5G Advanced

Big Role for 5G RedCap in 5G Evolution, Massive IoT Adoption

5G RedCap promises a mix of capabilities including improved throughput, extended battery life and less complexity to power diverse use cases cost-effectively.
5G RedCap (including eRedCap) modules are expected to contribute to one-fourth of total cellular IoT module shipments by 2030.
5G RedCap will serve use cases such as wearables, medical devices, video surveillance, industrial sensors and smart grid applications.

We have come a long way from the first generation (1G) to the fifth generation (5G) of cellular connectivity. Despite being in the initial stages of its rollout, 5G is poised for adoption at a speed not seen by previous cellular standards.

However, from the IoT perspective, 5G is being considered only for high-end applications due to the higher cost and existence of many use cases which need low power and low bandwidth, currently served by LPWAN. We can see the potential that 5G brings to IoT applications in terms of faster connectivity, low latency, reliability and large capacity compared to LTE networks. These benefits make 5G valuable for certain IoT use cases, creating a need for low-end 5G for the LPWAN application.

What are 5G RedCap and 5G eRedCap?

5G RedCap(Reduced Capacity), aka NR-Lite (New Radio-Lite), is a lighter version of the 5G standard that will cater to those use cases where ultra-low latency is not essential, but there is a need for reasonable throughput to support data flows in applications likerouter/CPE, mass-marketautomotive,POSandtelematicsdevices, which are currently addressed by LTE Cat 4. In the upcoming 3GPP Release-18, there will be another version of 5G RedCap, calledeRedCap(enhanced-RedCap), which will serve the use cases currently being served by LTE Cat 1 and LTE Cat 1 bis.

Market opportunity for 5G RedCap

5G RedCap addresses new use cases that cannot be served byadvanced 5Gstandards like eMBB/URLLC and LPWAN. 5G RedCap chipset is already available in the market but we can expect commercial rollout by the first half of 2024. According to Counterpoint Research’sGlobal Cellular IoT Module Forecast, 5G RedCap modules will constitute18%of total cellular IoT module shipments by 2030, indicating a significant market potential, particularly in developing nations where the cost is key to wide technology adoption fordigital transformation.

The subsequent 5G eRedCap is planned for a 2024 introduction, with commercial availability likely by 2026. Expected to bring further innovations to theIoTsegment, 5G eRedCap modules are projected to contribute8%to the total cellular IoT module shipments by 2030.

During the transition phase, network operators will maintain IoT device support through the existing 4G network while focusing on 5G high-end applications like routers/CPE,XR/VRdevices and automotive.

By the end of the decade, cellular IoT will generally migrate to 5G, driven by new use cases offered by the 5G network, with 4G serving as a fallback. The industry is already preparing for this shift, moving away from legacy technology towards newer standards.

5G RedCap ecosystem and applications

We can see a flurry of new announcements from ecosystem players to adopt the 5G RedCap standard. Module and chipset players are forging partnerships to capture the opportunity which will be created by 5G RedCap.Qualcommalways has been at the forefront when it comes to adopting new technologies with big potential. We can see that with its launch of the industry’s firstSDX355G RedCap modem. Qualcomm’s early entry and partnerships with major module vendors will help it to grab more market share in 5G when the mass adoption of 5G RedCap will take place.

5G RedCap will serve the use cases in industrial, enterprise and consumer applications, like smart wearables, medical devices, XR glasses, health monitors, video监视凸轮eras, wireless industrial sensors,utility/smartgrid applications and even Fixed Wireless Access (FWA) and customer premises equipment (CPEs).

5G eRedCap is likely to be preferred for the applications served by4G Cat 1, such as tracking devices, charging stations, micro-mobility and battery-powered sensors.

Conclusion

5G RedCap promises to broaden the 5G ecosystem, facilitating more connections. It fills the gap betweenLPWAandURLLC, simplifying 5G integration in IoT applications. 5G RedCap and eRedCap modules will be cost-effective, enabling OEMs to manufacture less complex, low-cost devices with lower power consumption, something that standard 5G cannot offer.

Though 5G at the IoT level is a few years out, vendors can create devices operable over LTE, with an easy switch to RedCap by changing the communication module. This allows immediate product deployment, with an easy future transition to 5G RedCap as the standard evolves.

