Networks and Infrastructure Archives - Page 4 of 7

Media Aggregators, Canadian Government Tussle Over Bill C-18

Over the past few weeks, tech giants Meta and Google announced that they will no longer be publishing Canadian news on their platforms within Canada following the passage of Bill C-18, or the Online News Act, due to concerns over financial liability imposed on them by the Act. The new law requires large news aggregators operating in the country to pay for the news links they post on their platforms. Meta has already informed news outlets, including The Globe and Mail and The Canadian Press, that their contracts will end at the end of July and that Meta will no longer post the news outlets’ content.

What is Bill C-18 and what is the goal it intends to reach?

Section 4 of the Online News Act states the purpose of the bill is:

“…to regulate digital news intermediaries with a view to enhancing fairness in the Canadian digital news marketplace and contributing to its sustainability, including the sustainability of news businesses in Canada, in both the non-profit and for-profits sectors, including independent local ones.”

In layman’s terms, the government wants to increase the visibility of smaller local news outlets to expand the portfolio of news sources and to avoid the dominance of the few large news publishers who have contracts with large media aggregators like Meta and Alphabet (Facebook and Google). The way this legislation intends to reach its goal is by imposing a ‘link tax’ on these large media aggregators, which means they will have to pay for the news links that they post on their platforms. These media platforms will be expected to keep a roster of the ‘eligible journalists’ that are posted on the platform to ensure there is transparency on the news outlets and to ensure there is enough representation from underrepresented groups. These regulations aim to hold the media platforms accountable to ensure they are giving more news sources an equal opportunity to be promoted on these large platforms.

Tech giants’ concerns with Bill C-18

Despite the goal of equal news source opportunity, these tech giants are choosing to block Canadian headlines rather than comply. Google announced concerns that led it to pull from the Canadian media market:

Subsidizing and promoting ‘Bad Actors’ and strict media control from the government

Google explained in their statement that the definition provided for ‘eligible news businesses’ is very broad with low standards for journalistic integrity, which could risk the spread of propaganda and fake news. This gives rise to the issue of Google having to pay these outlets and provide them with profit and a platform to peddle poor information. This is currently prevented through qualifying criteria for journalism tax credits to be considered in Canada.

As Google pays proportionally for these headings, the act also stipulates that there is no ‘undue preference’ in the rank of relevant searches that Google currently uses. This means that there is a chance these bad actors could achieve a higher ranking in the searches and therefore reach Canadians a lot easier than with the current Google algorithm, which aims to return the most reliable and relevant sources.

On the flip side, the Canadian Radio-television and Telecommunications Commission (CRTC) will be responsible for qualifying who is considered an ‘eligible journalist’ and will be able to control the content that Canadians have access to. Although this could help control the foreign ‘eligible journalist’ who may peddle propaganda, it will also give more control to the government regarding what news Canadians will have access to that could eventually create a bubble. There is little information about the checks and balances that are in place by the CRTC to moderate these eligible news sources.

Lose-Lose business deal for Google and Meta

The new bill would require Google to pay news outlets for the links they provide, but ultimately the link is driving visitors to the publisher’s website. This means that instead of free marketing of the news article on Google (which is currently happening), the news outlet would get free marketing plus a pay cheque from Google. Aside from the journalistic morale that Google outlined before, from a business perspective, it makes very little sense for Google to participate as they would be paying the client and also providing them with a free service.

Status and the expected implications

This is past being a bluff from these large tech companies; the media industry has seen the power these giants have, as a similar legislation change happened in Spain which caused Google News to shut down for almost seven years, although ultimately it ended up returning after there were changes to the law. The CRTC announced this week that the ministry is drafting regulations that will address the concerns these media platforms have with the legislation. The ultimate fear of these tech giants is that there is an undefined financial liability that they will be responsible for, so the goal of these drafted regulations is to answer exactly how much these tech giants will be expected to pay if they do decide to keep their services in Canada.

Despite the turmoil this has caused in the Canadian Media market, other governments are also aiming to find ways to limit the media control these privatized media platforms have over the spread of news within a country. US states are exploring similar ways to enforce more competition in the news. Meta and Alphabet’s revenues would take a harder hit if the two companies follow the same course of action in the US as well.

Weekly Newsletter
July 13, 2023
Why Are Foldables So Hot In China?

READ HERE

Podcast #67: Interoperability and Testing Essential to eSIM Ecosystem’s Success

2022 was a landmark year for the eSIM ecosystem. eSIM adoption has accelerated around the world and is now rapidly moving towards becoming the preferred mode of connectivity. Consumer interest in eSIM is at an all-time high, profile downloads have increased exponentially and newer players are entering the market – all signs of an ecosystem on the rise.

Most premium smartphones now come with an embedded SIM (eSIM) as it is more advantageous than a physical SIM for OEMs, operators and consumers. We’ve already seen an eSIM-only iPhone 14 in September last year. It is possible that in the coming years, most premium smartphones may not have a physical SIM at all. But it is not just smartphones, even companion devices like tablets, smartwatches and other IoT devices are now adopting eSIM. However, because eSIMs are not removable like physical SIMs, testing for profile suitability and interoperability becomes extremely important.

But how does eSIM testing work? What is the process? Which companies help in such testing? We discuss all that and more in our podcast with Comprion, which is a German company that specializes in eSIM testing.

