ Interference estimation ▪ Self-healing network ▪ Dynamic cell reconfiguration based on network parameters ▪ Real-time adaptive networks ▪ Real-time migration of services ▪ Intelligent node updation ▪ Data verification ▪ Intelligent cell site designing ▪ Dynamic resource allocation ▪ Network intelligence for improved cell coverage and capacity ▪ Traffic prediction and management ▪ Intelligent slicing ▪ Intelligent power control of network nodes ▪ Service quality management ▪ Network virtualization ▪ Monitoring of network KPIs ▪ Automated ticketing systems ▪ Failure detection ▪ Root cause analysis ▪ Network intrusion detection and elimination ▪ Alarm correlation ▪ Predictive hardware maintenance Role of AI in the Telecom Ecosystem
  8. 8. SAMPLE REPORT AI in Telecommunication 8AI in Telecommunication SAMPLE REPORT 8 Use Cases of AI in the Telecom Industry
  9. 9. SAMPLE REPORT AI in Telecommunication SAMPLE REPORT 9 Use Case 1: AI in Network Optimization AI and ML algorithms are transforming traditional networks into intelligent systems. Predictive mechanisms are enabling telecom companies take faster and better data-driven decisions. Intelligence built on huge amount of historical network data provides a pattern for network anomalies. Using AI models, telecom operators can detect network failures, forecast traffic patterns, understand traffic congestion, predict customer behavior, build intelligent security, and gain actionable insights. The intelligent networks will be built using self-organizing networks (SON) for efficient field planning and optimization. Improved customer experience Minimized truck rollsIncreased ROI Reduced maintenance cost and OPEX Reduced churn AI Overcoming the Limitations of Siloed Network Planning Operational Network Efficiency Traditional Networks Intelligent Networks Reactive approach to optimize network parameters Oriented towards hardware infrastructure Increased downtime and network failures Manual effort required for hardware maintenance Prone to breaches, attacks and other security issues Not suitable for applications requiring multi-mode capabilities Defined by technological and industrial silos Complexities in network virtualization
  10. 10. SAMPLE REPORT AI in Telecommunication 10 Use Case 2: AI for 5G Networks Network slicing is critical for the 5G-based diversified use cases and to maximize the flexibility of 5G networks. The incorporation of AI techniques can detect slicing related anomalies and faults, and also use the learning to improve slicing strategies. Additionally, AI can ensure cost effective and high quality slicing solutions. AI techniques can be used to plan 5G network deployment. The initial non-standalone architecture implementation will face multiple challenges associated with cell allocation, varied systems, multiple frequency bands and network configuration. AI and ML can be used for 5G network planning based on insights related to multi-mode coverage, cell distribution, traffic management and analysis of network parameters for better planning and increased network capacity. Neural networks and ML algorithms can be used for complex channel modeling and estimation techniques for the large scale MIMO technology. These algorithms can be applied on the beamforming parameters to adjust coverage in different scenarios. Intelligent mechanisms for interference calculation and adaptive optimization in intra-cell network deployment will result in higher accuracy. ML techniques can be applied to predict configuration of carrier aggregation between 5G network nodes. Additionally, algorithms applied for design and fabrication of millimeter wave RF front circuitry is going to reduce the time to market. Network Slicing Planning for 5G Deployment AI-Enabled Massive MIMO RF Front End Circuitry Intelligent 5G Networks
  11. 11. SAMPLE REPORT AI in Telecommunication 11AI in Telecommunication SAMPLE REPORT 11 How is AI Transforming Telecom Business Models ?