5G RedCap’sflexibilityand network advantages, includinglowerlatencyandhigherspeedscompared to previous LTE generations, position it as a superior choice for future mass IoT deployment. Numerous potential connections across consumer, industrial and enterprise verticals such as FWA, CPE and vehicle connectivity will greatly benefit, accelerating IoT adoption on a massive scale.

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5G Advanced and Wireless AI Set To Transform Cellular Networks, Unlocking True Potential

最近的兴趣激增生成AI高lights the critical role that AI will play in future wireless systems. With the transition to 5G, wireless systems have become increasingly complex and more challenging to manage, forcing the wireless industry to think beyond traditional rules-based design methods.

5GAdvanced will expand the role of wireless AI across 5G networks introducing new, innovative AI applications that will enhance the design and operation of networks and devices over the next three to five years. Indeed, wireless AI is set to become a key pillar of5G Advancedand will play a critical role in the end-to-end (E2E) design and optimization ofwirelesssystems. In the case of 6G, wireless AI will become native and all-pervasive, operating autonomously between devices and networks and across all protocols and network layers.

E2E Systems Optimization

AIhas already been used in smartphones and other devices for several years and is now increasingly being used in the network. However, AI is currently implemented independently, i.e. either on the device or in thenetwork. As a result, E2E systems performance optimization across devices and network has not been fully realized yet. One of the reasons for this is that on-device AI training has not been possible until recently.

On-device AI will play a key role in improving the E2E optimization of 5G networks, bringing important benefits foroperatorsand users, as well as overcoming key challenges. Firstly, on-device AI enables processing to be distributed over millions of devices thus harnessing the aggregated computational power of all these devices. Secondly, it enablesAImodel learning to be customized to a particular user’s personalized data. Finally, this personalized data stays local on the device and is not shared with thecloud. This improves reliability and alleviates data sovereignty concerns. On-device AI will not be limited to just smartphones but will be implemented across all kinds of devices from consumer devices to sensors and a plethora of industrial equipment.

New AI-nativeprocessorsare being developed to implement on-device AI and other AI-based applications. A good example isQualcomm’snew Snapdragon X75 5G modem-RF chip, which has a dedicated hardware tensor accelerator. Using Qualcomm’s own AI implementation, this Gen 2 AI processor boosts the X75’s AI performance more than 2.5 times compared to the previous Gen 1 design.

While on-device AI will play a key role in improving the E2E performance of5G networks当人工智能,整体系统优化是有限的is implemented independently. To enable true E2E performance optimization, AI training and inference needs to be done on a systems-wide basis, i.e. collaboratively across both the network and the devices. Making this a reality in wireless system design requires not only AI know-how but also deep wireless domain knowledge. This so-called cross-node AI is a key focus of 5G Advanced with a number of use cases being defined in 3GPP’s Release 18 specification and further use cases expected to be added in later releases.

Wireless AI: 5G Advanced Release 18 Use Cases

3GPP’s Release 18 is the starting point for more extensive use of wireless AI expected in6G. Three use cases have been prioritized for study in this release:

Use of cross-node Machine Learning (ML) to dynamically adapt the Channel State Information (CSI) feedback mechanism between a base station and a device, thus enabling coordinated performance optimization between networks and devices.
Use ofMLto enable intelligent beam management at both the base station and device, thus improving usable network capacity and device battery life.
Use of ML to enhance positioning accuracy of devices in both indoor and outdoor environments, including both direct and ML-assisted positioning.

Channel State Feedback:

CSI是用来确定传播的角色istics of the communication link between a base station and a user device and describes how this propagation is affected by the local radio environment. Accurate CSI data is essential to provide reliable communications. With traditional model-based CSI, the user device compresses the downlink CSI data and feeds the compressed data back to the base station. Despite this compression, the signalling overhead can still be significant, particularly in the case of massive MIMO radios, reducing the device’s uplink capacity and adversely affecting its battery life.

An alternative approach is to use AI to track the various parameters of the communications link. In contrast to model-based CSI, a data driven air interface can dynamically learn from its environment to improve performance and efficiency. AI-based channel estimation thus overcomes many of the limitations of model-based CSI feedback techniques resulting in higher accuracy and hence an improved link performance. The is particularly effective at the edges of a cell.