In the latest episode of “The Counterpoint Podcast,” host Ankit Malhotra is joined by Marcus Dormanns, Director of Product Management and Business Development at Comprion. The discussion touches on several topics including the technical process of testing an eSIM, interoperability with different network providers, the time taken for testing, and more.

Click to listen to the podcast

You can read the podcast transcript here.

Podcast Chapter Markers:

0:52 – Marcus Dormanns introduces Comprion.

2:25 – Dormanns talks about Comprion’s history in eSIM testing.

3:18 – Dormanns explains the technical details of eSIM testing.

5:19 – We deep dive to understand how eSIM testing is different from testing a physical SIM.

6:29 – Dormanns explains why the process of eSIM testing is not talked about much in public.

7:54 – How interoperability of eSIM is tested with different carriers.

09:55 – How correct 4G and 5G profiles are loaded in eSIM?

11:36 – Dormanns sheds more light on Comprion’s partnership with TCA for interoperability service.

13:52 – How long does it take for an operator to complete the eSIM testing process?

15:29 – Dormanns talks about the big trends developing in the eSIM space.

17:51 – As networks are becoming more complex, does it impact eSIM profiles and testing?

18:38 – What is the role of regulatory bodies in eSIM testing?

Also available for listening/download on:

NVIDIA and Softbank Developing AI-Based 5G MEC Telco Network

NVIDIA and Softbank recently announced that they had developed a dual-purpose AI-driven 5G MEC and vRAN distributed platform based on NVIDIA’s new GH200 Grace Hopper superchip. The two partners intend to deploy a network of regional data centres across Japan later this year to capitalize on the demand for accelerated computing and generative AI services. The shared multi-tenant platform will also offer a range of 5G vRAN applications and Softbank is creating 5G applications for autonomous driving, AI factories, augmented and virtual reality, computer vision and digital twins.

Key Takeaway No. 1: Platform Limitations

Softbank is offering a dual-purpose platform where the main application is AI compute via a high-performance edge analytics platform to capitalise on the expected surge in demand for AI processing capacity. Although the company is also developing a range of 5G applications, the AI and 5G workloads will be offered simultaneously. Counterpoint Research believes that the platform is unlikely to be feasible for vRAN workloads alone and understands that the two partners are not targeting this market.

Key Takeaway No. 2: Leveraging GPU Usage in the RAN

NVIDIA is planning to leverage its GPU processing capacity in the RAN in a number of ways, for example, to improve spectral efficiency. One way of doing this is to apply AI to optimise channel estimation feedback data between a user device and a base station. A compute intensive problem with mMIMO radios, using AI to compress receiver feedback data would reduce signalling overhead, thereby resulting in an useful increase in uplink channel capacity. This could be particularly effective at the edges of a cell. Using its GPUs in this way, NVIDIA claims that it can boost gain for cell edge users by 14-17 dB. Other applications include using AI to optimise beamforming management in millimetre wave mMIMO radios as well as to accelerate Layer 2 scheduling.

The full version of this insight report, including a complete set of Key Takeaways is published in the following report, available to clients of Counterpoint Research’s 5G Network Infrastructure Service (5GNI).

New Report: NVIDIA and Softbank Join Forces To Deploy AI-Based 5G MEC Telco Network Across Japan

Table of Contents:

Snapshot
Key Highlights
– 5G MEC Telco Network
– Grace Hopper Superchip
– Leveraging Software Resources
– Performance Details
– Use Case and Deployment Options
– Key Partners
– Competitors
Analyst Viewpoint
– Platform Limitations
– Benefits of RAN-in-the-Cloud
– Eliminating RAN hardware dependency
– The Intel vs ARM battle
– Leveraging GPUs in the RAN
– A Crowded Market

Get a quote

Related Reports and Blogs

Open RAN Networks – Layer 1 Acceleration Strategy Will Be Key To Operator Success

In-line, PCIe-based accelerator cards will be essential to process latency-sensitive, Layer 1 workloads in COTS-based massive MIMO open RAN networks.
Accelerator card energy efficiency will be a key differentiator among vendors as could the degree of Layer 1 stack openness.

Virtually all open RAN deployments to date are based on Intel’s FlexRAN reference software architecture running on x86-based COTS servers. While this configuration is adequate for low to medium traffic scenarios, it is not sufficient for high-traffic use cases in dense urban areas involving the use of massive MIMO radios.

Solving the massive MIMO performance deficit is a major challenge delaying the transition to open RAN. This challenge must be resolved before mainstream adoption of open RAN-based massive MIMO radios can occur. However, this will require a new breed of merchant silicon solutions designed to efficiently process latency-sensitive Layer-1 baseband workloads. Last year, a number of vendors announced alternatives to Intel’s FlexRAN platform based on ASICs, GPUs as well as RISC-V architectures and several of these vendors showcased their latest products recently at MWC-23 in Barcelona (Exhibit 1).

Look Aside Versus In-line Acceleration

Open RAN COTS platforms typically use PCIe-based accelerator cards to process the compute-intensive Layer 1 workloads. There are essentially two types of architecture designs: look-aside and in-line:

Look-aside accelerators offload a small subset of the 5G Layer 1 functions, for example, forward error correction, from the host CPU to an external FPGA or eASIC-based accelerator. However, this offloading adds latency and degrades system performance as the compute is done offline.