  12. 12. SAMPLE REPORT AI in Telecommunication 12 AI Transforming Telecom Business Models Telecom companies are reinforcing strategies to incorporate AI in their core business offerings. CSPs are changing their core value propositions and are making fundamental changes to the traditional revenue models. These transformations are enabling them to capture new revenue streams and, in the future, can help them expand several industry verticals. In order to prepare for the digital transformation, telecom companies are therefore adopting different routes with a focus on delivering automation for the future complex networks. Some of the CSPs are considering …………….. of data. The evolution in the business models is a proof of how widely AI technologies are getting accepted in telecommunication. AI-Managed Services AI Chipsets Open Source Platform Data Monetization AI Cloud Services Telecom companies are providing ………….network requirements. This is crucial with the increasing number of ………….. service requirements. Open source frameworks will lead to ………….. innovation and is a huge transformation for the traditional business models. Telecom companies are harnessing the power of data for …………. opportunities. Data-sharing models are bridging the gap between ……….. across sectors. Telecom entities are focusing on automation in cloud that places them in direct competition with …………. Telecom companies’ strategic approach to develop ………… to enter new business areas. In the future, this will lead to direct competition with the ………………. Business Models for Integration of AI in the Telecommunication Industry
  13. 13. SAMPLE REPORT AI in Telecommunication SAMPLE REPORT 13 Business Model 1: AI Managed Services Companies Providing Managed Services Ericsson has signed multiple …….. telecom companies to support the innovation for next generation networks. o Ericsson’s partnership with Airtel is focusing on utilizing AI/ML for xxx operation management. o Mobily and Ericsson are working on anomaly detection, proactive ………… rollout for 5G and IoT use cases. o Ericsson is catering …….. regions for Telefonica. o Ericsson’s partnership with existing managed services customer MBNL to ………. Telecom companies are providing AI-based managed services to transform the traditional network-centric business models to a …………………………………………………………………….of data insights for network optimization, design and application development. This operating model addresses the ……………………………………………………………………………….. ……………………..network capital and operator expenditure. The managed services model is aiming towards addressing the future complexity challenges driven by ………………………….transformation. Companies • Xx • Xx • xx Benefits
  14. 14. SAMPLE REPORT AI in Telecommunication 14AI in Telecommunication SAMPLE REPORT 14 Overview of AI Adoption by Key Telecom Entities
  15. 15. SAMPLE REPORT AI in Telecommunication 15 Adoption Status of Telecom Companies – (1/5) Other AI Solutions AI Strategy • AT&T’s open source initiatives (ONAP, Danos, Acumos AI, Akraino Edge Stack), and the architecture and roadmap for …….. intelligent software-defined framework. • FlexWare is a network virtualization solution to deploy multiple functions through software. • The company’s ECOMP will …….. networks. • In 2018, the company virtualized 65.5% of its core network and is targeting 75% by 2020. • Verizon has partnered with ………. for the development of virtualized cloud RAN. In 2018, the company had tested vRAN using xx equipment and xx processors. Verizon has also conducted trial related to disaggregating hardware and software in the network. • The company is also deploying its Intelligent Edge Platform as a part of its automation strategy. • Verizon is also using AI and ML models to enhance ………………….. for improved customer experience. • Rakuten is following a greenfield approach to build a cloud native and virtualized network. The company’s partnership with…………….. ………………………..Altiostar, (for vRAN software), Nokia (Impact IoT), …………… and others are critical factors for the deployment of cloud-only 5G network. • AT&T is using AI models for improving customer experience, ……………… computing foundation. • Verizon has launched Digital CX, an end to end managed services that leverages AI for improved customer experiences. • The AI-driven business models will also help Rakuten explore new IoT applications in industrial and automotive sectors. • AT&T aims to provide a common open AI platform and …………. of the future to offer applications across sectors. The company has done multiple partnerships related to AI, cloud computing and 5G. • Verizon’s AI solutions are largely built from open source platforms. The company is also ………… …….will enable predictive analytics, ML, and AI applications. • Rakuten is directly competing with ………………………. in virtualization and container technologies for the telecom environment. • Rakuten’s vision is to innovate at ……………., thus reducing significant capex investment. The company intends to launch its commercial service in xx with a fully virtualized network. AI for Networks
  16. 16. SAMPLE REPORT AI in Telecommunication 16AI in Telecommunication SAMPLE REPORT 16 How is AI Driving M&A Activity in Telecom Sector?