Implementing ML-based CSI feedback, however, can be challenging in a system with multiple vendors. To overcome this, Qualcomm has developed a sequential training technique which avoids the need to share data across vendors. With this approach, the user device is firstly trained using its own data. Then, the same data is used to train the network. This eliminates the need to share proprietary, neural network models across vendors.Qualcomm has successfully demonstrated sequential training on massive MIMO radios at its 3.5GHz test network in San Diego (Exhibit 1).

Exhibit 1: Realizing system capacity gain even in challenging non-LOS communication

基于ai毫米波梁管理:

The second use case involves the use of ML to improve beam prediction on millimetre wave radios. Rather than continuously measuring all beams, ML is used to intelligently select the most appropriate beams to be measured – as and when needed. A ML algorithm is then used to predict future beams by interpolating between the beams selected – i.e. without the need to measure the beams all the time. This is done at both the device and the base station. As with CSI feedback, this improves network throughput and reduces power consumption.

Qualcomm recently demonstrated the use of ML-based algorithms on its 28GHz massive MIMO test network and showed that the performance of the AI-based system was equivalent to a base case network set-up where all beams are measured.

Precise Positioning:

The third use case involves the use of ML to enable precise positioning.Qualcomm has demonstrated the use of multi-cell roundtrip (RTT) and angle-of-arrival (AoA)-based positioning in an outdoor network in San Diego.The vendor also demonstrated howML-based positioning with RF finger printing can be used to overcome challenging non-line of sight channel conditions in indoor industrial private networks.

An AI-Native 6G Air Interface

6G will need to deliver a significant leap in performance and spectrum efficiency compared to 5G if it is to deliver even faster data rates and more capacity while enabling new 6G use cases. To do this, the 6G air interface will need to accommodate higher-order Giga MIMO radios capable of operating in the upper mid-band spectrum (7-16GHz), support wider bandwidths in new sub-THz 6G bands (100GHz+) as well as on existing 5G bands. In addition, 6G will need to accommodate a far broader range of devices and services plus support continuous innovation in air interface design.

To meet these requirements, the 6G air interface must be designed to be AI native from the outset, i.e. 6G will largely move away from the traditional, model-driven approach of designing communications networks and transition toward a data-driven design, in which ML is integrated across all protocols and layers with distributed learning and inference implemented across devices and networks.

This will be a truly disruptive change to the way communication systems have been designed in the past but will offer many benefits. For example, through self-learning, an AI-native air interface design will be able to support continuous performance improvements, where both sides of the air interface — the network and device — can dynamically adapt to their surroundings and optimize operations based on local conditions.

5G Advanced wireless AI/ML will be the foundation for much moreAIinnovation in 6G and will result in many new network capabilities. For instance, the ability of the 6G AI native air interface to refine existing communication protocols and learn new protocols coupled with the ability to offer E2E network optimization will result in wireless networks that can be dynamically customized to suit specific deployment scenarios, radio environments and use cases. This will a boon for operators, enabling them to automatically adapt their networks to target a range of applications, including various niche and vertical-specific markets.

5G Rollouts in Emerging Economies Aid Q2 Numbers of Ericsson, Nokia

Ericsson and Nokia’s Q2 2023 results are in line with their revised expectations.
The telecom gear manufacturers are convinced that a few short-term hurdles can be managed to drive growth.
The mobile network segment, the largest contributor to both firms’ revenues, witnessed some slowdown in Q2 2023 due to decreased demand from capex-saturated regions.

Nordic telecom giants Ericsson and Nokia announced their Q2 2023 results last week, which were in line with their revised expectations despite lower revenues. The overall market condition remains challenging due to macro uncertainty, but the telecom gear manufacturers are convinced that a few short-term hurdles can be managed to drive growth both in the short term and long term.

Fast5G糊涂的emerging nations such as India were highlights for both the vendors as revenue growth in these regions was able to offset the sales decline in North America and North-East Asia where operators slowed down their network expenditures after several quarters of high investment.

Swedish giant Ericsson generated net sales of $5.9 billion for the quarter, reporting a 9% YoY decline in organic sales. Ericsson’s Finnish counterpart Nokia generated $6.2 billion in revenue for the quarter, which was flat YoY on a constant currency basis.

移动网络段

This segment is based on the core competence of these organizations and is also the largest contributor to both firms’ revenues. It witnessed some slowdown for the two companies in Q2 2023 on the back of decreased demand from capex-saturated regions. Operators in these regions continue to be selective in spending and are depleting their inventories that have been running high after the 2021-2022 boom.