An alternative to using PCIe cards is to integrate the look-aside accelerator and CPU in a SoC. This eliminates the need for a separate PCIe card. Look-aside acceleration is used by AMD and Intel, including in the latter’s vRAN Boost integrated SoC design.

With the in-line accelerator architecture, all the Layer 1 data passes directly through the accelerator and is processed in real-time – a critical requirement for Layer 1 workloads. This processing is done by other types of processors, for example, ARM or RISC-V based DSPs, which results in a more energy-efficient implementation and reduces the need for additional CPUs with a high number of cores. For operators, this results in significant CAPEX and OPEX savings, particularly in the case of massive MIMO base stations, the most demanding of all 5G network deployments.

In-line accelerators also offer important scalability benefits as operators can add extra accelerator cards (up to six cards in a standard telco grade server) as more L1 capacity is required. In contrast, the look-aside architecture involves adding expensive, power-hungry COTS CPUs (+FPGA/eASIC cards) to meet capacity increases. In-line acceleration is used by ARM-based chip vendors such as Qualcomm Technologies, Inc and Marvell, as well as some RISC-V start-ups.

Layer-1 Software Stack

Chip vendors typically offer Layer-1 reference software stacks, which OEMs or third-party software vendors then customize and harden. This is an intensive two- or three-year process that demands considerable technical expertise and resources, particularly for telco-grade massive MIMO networks. With 5G, there is added complexity as the Layer 1 stack needs to be very adaptive and programmable to cater for the multitude of workloads and use cases. As a result, very few chip vendors offer carrier-grade Layer 1 software. In fact, the choice of vendors with the capability to develop macro cell massive MIMO Layer 1 stacks is essentially limited to the Tier-1 incumbents plus a handful of open RAN challenger vendors.

Tier-1 incumbents are unlikely to offer the same level of openness and flexibility as the challenger vendors, and hence accelerator cards from the latter may be a more attractive option for operators looking for a higher level of network customization. For example, Qualcomm Technologies will offer a set of APIs that allow vendors to port alternative software into its Layer 1 stack – such as beamforming or channel estimation algorithms. This lowers the barriers to entry for small software vendors and enables new players to enter the market – i.e. without requiring them to develop a full commercial-grade Layer 1 stack themselves. This could result in a rapid expansion of the Layer 1 software ecosystem.

Exhibit 1: Open RAN Layer 1 Accelerator Card Options by Vendor (Macro Cells)

Energy Efficiency – A Critical Metric

Reducing OPEX costs has become a major priority for operators due to soaring energy costs plus the need to minimize their carbon footprints. As a result, energy efficiency, i.e. Gbps/Watt, will be a critical metric for operators when evaluating Layer 1 accelerator cards. However, only a few vendors have revealed power consumption data. In a recent demo, Qualcomm Technologies showed the total power consumption of its Qualcomm® X100 5G RAN Accelerator Card [1] to be just 16-18W when driving a 16-layer 64TRx massive MIMO radio configuration serving four user devices (using four layers/device). According to the vendor, the card is designed to support a throughput of 24Gbps at less than 40W peak power consumption.

Viewpoint

The open RAN story is gaining momentum and Counterpoint Research expects this growth to accelerate during 2023 and beyond driven by the availability of new merchant silicon solutions. To succeed in the marketplace, however, these new silicon platforms will need to demonstrate the potential to compete against the latest incumbent RAN solutions – across all key technical metrics – as well as offering the same level of advanced 5G features. Clearly, energy efficiency will be a major product differentiator, which puts current x86-based look-aside designs at a disadvantage compared to the latest in-line accelerators and suggests that most operators will favour the in-line architecture approach. Ultimately, the winning vendors will be those that are best able to satisfy the key technical requirements of individual operators while at the same time offering them the flexibility to customize their networks to suit their own requirements.

[1] Qualcomm X100 5G RAN Accelerator Card is a product of Qualcomm Technologies, Inc. and/or its subsidiaries. Qualcomm is a trademark or registered trademark of Qualcomm Incorporated

Cloud RAN – Waiting for a Viable Business Plan?

The Emerging Cloud RAN Ecosystem – Players and Solutions

Cloud RAN – Technology Review and Market Challenges

Open RAN Radios – Chinese Vendors Set To Dominate An Emerging Market?

AI/ML Key in Enhancing 5G Network Efficiency, Reducing Complexity

5G network has seen steady growth in deployment globally, with the total number of subscribers of 5G services crossing the billion mark. Although most of the deployment has been through the Non-Standalone (NSA) mode, the Standalone (SA) core will see big commercial deployment in the years to come to explore the full potential of 5G, including venturing into newer 5G applications such as Network Slicing. To provide high bandwidth and low-latency connectivity with processing capabilities at the Edge to support enterprise and mission-critical use cases, it becomes important to manage the network effectively and autonomously, and AI/ML can help in this case. As the algorithm is advancing every day, AI/ML can help automate most of the tasks. The huge amount of data collected by network vendors and operators can be used to train an effective algorithm, thereby helping in the effective management of resources.