  17. 17. SAMPLE REPORT AI in Telecommunication 17 M&A Analysis: Technology Drivers of the Deals SON Predictive Analytics Security Network Analytics Intelligent Spectrum vRAN Others Cloud Native Acquirers are focusing on the following technology areas to expand AI capabilities and create new business models. * Other category includes deals related to data sets, IT operations, customer support & internal business operations. *Deals related to chatbots and digital assistants were not considered for analysis.
  18. 18. SAMPLE REPORT AI in Telecommunication The following heatmap provides insights related to the technology distribution in the M&A deals across the years. Transactions related to cloud native, SON and security have been gradually increasing over the years. The year 2018 witnessed a spike in the number of deals with companies making acquisitions in different domains. In 2019, four cybersecurity related deals highlight the need of security companies in the future for autonomous detection and response. As virtualization is the aim of telecom companies, it is expected that M&A activities in vRAN space will continue in the future. M&A Analysis: Technology Distribution 18 M&A Trend Across AI Technologies *Other category includes deals related to data sets, IT operations, customer support & internal business operations
  19. 19. SAMPLE REPORT AI in Telecommunication M&A Analysis: Prominent Acquirers in the Space 19 Maximum number of deals have been done by ……………….. Nokia is prominent buyer in ……… space. While Ericsson is focused on ………. solutions. The security-related acquisitions were done by telecom operators including …………. Telefonica has followed a unique approach of ………….. in the telecom AI market. The company has integrated …………… for catering to clients globally. Some of the transactions highlight that startups are also expanding their niche portfolios to gain a competitive edge. ………….. has made multiple acquisitions to enter the telecom industry with capabilities such as intelligent RAN behavior analytics. A private equity firm has acquired …………… owing to the huge business growth opportunity available in the telecom industry. Such investors are capitalizing on the transformation need of the CSPs towards all-cloud network. This route not only enables the buyers an entry into the 5G market but also other parallel markets such as IoT. Solution Providers Telecom Operators Startups Investors
  20. 20. SAMPLE REPORT AI in Telecommunication 20 M&A Analysis: Post-deal Integration Examples – (1/2) Blue Planet became a part of Ciena’s portfolio with the acquisition of Cyan, a SDN solution provider. Addition to the existing Blue Planet portfolio for enabling the vision of adaptive networks with self-healing capabilities Blue Planet spun out as an independent division to focus on new growth strategies and roadmaps for closed-loop network automation. Layer 3 network optimization, topology and route analytics Intelligent inventory management solutions 2015 2018 2019 Ciena’s post acquisition analysis highlights how telecom companies are considering vital strategies to build network with automation capabilities and explore adjacencies for growth. Existing Portfolio Acquired Capabilities Post-acquisition Outcome
  21. 21. SAMPLE REPORT AI in Telecommunication 21 M&A Analysis: Netscribes Analysis and Insights The M&A trend highlights some of the significant technology domains on the radar of different participants in the telecom space. However, according to our analysis, as deep learning technologies are becoming pervasive in the telecom sector, other AI solutions are also going to be significant in the future M&A deals. Few of these technology areas include: Reinforcement Learning Hardware Innovations Innovative reinforcement learning……………… solutions…………………………………………………………………………………………….. to the ecosystem requirements. Such companies will be the potential targets. Startups that are ………………………………..are likely to grab attention from industry participants. ………………..has been included in the next section. • Self-healing solutions • Reinforcement and deep-learning algorithms • Xx • xx • Xx • xx • Xx • xx
  22. 22. SAMPLE REPORT AI in Telecommunication 22 M&A Analysis: Overview of the Deals – (1/9) Sr. No Buyer Target Date of Deal Region of Target Overview Technology Category 1 Securelink May-19 Belgium, Europe SecureLink provides security intelligence based on AI and ML techniques. It has a significant cybersecurity portfolio ranging from maintenance, support and consulting to advanced managed detection and response. This acquisition, with the deal size of USD 575 Mn, puts Orange at the forefront of European cybersecurity market. Security 2 SecureData Feb-19 UK, Europe SecureData employs AI-based engines for identifying critical threats in the network and the cloud. This acquisition will help Orange strengthen its position in the European market as a cybersecurity leader. Security 3 BluVector Mar-19 Virginia, US BluVector's proprietary ML engines provide detection, analysis of advanced cyberthreats. Comcast plans to leverage BluVector's expertise for new products and initiatives. Security 4 Mist Mar-19 California, US Juniper aims to expand its end to end software defined enterprise portfolio for its IT operations by using MIST'S AI Engine and cloud based solutions. IT Operations 5 Protectwise Mar-19 Colorado, US Protectwise applies ML and AI algorithms to predict and automate detection of security events and respond to them in real time. With rapid expansion in next-gen 5G networks, Protectwise brings the network detection and response capabilities to Verizon's global networks. Security
  23. 23. SAMPLE REPORT AI in Telecommunication 23AI in Telecommunication SAMPLE REPORT 23 How are Startups Innovating in the Space?