Revenue from Ericsson’s Networks division stood at $3.9 billion. It doubled for emerging markets like India and Southeast Asia but plummeted for regions like North America. India is now Ericsson’s second-biggest market. During the quarter, the company also marked the shipping of 10 million 5G-ready radios.
Revenue from Nokia’s Mobile Networks division stood at $2.85 billion, a slight growth YoY. The increase in revenue due to faster 5G rollouts in India and Europe was able to offset the decline in North America.

Gross margin was impacted for both operators as the sales mix changed drastically. Adapting to changing demand and expecting a recovery in the North American region, both manufacturing firms are looking forward to an improved gross margin by the end of this year.

Cloud software services

Telecom service providers too have been hit by cloud disruption as network evolution has witnessed operators migrating to the cloud. The two Nordic vendors have been at the forefront in assisting operators in transitioning to cloud-native operations, which helps in future-proofing and improving network performance and efficiency.

Ericsson’s revenues from its Cloud and Software Services division stood at $1.39 billion, a marginal increase over the previous year. The sales, for a change, were driven by 5G in the North American region. Ericsson currently leads the global market for5G Standalone Core deploymentswith a majority of operators choosing the Swedish company for their cloud-native 5G SA Core. Ericsson’s managed services, however, took a hit.
Nokia registered $806 million in net sales for its Cloud and Network Services division. Unlike its Swedish counterpart, Nokia’s growth came from the Europe and Middle-East and Africa (MEA) regions, while it faced a decline in the North American region. Nokia too has been actively helping operators worldwide to deploy 5G Standalone Core (just behind Ericsson in the number of deployments), which alongside Enterprise Solutions helped boost its revenues in this segment, marginally offset by declines in the Cloud Services and Business Applications.

Ericsson’s enterprise segment, network APIs and IPR licensing

Last year, Ericsson acquired Vonage, which contributed revenues of nearly $390 million during the quarter, a 12% increase YoY. The company strongly believes that the enterprise segment will continue to grow as it redefines how the capabilities of 5G networks are utilized and paid for by the customers.
Ericsson will also continue to digitize the ecosystem for CSPs by maintaining its investments to build the Global Network Platform (network Application Program Interfaces or APIs). With time, a variety of global network APIs will complement the existing communication APIs like video, voice and SMS to help CSPs better monetize their 5G networks, accelerate 5G network rollout and improve network capex.
The company also signed a5GIPR licensing agreement during this quarter to help validate its IPR portfolio strength.

Nokia’s diverse portfolio – Networks infrastructure and enterprise

Despite facing some short-term challenges and macroeconomic uncertainty, which resulted in a YoY revenue decline, Nokia’s Network Infrastructure segment generated $2.15 billion in revenues and continued to gain market share across the globe.
- The IP networks grew in Europe with increasing sales to enterprise customers.
- The optical networks unit registered a double-digit growth driven by increasing broadband penetration in India.
- The fixed networks unit witnessed a decline on the back of slowing FWA deployments in North America.
Nokia’s revenue from its enterprise customers grew by almost 30% YoY. The company added 90 new enterprise customers this quarter. Its private wireless business reached more than 635 customers.
Nokia also signed a long-term patent license agreement with Apple. Multi-year revenue recognition might start in January 2024.
Nokia also struck an important deal with Red Hat this quarter, where the latter will serve as the primary reference platform to develop, test and deliver core network applications in an attempt to rebalance Nokia’s portfolio.

分析师预期

Network equipment vendors and software providers are looking to transform obstacles into opportunities. Both Ericsson and Nokia are expecting their business performance to improve towards Q4 2023 and to continue improving in the coming years. Inventory correction by operators has been the prime reason for the revenue decline this quarter. But network sales have been able to weather the slowdown as operators need to increase the capacity of their networks.

Counterpoint Research believes that 5G investment has not yet peaked. Over the next few years, the industry will witness the advent of5G Advancedstarting with 3GPP Release 18, operators transitioning to 5G SA, an increase in the number of monetizable 5G use cases,FWA going global, and increased 5G investments in mid-band andmmWave乐队。整个移动行业看好private networks, which present a significant opportunity for operators and vendors alike. Amid the growing geopolitical turbulence, with the West hardening its stand on the “rip and replace” of Chinese networking equipment, Nokia and Ericsson might even see other markets opening up for them. Reducing internal costs and streamlining internal operations remains a challenge for both suppliers. The two should benefit from growing confidence in the enterprise segment. Nokia expects to leverage its leadership in the network infrastructure business and attain market leadership in the fixed-broadband space with its wide variety of ONTs, OLTs and FWA CPEs.