Some of the verticals where AI/ML will be useful in network management are:

Intelligent Network Automation

5G networks are complex and managing them is a difficult and expensive task. AI/ML can provide intelligent algorithms that can automate various network management tasks, thus reducing the time and resources required to manage the network. AI can help in managing the network traffic, as an increasing number of devices connected to a network makes it harder for an operator to monitor the usage, and the algorithm can monitor the network traffic pattern and optimize it, and allocate resources based on the devices’ bandwidth requirements, thus ensuring the efficient use of resources.

AI can also be used to get insights into network behavior, which can be used to identify bottlenecks and anomalies in the network that can cause security issues.

RAN Enhancement

AI can help improve network energy savings by managing energy usage. The algorithm can optimize the transmission power of base stations for the devices based on their proximity. Another application can be the activation of sleep mode to reduce energy consumption when there is less network load on the base station or it is idle.

AI can also be used for precision planning in small-cell deployments. The ever-increasing demand for data is congesting the network in some areas, especially in urban and compact spaces such as stadiums. To solve the problem, small cells are required to be deployed. AI can analyze the data on network traffic and latency, and identify the black spots where small cells can be deployed. It can also help in identifying suitable locations for small-cell deployment so that not many cells are deployed at a site.

Source: Huawei

Huawei has launched intelligent RAN solutions iFaultCare and iPowerStar. The company claims that its iPowerStar AI-based intelligent RAN solutions can generate power savings of 25% and reduce OPEX by 20 million KWh per year, whereas iFaultCare can improve troubleshooting efficiency by 40%.

Network Management

One of the major use cases of automation will be network management. The algorithms can monitor the network metrics, such as load factor, traffic and latency, and adjust them to optimize the performance. Another way in which AI can help is by improving network reliability through the prediction of issues that may arise. The algorithm can analyze the network data to identify patterns that may lead to outages, thus allowing time for preventive action.

Network Security

In 5G, we are going to see an increasing number of connected devices along with an increasing volume of data transmitted across the network. With an increasing number of devices, the potential for cyberattacks also increases, and operators must enhance cybersecurity to prevent a possible attack on the network. Critical use cases such as private networks are more prone to cyberattacks, which can result in revenue losses to the enterprise. AI can come in handy in preventing cyberattacks. It can help identify potential threats, such as malware or phishing attacks, and respond quickly to mitigate the risk. Besides, AI can play a significant role in 5G network security by detecting, analyzing, and responding to security threats in real-time. With the past dataset provided to analyze network behavior, the algorithm can identify patterns and anomalies that may lead to cyberattacks. AI algorithms, for instance, can recognize a potential security breach if a certain device is transmitting an unusually large volume of data. It can then take appropriate steps to prevent any damage.

MIMO

AI can help in effective MIMO management. The algorithm can analyze the network and adjust the number of MIMO antennas to be used for optimized device performance. AI can also be helpful in beamforming, a technique that allows the transmitter to focus its energy in a specific direction to improve network coverage and capacity. The algorithm can identify from where the demand is coming and ensure that sufficient bandwidth is provided to the device. By effective use of beamforming, operators can provide high-speed, low-latency services to different devices and applications.

Network Slicing

Network slicing is one of the most discussed topics in the industry. It is being touted as one of the important use cases for 5G networks. Network slicing is a technique that allows operators to create multiple virtual networks on top of a shared physical infrastructure. Each virtual network can be designed to meet the specific requirements of a particular use case, such as high-speed data transfer and low latency. AI can be of immense help in network slicing, as it can automate most of the prerequisite tasks, such as:

Preparing the network: The algorithm can use past data to predict the demand that might come from the user.
Resource reservation: The algorithm, after predicting the demand, can slice the network and reserve it for the task which the network might be getting.
Resource allocation: Once the requirements come from the user, the reserved resource can be allocated to the user.

5G Advanced

Networks are becoming complex and AI/ML-based solutions are being used to reduce the complexity and make the network more intelligent. Introduced with Release 15, and with subsequent enhancements in Releases 16 and 17, AI/ML is being used for different use cases, such as network energy savings, network load balancing and mobility optimization.

Release 18 will be looking to incorporate more enhancements for automating the network and predicting the network behavior to make it efficient. Different areas are being looked into to study the potential of AI/ML for different elements of air interface, such as beam management, mobility, and position accuracy.

Drawbacks of AI/ML

Although AI offers lots of benefits in effectively managing the network and automating most of the tasks, it also has some deficiencies. One of the biggest problems faced in writing an effective algorithm is getting a large amount of reliable and relevant training data. Bigger players have access to large amounts of data and resources to train their models, whereas smaller players lack them and have to rely on other players to get the algorithms, which might not be relevant for their use cases. A lack of appropriate training data can make the model less reliable and relevant, and it might produce undesired outcomes.

Some of the challenges which an AI algorithm can face are:

Complexity: Implementing AI technology in 5G is a complex task and it requires significant investments in resources. Besides, the AI algorithm needs to be effectively trained and tested before being deployed, to ensure that it provides the desired outcome.
Bias: AI algorithms are trained on the data they get. If the data on which they are trained is biased or skewed towards one side, then it can generate biased results and could lead to the wrong label of false positives or false negatives.
Privacy: Privacy has become one of the most important issues today for AI algorithms, as the algorithm needs data to be trained upon, but some of the data might contain sensitive information. Privacy laws must be in place to ensure that sensitive information is not used for inappropriate purposes.