  24. 24. SAMPLE REPORT AI in Telecommunication 24 Key Challenges Targeted by Startups Challenges Solutions Companies Full duplex communication has ………. frequency. Presently, xx are used for noise suppression. The parameters in such devices cannot be …………. Interference in Full-duplex Communication Traditional hardware in the telecom vertical were …………… it is challenging to scale up the network for new use cases in multi- vendor scenarios. Decoupling Hardware & Software SON implementation for autonomous 5G networks requires increased ………… of new 5G systems for higher cell coverage, higher bandwidth and millimeter frequency range. SON Implementation Challenges AI-based ………….. techniques allow multiple transmitters to coexist enabling, …………… for mobile operators and eliminates the requirement for traditional xx. AI for Interference Cancellation Telecom companies are introducing …………. of the network. To accelerate the virtualization of RAN, they are exploring cloud native ……… of software and hardware and become xx independent. Cloud Native Architecture Companies are approaching ……….. with ML techniques for dynamic cell site ……… and xx algorithms for self-healing and self- configuring networks. Targeting Autonomous Telecom Network
  25. 25. SAMPLE REPORT AI in Telecommunication 25 Overview of Startups
  26. 26. SAMPLE REPORT AI in Telecommunication
  27. 27. SAMPLE REPORT AI in Telecommunication SAMPLE REPORT Foundation Year: 2013 Headquarters: Singapore Technology Focus: Intelligent SON The company is targeting …………………………………associated with multi-layered and service oriented wireless networks for virtualized 5G deployment with self-healing. Funding: USD 24.5 million Cellwize’s centralized elastic-SON platform eliminates the hardware dependencies for future adaptive and scalable networks to provide a multi-vendor environment for SDN/NFV. The company’s virtualized SON architecture is closed-loop and allows easy deployment of new network clusters with continuous monitoring and optimization. Cellwize’s business strategies suggest a focus on truly automated SON architecture. Cellwize has initiated ……………………………………………………………………………….. investigating AI and ML for mobile load balancing, automatic neighbour relation and other use cases. The strategic acquisition of CrowdX strengthened Cellwize’s customer-centric SON offering. By leveraging the acquired ………………………………………………… customer experience. Cellwise has partnership with companies like Tech Mahindra, HP Enterprise, Comarch, IBM, Telefonica, etc. The company has conducted proof of concept with ……………………………………………………………………………………………………… autonomous management. Additionally, Cellwize plans to focus on 5G rollouts and expansion of global footprint with the recent funding from Deutsche Telekom Capital Partners. Telefonica, Axiata, and Bell Canada are its global clients. Startups Focusing on SON 27 Technology Overview Company Overview Future Focus