How OPPO Aims to Transform into a Global Technology Leader from a Smartphone OEM: Insights from OPPO INNO DAY 2022

Chinese smartphone maker OPPO hosted the OPPO INNO DAY 2022, its fourth annual technology launch event, on December 14. During the event, apart from highlighting innovation in the underlying technologies, essential capabilities and devices design and form factors of smartphones, OPPO also indicated its endeavor to transform from a smartphone OEM to a global technology leader.

During the event, OPPO announced the top seven strategic control points (SCPs) that the company would continue investing in to develop as the backbone of the company’s sustainable development. It also unveiled innovative products and technologies that represent the company’s best R&D capabilities, such as the MariSilicon Y Bluetooth audio SoC, AndesBrain platform, OPPO Air Glass 2, OPPO Find N2 and N2 Flip foldable smartphones. Hereby in the article, we will place OPPO’s newly launched products side by side with its SCPs to demonstrate how the company is working to convert its high-level ideas into reality.

SCP No. 1: Self-developed computing chips that integrate multiple cores and structures to optimize the device performance from the underlying level

Execution plan: MariSilicon self-developed chips

New product launched at the event: MariSilicon Y Bluetooth audio SoC

Source: OPPO

Key innovation points: The MariSilicon Y Bluetooth audio SoC is the first SoC developed by OPPO. The chip supports transmission speeds of 12Mbps, the highest specification a Bluetooth SoC has achieved in the market so far. Combined with OPPO’s self-developed audio codec, called URLC (Ultra Resolution Lossless Codec), which enhances the compressibility of lossless audio content by 50%, the MariSilicon Y can transmit an unprecedented 24-bit/192kHz ultra-clear lossless audio via Bluetooth – the first of its kind in the industry. OPPO has claimed that the chip can give users the benefits of wireless connectivity but with the same audio quality of a wired connection. The chip also integrates a dedicated Neural Processing Unit (NPU) with up to 590 GOPS of on-device computing power to unlock new possibilities in spatial audio experiences, such as separation or customization of instrumental music and vocals based on users’ specific commands. The MariSilicon Y is based on the most advanced N6RF process technology from TSMC, which is the same technology used in the Apple AirPods 2 Pro’s H2 SoC. The MariSilicon Y is compatible with a wide range of Bluetooth codecs, which allows non-OPPO devices to connect and collaborate with the devices powered by the chip.
Analyst comments: The MariSilicon Y Bluetooth audio SoC is the second addition to the MariSilicon family, following the first self-developed dedicated imaging NPU, called MariSilicon X, which OPPO launched last year. While the MariSilicon X is still a co-processor, the MariSilicon Y is more advanced as an audio SoC given that it integrates functional blocks such as connectivity units (Bluetooth baseband and RF), NPU, DSP, audio codecs, memory units and more. This represents the progress in OPPO’s design capabilities for system-level solutions. According to Counterpoint’s TWS sales tracker, OPPO has not yet found success in the TWS business. We are looking forward to seeing new MariSilicon Y-powered devices and how they will affect OPPO’s TWS business. More importantly, as OPPO aims to develop more advanced chips going forward, we expect the IPs and experiences accumulated from developing less complex chips, such as the Bluetooth audio SoC, will contribute to OPPO’s ambition in the long run.

SCP No. 2: To provide users and developers with seamless cross-platform connections and services

Execution plan: Pantanal cross-platform smart system

New product launched at the event: There were limited updates on the Pantanal cross-platform smart system during the OPPO INNO DAY 2022 as the platform was launched very recently during the 2022 OPPO Developer Conference in August. The platform is designed to break boundaries across different devices and systems to enable seamless connection and collaboration among them. According to OPPO, with cross-device sensing and computing capabilities, Pantanal can better understand and anticipate users’ needs and provide services at the right time in the right way, making it easier for developers to deploy services across platforms. OPPO Carlink, which enhances the collaboration and integration between smartphones and automobiles, is a typical use case under the Pantanal plan.

Source: OPPO

Analyst comments: OPPO’s non-smartphone businesses are still relatively small compared to those of Apple, Xiaomi, Huawei and other rivals. We expect OPPO to work towards converting its smartphone-only users into multi-device users and boosting the sales of its IoT and ecosystem products by providing better cross-device connectivity and collaboration under the Pantanal plan. This is likely a long-term investment for OPPO given its current smaller base of non-smartphone users. It will be important for the company to build on the plan by expanding its pool of cooperation partners. OPPO has partnered with SAIC Motor to fully integrate the capabilities of the OPPO Carlink into some of the automobile manufacturer’s car models. OPPO has also gained support from leading automobile OEMs, including BYD, Tesla, Li Auto, XPeng Motors and Changan Automobile, for the basic Carlink capabilities. We expect it to take time for OPPO’s Pantanal plan to have tangible impacts and business benefits.

SCP No. 3: Combing the computing capabilities of device and cloud to enable intelligent services.