  28. 28. SAMPLE REPORT AI in Telecommunication SAMPLE REPORT Foundation Year: 2011 Headquarters: Massachusetts, USA Technology Focus: Virtualized RAN for Cloud Native Telecom Network The company is planning to use the recent funding for expansion of its ……………………….. globally to build cloud native networks. Additionally, Altiostar’s partnership with its investors is focused on developing new 5G solutions. Funding: USD 325 million Altiostar is an open virtualized RAN (vRAN) software provider that is providing end to end cloud native 5G networks. The company is catering to telecom operators for migration to non- standalone 5G architecture and later to future standalone 5G network. The company’s innovative software architecture is deployed on commercially-off-the-shelf (COTS) hardware. Altiostar’s core vRAN software disaggregates hardware and software to build a multi- vendor environment of the future. The vRAN software can be deployed across the RAN ecosystem including massive MIMO, small cells and macro radios. It uses SON algorithms for easy configuration of new small cells and spans across technologies including LTE-A, Gigabit LTE, IoT, Hetnet, 5G NR, NFV, orchestration, C-RAN, CoMP and others. Altiostar’s software reduces the time to market as there is no requirement to change the hardware. The company is already working with Rakuten, a Japanese mobile operator, to deploy the first fully virtualized network that is targeted to be commercially launched in late 2019. Altiostar’s vision of virtualized RAN is aligned with its investors (Qualcomm Ventures, Tech Mahindra and Rakuten) aim to transform the mobile industry. Startups Focusing on Cloud Native Networks 28 Technology Overview Company Overview Future Focus
  29. 29. SAMPLE REPORT AI in Telecommunication 29AI in Telecommunication SAMPLE REPORT 29 Insights & Recommendations
  30. 30. SAMPLE REPORT AI in Telecommunication 30 Current Adoption Status of AI in Telecom Frontrunners AT&T and xx are the two companies that can attain network virtualization faster as compared to other telecom operators. AT&T has achieved 65.5% virtualization already xx, xx, Ericsson and xx are targeting self-driving networks. Huawei is creating reinforcement learning techniques Most of the MNOs have deployed AI at a chatbot level, driving improved customer experience and for ensuring internal business operation. Followers Late-adopters
  31. 31. SAMPLE REPORT AI in Telecommunication 31 Recommendations for Telecom Companies – (1/3) 5G Market New Market Opportunities Threats The 3GPP Release 16 for 5G standards is expected to be finalized by March 2020. MNOs, …… solution providers and ….. should consider working in collaboration with the regulatory bodies to accelerate the adoption of ML in telecom industry. ITU-T ……… of standalone 5G networks. Telecom industry will hold a strong position in the future AI market and can have the capabilities to enter new industry segments. For instance, …… directly with cloud service providers such as Amazon and Microsoft. xx business transformation from MVNO to MNO is an example of how IT or cloud service providers can enter the telecom sector by leveraging their existing infrastructure support.
  32. 32. SAMPLE REPORT AI in Telecommunication 32 Recommendations for Telecom Companies – (2/3) Presently, R&D in xx techniques is critical for designing autonomous, self-driving networks of the future. According to our analysis, telecom entities should consider partnership with universities and research institutes that are investigating xx. These universities are researching on these algorithms for different aspects covering traffic scheduling issues, offloading decisions, SDN/NFV 5G slices, interference mitigation, heterogenous network solutions and others. Telecom operators can collaborate with such universities and contribute with historical network data sets.