Execution plan: Andes Plan

New product launched at the event: AndesBrain

Source: OPPO

Key innovation points: AndesBrain is a cross-platform smart cloud service that integrates six capabilities – device-cloud data storage, device-cloud machine learning, device-cloud real-time rendering, smart dialogue, hardware emulation, and security and privacy. The platform provides an innovative storage experience by offering Android users infinite storage space based on CubeFS, which is the first cloud-native distributed storage platform in China that embeds cloud storage into the OS layer. It can provide users with the infinite cloud photo album service with smart photo search function, infinite document storage service and the cross-device document management service. During the annual INNO DAY event, OPPO demonstrated the AndesBrain’s Xiaobu AI assistant, which has machine learning capabilities, and the platform’s digital human, which is based on the device-cloud real-time rendering engine. OPPO will soon open these platform capabilities to external developers.
Analyst comments: It is getting increasingly difficult to improve the performance of a chip as the scale of chip components continue to shrink in size. Therefore, there has been an increasing demand for real-time collaboration between device and cloud to reduce the computing burden at the device end. The AndesBrain represents OPPO’s plan to optimize computing power and resources between device and cloud to upgrade the storage and AI-powered intelligent services for users. We expect this platform to be an important strategy for OPPO to differentiate itself from other cloud and software services to stay ahead of the over-intensified hardware competition among Android vendors.

SCP No. 4: Smart health products and services to help users prevent diseases

Execution plan: OPPO Health Lab

New product launched at the event: OHealth H1 family health monitor

Source: OPPO

Key innovation points: The OHealth H1 combines six health data monitoring functions into a single device, including measurement of blood oxygen, ECG, heart and lung auscultation, heart rate, body temperature and sleep tracking. Unlike the industrial designs of traditional medical equipment, the OHealth H1 is a super-light device that weighs just 95g and is intended for family use. OPPO claims that the heart and lung auscultation of the OHealth H1 is based on the piezoceramics sensor technology, which enables it to provide better accuracy than the mainstream digital stethoscope products available in the market for Remote Patient Monitoring (RPM). OPPO also boasts that the accuracy of the OHealth H1’s heart rate and blood oxygen readings, and the algorithm to detect arrhythmia can exceed performances of mainstream smart wearable products. However, the OHealth H1 is still a concept product at this moment.
Analyst comments: OPPO established the Smart Health Lab in 2020 and positioned it as a strategic investment with no return on investment (RoI) KPI in the short to midterm (another strategic investment apart from the self-developed chips). OPPO’s Health Lab has been building strategic partnerships with top medical universities, R&D centers and hospitals in China to combine the professionalism and resources of medical research organizations with OPPO’s expertise in device development and user operation. It targets to spark innovation in not only consumer medical hardware, but also medical service and ecosystem business models. The OHealth H1 is the first hardware product developed by OPPO’s Smart Health Lab. OPPO’s smart health strategy can be a bold step for the company to enter the lucrative medical devices market and achieve a breakthrough in the consumer IoT area, which will also allow it to diversify away from its already-successful smartphone business. For that, OPPO will have to put in great efforts to improve medical research, attain regulatory approvals and develop life-design skills.

SCP No. 5: Developing home service robots to bring warmth and convenience to individuals and families

New product launched at the event: Xiaobu home robot

Source: OPPO

Key innovation points: The Xiaobu home robot is the first humanoid robot product developed by OPPO. It can verbally answer questions, converse with humans and offer reminders on the weather, schedules and healthcare routines, using the Natural Language Processing (NLP)/Natural Language Understanding (NLU) technologies. The robot can also comfort humans and interact with them using its emotion recognition technology. The robots can automatically elude barriers to navigate home spaces based on the SLAM and acoustic source localization technologies. OPPO expects the Xiaobu home robot to fill the role of an adorable family member to provide convenient, intelligent and heartwarming services at home.
Analyst’s comments: We anticipate some segments of the consumer robotics market to offer excellent growth potential. Because these products are easier to make, we expect OPPO to target the home companion and service robot segment as its entry point into the market. By developing these products, OPPO can accumulate key IPs in the robotics domain, such as localization and navigation technologies, mechanical arm technologies, motor systems and AI algorithms, including NLP/NLU and computer vision. At this point, the Xiaobu home robot is most likely a concept product with no commercialization plan in the short term. We expect it to still take some time for home service robot products (not only from OPPO but the mass market) to mature and achieve commercial success.

SCP No. 6: XR and Metaverse have the potential to disrupt how humans interact

Execution plan: XR research lab

New product launched at the event: OPPO Air Glass 2

Source: OPPO

Key innovation points: The OPPO Air Glass 2 features a super-lightweight design, weighing only about 38g. It includes the world’s first resin SRG-diffractive waveguide lens developed by OPPO. According to OPPO, these lenses will support vision correction and further customization, making them almost indistinguishable from regular glasses. The Air Glass 2 can make phone calls, make real-time translations, provide location-based navigation, convert voice into text for people with hearing impairments and provide other smart experiences.
Analyst’s comments: The Air Glass 2 is the fourth smart AR glass developed by OPPO, since the introduction of the first OPPO AR Glass in December 2019. OPPO has been trying to optimize the weight and size of each iteration. There seems to be limited upgrade in the utility functions of the Air Glass 2 from its predecessor, the OPPO Air Glass. The OPPO Air Glass 2 can display content on double lens while the predecessor only supports single-lens display. The OPPO Air Glass was commercialized at a small scale in China, priced at RMB4,999 ($769), while the Air Glass 2 is still under development. We expect OPPO to position the Air Glass 2 either as a concept product or commercialize it at a small scale as well. The Metaverse has become a highly-hyped concept in recent years, and we see tremendous growth potential in the global XR market over the next five years. Nevertheless, with the uncertainty in financial return and the timing of widespread adoption, we’re also seeing more prudent investments from Chinese smartphone OEMs in this domain.

SCP No. 7: Smartphone to remain the core computing hub in the next decade

Execution plan: Premiumization of the OPPO smartphone brand

For the insights on OPPO’s brand premiumization strategy and new products launched in this regard, and the PDF version of the full report, please click here to download.

Ericsson's Cradlepoint Launches NetCloud Private Networks Solution

Last week, Cradlepoint announced the launch of its subscription-based NetCloud Private Networks solution, an SME-type enterprise solution targeted at office buildings, retail outlets, stadiums, hospitality venues, smart city, schools, etc. It will complement Ericsson’s Private 5G product which is designed primarily for industrial applications such as manufacturing, energy and utilities, etc, where low-latency, high reliability and business-critical capabilities are essential.

Key Features of NetCloud

Key features of NetCloud include:

A “Private Networks-in-a-Box” solution – which includes access points, core/gateway, network planning software, routers, private SIMs plus a single pane of glass cloud management and orchestration system via NetCloud.

Cradlepoint Distribution Channels – NetCloud will leverage Cradlepoint’s existing sales channels via network enterprise resellers, managed service providers, etc. Later in the year, the company plans to target distribution via mobile operators.

Flexible Customer Offering – based on a CAPEX or OPEX business model to suit customers. Enterprises can buy cellular access points, core capacity, etc on a capacity and service duration basis. For example, Cradlepoint offers 500Mbps 2 Gbps and 5 Gbps options for its core on a 3 or 5 year basis with SIM cards being sold in packs of 10.

At present, NetCloud is only available for use in the US’s 4G LTE CBRS market, but later this year, Cradlepoint will offer 5G radios for use in markets beyond the US, for example, Europe. The company also expects to introduce eSIM capabilities at the same time.

The key elements of Cradlepoint NetCloud solution are shown in Exhibit 1 below:

Exhibit 1: Overview of Cradlepoint’s NetCloud Private Network Solution

Viewpoint

In the past year or so, there have been many “Private-Networks-in-a-Box” announcements from numerous vendors such as AWS, Cisco, HPE, etc. as well as operators such as Dish, etc. who believe that private network solutions can be marketed as an “out-of-a box” product like Wi-Fi. With so many offerings, this could turn out to be a very competitive market with thin margins, where success is largely determined by the vendors’ “go-to-market” strategy and distribution channels.

The launch of NetCloud is a clear indication that Ericsson (with Cradlepoint) plans to play across the whole breadth of the private networks market. Although surprisingly a bit late launching its own “box” solution, Counterpoint Research believes that Cradlepoint is well-positioned to benefit from the opportunities in this market due to its strong position in the enterprise market (32,000 customer base), its global distribution network of 6,000 resellers and partners – and backed by Ericsson’s connectivity expertise.

Private Networks – High Expectations Amid An Expanding Ecosystem

Network Slicing vs Private Networks: Benefits and Drawbacks

Private Cellular Networks – Devices Key To Growth In Unlicensed Spectrum

5G Mobile Edge Computing – An Emerging Technology Slowly Transitioning To Commercial Reality

India 5G Smartphone Shipments to Cross 4G Shipments in 2023

Cumulative 5G smartphone shipments will cross the 100-million mark in Q2 2023 and exceed 4G smartphone shipments by the end of 2023.
India’s 5G smartphone shipments are estimated to grow 81% YoY in 2022 driven by their expanding presence in lower price bands (<INR 20,000 or ~$244) and rollout of 5G networks.
5G share in lower price bands (<INR 20,000 or ~$244) is gradually increasing, from 4% in 2021 to 14% in 2022.

India’s smartphone shipments are projected to witness a yearly decline in 2022 due to macroeconomic factors affecting consumer demand in the entry and budget segments. However, 5G has been a driving force and will continue to push smartphone demand in 2023 as well. India’s 5G smartphone shipments are estimated to grow 81% YoY in 2022 driven by their expanding presence in lower price bands (<INR 20,000 or ~$244) and rollout of 5G networks in the latter half of the year.

According to Counterpoint’s India Market Outlook, cumulative 5G smartphone shipments will cross the 100-million mark in Q2 2023 and exceed 4G smartphone shipments by the end of 2023. Our latest consumer study also reveals that 5G is the third most important factor for future smartphone purchases.

India 5G vs 4G Smartphone Shipment Penetration

Source: Counterpoint Research India Smartphone Outlook, November 2022

5G share in lower price bands (<INR 20,000 or ~$244) is gradually increasing, from 4% in 2021 to 14% in 2022. It is expected to reach 30% in 2023. The cost of an entry-level 5G smartphone came down to below INR 10,000 (~$122) in 2022 with the launch of the Lava Blaze 5G. The availability of cheaper 5G chipsets from Qualcomm and MediaTek has enabled OEMs to launch more 5G devices in the lower price segment, while the commercial rollout of 5G services has also driven demand for the same.

Source: Counterpoint Research India Smartphone Outlook, November 2022

However, the growth here has been limited due to component supply shortages, inflation, geopolitical conflicts and other macroeconomic issues, which have delayed 5G device launches in the budget segment. Though OEMs have brought more 5G devices for lower price bands (<INR 20,000 or ~$244), they have done so by dropping or downgrading other key features like display or fast charging to lessen the impact of increasing component costs. This, in turn, has affected the consumer demand for 5G within this price tier. The limited availability of 5G networks has also affected the demand.