  33. 33. SAMPLE REPORT AI in Telecommunication 33 Recommendations for Telecom Companies – (3/3)
  34. 34. SAMPLE REPORT AI in Telecommunication Recommendations for Other Industry Players 34 Semiconductor companies should explore deep learning frameworks like ………………for maintaining lead in the telecom space that is witnessing growth in competition. For instance, xx’s strategy towards AI chips. As key integrators in the telecom domain, IT companies can consider the opportunity of offering ……………………….. 5G deployments. They also have an opportunity to enter the telecom market if the regulatory policies favour such a move. Then ………………… business transformations would be possible with other technology companies like xx directly competing with telecom operators to fuel price competition. Companies developing AI solutions can cater to ……………………….. and scaling it up till SON. AI startups can conduct PoCs in collaboration with telecom entities for 5G rollouts. Investors can consider portfolios that integrate automation solutions with 5G-enabling technologies. xx is already exploring 5G business ……… with the help of newly acquired spectrum. This is the right time for them to tap in the telecom market. Investors can increase ROI as ……………. CSPs to new application areas. In the near term, semiconductor companies can consider selling intelligent ………. the 5G market. Additionally, carrier aggregation demands in 5G require complex filtering configurations that can be overcome by ………………. Startups InvestorsSemiconductor Companies IT Companies
  35. 35. SAMPLE REPORT AI in Telecommunication 35AI in Telecommunication SAMPLE REPORT 35 Appendix
  36. 36. SAMPLE REPORT AI in Telecommunication References – (1/4) 36 ❑ Cellwize. “LP-AW-K-Self-Organizing Networks.” Cellwize, 4 May 2016, cellwize.com/self-organizing-networks/. ❑ Cambricon. “Cambrian Is Committed to Building Core Processor Chips for All Types of Intelligent Cloud Servers, Smart Terminals and Intelligent Robots.” Cambricon, www.cambricon.com/index.php?c=page&id=9. ❑ Parallel Wireless. “HetNet Gateway.” Parallel Wireless, www.parallelwireless.com/products/hetnet-gateway/. ❑ Kumu Networks. “Technology.” Kumu Networks, kumunetworks.com/technology/. ❑ Altiostar. “Cloud Native Network.” Altiostar, www.altiostar.com/solutions/radio-access-network/cloud-native-network/. ❑ RamiRahim. “Juniper Networks Announces Intent to Acquire Mist Systems to Bring AI to IT, Delivering on Promise of Software-Defined Enterprise.” J, 3 Mar. 2019, forums.juniper.net/t5/Engineering-Simplicity/Juniper-Networks-Announces-Intent-to-Acquire-Mist-Systems-to/ba-p/459688. ❑ Orange. “Orange Signs an Agreement to Acquire SecureLink and Accelerate Its Leadership in the European Cybersecurity Industry.” Site Institutionnel D'Orange, 10 May 2019, www.orange.com/en/Press-Room/press-releases/press-releases-2019/Orange-signs-an-agreement-to- acquire-SecureLink-and-accelerate-its-leadership-in-the-European-cybersecurity-industry. ❑ AT&T. “AT&T Completes Acquisition of AlienVault.” AT&T News, Wireless and Network Information, 22 Aug. 2018, about.att.com/story/2018/att_completes_acquisition_of_alienvault.html. ❑ ZephyrTel. “VoltDelta. Multi-Channel Contact Centre Solutions.” VoltDelta Cloud and on-Premise Contact Center Solutions, Virtual Call Center Infrastructure Services, www.zephyrtel.com/solutions/voltdelta/. ❑ Hamilton, Rick. “Ciena Completes Acquisition of Packet Design.” Ciena, 2 July 2018, www.ciena.com/insights/articles/Ciena-Completes- Acquisition-of-Packet-Design.html. ❑ Rakuten. “Rakuten Is Building the World's First End-to-End Cloud-Native Mobile Network.” Rakuten Today, 22 Feb. 2019, rakuten.today/blog/rakutens-upcoming-end-to-end-cloud-native-mobile-network.html. ❑ Nohrborg, Magdalena. “The MobileBroadband Standard.” SON, www.3gpp.org/technologies/keywords-acronyms/105-son. ❑ Ericsson. “New AI-Based Ericsson Operations Engine Makes Managed Services Simple.” Ericsson.com, 29 Jan. 2019, www.ericsson.com/en/press- releases/2019/1/new-ai-based-ericsson-operations-engine-makes-managed-services-simple