We expect these constraints to ease by the end of 2023, leading to the mass adoption of 5G. Better availability of networks in major areas will also facilitate 5G smartphone growth in 2023, which is estimated to be 62% YoY.

Counterpoint Research Forecasts Private Networks Market To Reach $21.8 billion by 2030

Counterpoint Research forecasts that the global LTE/5G private networks infrastructure market will increase from approximately $2.5 billion revenues at the end of 2022 to reach $21.8 billion at the end of 2030, representing a CAGR of 30.7% over the forecast period (Exhibit 1).

Private Networks Market by Verticals

Counterpoint Research segments the private networks market into seven vertical markets: public sector, manufacturing, energy and utilities, natural resources extraction, transportation and logistics, SME and Others. The public sector consists of several sub-verticals such as public safety networks, smart cities, healthcare and government as well as education and defence. The SME market encompasses mostly non-industrial applications of private networks, for example, office buildings, hotels, shopping malls, leisure centres, etc. Counterpoint Research forecasts that manufacturing will be the biggest vertical by revenue at the end of the forecast period in 2030.

Variations by Region

The release of CBRS spectrum has been a key driver in the US, which is predominantly based on 4G LTE and typically used for simple applications that do not require 5G, for example, mobile broadband connectivity in schools. In contrast, Europe has a higher proportion of 5G networks with the focus being on Industry 4.0 automation and the development of smart factories. Key use cases driving the market in Europe include AGVs/AMR connectivity, advanced worker productivity technologies such as AR/VR, predictive maintenance, video analytics (particularly security), asset tracking, etc.

Most deployments of private networks are in the developed, high GDP regions of the world with developing regions of the world lagging. An exception is Latin America, where there are numerous networks targeting the mining industry (predominantly copper mines).

Exhibit 1: Global Private Networks Market: 2021-2030

Key Points and Issues

Some key points discussed in the report include:

Spectrum – spectrum availability has been the key driver behind the growth of the private networks market, with countries such as Germany, France, the UK and US leading deployments due to early availability of spectrum in those countries. However, none of these countries use the same spectrum bands for private networks. They also have different usage rules. Spectrum fragmentation is therefore an issue and there is an urgent need for harmonisation of private networks spectrum, particularly between countries in the same regions, such as Europe.
5G Advanced – the introduction of devices supporting 5.5G from 2025 onwards will provide a boost to 5G private networks and will result in a number of new device capabilities leveraging the advanced features offered by NR-Light (Redcap), expanded sidelink, etc. as well as new indoor/outdoor positioning techniques, passive IoT tag technology, etc. These developments will enable new applications and drive growth in key verticals such as advanced manufacturing, energy and utilities, etc.
Private Networks vs Network Slicing – although network slicing is starting to be introduced over public networks, the technology is not yet mature. However, as slicing become mainstream (and coupled with improvements in the uplink capacity of public networks), it will in time become a lower cost alternative to dedicated private networks for some use cases.

Report Overview

Counterpoint Research’s “Private Networks Market – Market Sizing and Forecasts: 2021-2030” PPT report provides market sizing and multi-year forecasts for the global private networks market divided by vertical, region, technology and spectrum. An overview of the business and technological challenges facing the industry is also presented as well as Counterpoint’s key takeaways about the future of the private networks market.

Table of Contents:

Study Overview and Assumptions
Private Networks: Introduction & Definitions
Market Sizing and Forecasts
- Global Market Revenues
- Market by Verticals
  - Definition of Verticals
  - Forecast Revenues by Vertical
  - Key Drivers per Vertical
- Forecast Revenue by Regions
  - Definition of Regions
  - Forecast Revenue per Region
  - Key Drivers per Region
- Forecast Revenue by Technology
  - 4G versus 5G
- Forecast Revenue by Spectrum
  - Sub 6GHz vs millimetre Wave
- Key Challenges
- Key Takeaways

Related Reports and Blogs

Private Networks Tracker, May 2022

Private Networks – High Expectations Amid and Expanding Ecosystem

5G Network Slicing versus Private Networks: Benefits and Drawbacks

Private Cellular Networks – Devices Key To Growth in Unlicensed Spectrum

5G Mobile Edge Computing – An Emerging Technology Slowly Transitioning To Commercial Reality

Top

Related Posts

Click to listen to the podcast

Podcast Chapter Markers:

Also available for listening/download on:

Related Posts

Key Takeaway No. 1: Platform Limitations

Key Takeaway No. 2: Leveraging GPU Usage in the RAN

Related Reports and Blogs

Look Aside Versus In-line Acceleration

Layer-1 Software Stack

Energy Efficiency – A Critical Metric

Viewpoint

Related Reports

Intelligent Network Automation

RAN Enhancement

Network Management

Network Security

MIMO

Network Slicing

5G Advanced

Drawbacks of AI/ML

Related Posts

Key Features of NetCloud

Related Reports

Private Networks Market by Verticals

Variations by Region

Key Points and Issues

Report Overview

Related Reports and Blogs

Coverage

Insights

Social media

Contact us

Term of Use and Privacy Policy