  37. 37. SAMPLE REPORT AI in Telecommunication References – (2/4) 37 ❑ ITU. “ITU Workshop on ‘Machine Learning for 5G and beyond.’” ITU Workshop on "Machine Learning for 5G and beyond", www.itu.int/en/ITU- T/Workshops-and-Seminars/20180807/Pages/default.aspx. ❑ Nokia. “#MWC19: Nokia, Korea Telecom to Conduct 5G Trials for Service Automation, Network Virtualization and Slicing.” Nokia, 24 Feb. 2019, www.nokia.com/about-us/news/releases/2019/02/24/mwc19-nokia-korea-telecom-to-conduct-5g-trials-for-service-automation-network- virtualization-and-slicing/. ❑ Incelligent. “Future RAN Evolution: Tools to Support Strategic Decision Making.” Future RAN Evolution: Tools to Support Strategic Decision Making, www.incelligent.net/news/247-future-ran-evolution-are-there-tools-to-support-strategic-decision-making. ❑ Guangping, Zhu. “SoftCOM AI: Hard on the Competition with Zero Faults - Huawei Publications.” Huawei, 6 June 2018, www.huawei.com/en/about-huawei/publications/communicate/85/softcom-ai-hard-on-the-competition. ❑ Ciena. “Waveserver Ai: Simple, Scalable DCI.” Ciena, www.ciena.com/products/waveserver-ai/. ❑ Luca. “Big Data Technologies: Statiq for Geolocation Data.” LUCA Data-Driven Decisions | Big Data Solutions | Data Engineering, luca- d3.com/technology-statiq/index.html. ❑ Metawave. “AWARE AI Engine | Metawave - Revolutionizing the Future of Wireless Communications.” Metawave, www.metawave.co/aware. ❑ Chinchali, Sandeep, et al. Cellular Network Traffic Scheduling with Deep Reinforcement Learning. AAAI, 2018. ❑ Huang, Liang, et al. “Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks.” Arxiv.org, 20 Apr. 2019, arxiv.org/pdf/1808.01977.pdf. ❑ Faris, and Brian. “Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement.” Deep Q-Learning for Self-Organizing Networks Fault Management IEEE Conference Publication, IEEE, 21 Feb. 2019, ieeexplore.ieee.org/abstract/document/8645083/authors#authors. ❑ Sadeghi, Alireza, et al. “Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities.” IEEE Journals & Magazine, IEEE, 29 Dec. 2017, ieeexplore.ieee.org/abstract/document/8241758/authors#authors.
  38. 38. SAMPLE REPORT AI in Telecommunication Research Methodology 38 Netscribes follows a structured project management approach to deliver deep-dive research analysis. A comprehensive approach is administered to assess innovative technologies and develop insights related to technology and business. Technology Research Business Analysis Report Format Report is prepared at the end of the research and is aligned as per expectations and quality check. An in-house editorial team conducts the final level QC. The report indicates roadmaps and key opportunities available in the AI telecom market. It provides insights of the AI/ML use cases across different segments in telecom. The report aims to shed lights on the key players working in the domain with their current and future offerings. The research methodology identifies the key challenges addressed by the startups and provides the overview of their growth strategies. The report also details the impact of innovative AI solutions in the telecom value chain. The report covers strategic business partnerships, acquisitions and future focus of key telecom companies. A detailed analysis of the M&A trend highlights the current AI demand in the space and the impact on different business portfolios. Additionally, the report includes recommendations for acquisition and collaboration to evolve in the growing ecosystem.
  39. 39. SAMPLE REPORT AI in Telecommunication About Netscribes 39 Netscribes is a global market intelligence and content services provider that helps corporations achieve strategic objectives through a wide range of offerings. Our solutions rely on a unique combination of qualitative and quantitative primary research, secondary/desk research, social media analytics, and IP research. For more than 15 years, we have helped our clients across a range of industries, including technology, financial services, healthcare, retail, and CPG. Fortune 500 companies, as well as small- to mid-size firms, have benefited from our partnership with relevant market and competitive insights to drive higher growth, faster customer acquisition, and a sustainable edge in their business.
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