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, theStandalone(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

Huaweihas 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 asprivate networksare 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.

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 18will 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:实现人工智能技术在5克是一项复杂的任务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.

5G Advanced – Stakeholder Collaboration Essential To Maximise ROI For Operators

With more than 230 5G networks deployed worldwide serving 1+ billion end user devices, 5G has become the fastest-growing cellular standard of all time. However, there is an urgent need to prepare for the future to enable operators and enterprises to leverage its full capabilities. 5G Advanced (5.5G) is the next evolutionary step in 5G technology which will introduce new levels of capabilities, enabling operators to generate a return on their 5G investments.

The “10 Gbps Everywhere” Experience

相比传统的5克,5.5 g代表一个10 -fold improvement in performance across the board. This means that 5.5G networks will be able to provide ubiquitous 10 Gbps downlink and 1 Gbps uplink speeds while supporting 100 billion IoT connections – compared to just 10 billion with 5G. In addition, 5.5G is expected to deliver latency and positioning accuracy that are a fraction of the current 5G standard as well as significant reductions in overall network power consumption.

5.5G will provide enhanced connectivity and better user experiences. By leveraging the 10 Gbps downlink throughput and low milli-second latency, 5.5G will bridge the gap between the physical and virtual worlds. Although 5G already provides some immersive services, 5.5G will enable interactive immersive services, such as 24k resolution VR gaming, glasses-free 3D video and 3D online malls.

Benefits for Enterprises

In addition to enhanced connectivity, 5.5G will offer a broad range of new capabilities for enterprises. Counterpoint Research expects a surge in new private network applications as networks are able to leverage the technical innovations enabled by 5.5G. For instance, enterprises will benefit greatly from the 1 Gbps uplink capability, enabling, for example, high-precision AI-based industrial vision inspection, while enhanced positioning with sub-10cm accuracy – both indoors and outdoors – will enable a plethora of new Industry 4.0 applications.

In addition, 5.5G will support three rapidly developing IoT technologies: NB IoT, RedCap and passive IoT tags, an innovative, low cost location sensing technology. A promising application of passive IoT tags is HCS-based Millimetre Wave[1]technology, an integrated sensing and communications technology, which enables centimetre precise positioning of objects, including pedestrians and personal items, livestock, autonomous vehicles, drones, etc. On the network side, enhanced AI/ML capabilities across the RAN, core and network management domains plus new power saving features will result in significant energy savings for operators.

Standards and Spectrum

Technical standards are the bedrock of the telecommunications industry and it is imperative that common standards are adopted worldwide. The standardization of 5.5G via 3GPP Release 18 is on-going. However, the industry must work together to ensure that Release 18 is frozen by the first quarter of 2024 as planned to enable 5.5G to be introduced from 2025 onwards.

Release 18 will be followed by Releases 19 and 20 after which the 3GPP will focus on 6G. Clearly, industry players need to collaborate closely over the next few years in order to define and maximise the technical innovations and capabilities of 5.5G and to ensure new services and use case scenarios are properly supported. This will help to maximise the potential of 5.5G for operators and extend its lifecycle.

Additional spectrum will be required to enable 5.5G to deliver its full potential. Re-farming of legacy 2G and 3G bands will free some lower band spectrum. However, this is not sufficient. More spectrum in the 6GHz and millimetre bands is necessary. With the WRC-23 radio conference taking place in November, it is essential that all stakeholders, including governments and regulators as well as operators and vendors, agree on the best spectrum strategy. Clearly, the 6GHz band should be a key 5.5G target band for the industry. In fact, the 3GPP has already licensed the 6,425-7,125MHz bands and Counterpoint Research expects that the upper part of this band will be identified as an IMT band at WRC-23. Millimetre wave is another key spectrum band for 5.5G and more than 800MHz additional millimetre wave spectrum will likely be needed to enable operators to deliver the 10 Gbps experience.

Networks and Devices

Networks and devices will need to be upgraded to enable 5G Advanced and this will involve further innovation with respect to 5.5G chipset technologies and devices.

5.5G will introduce a plethora of new devices with new capabilities beyond smartphones. Some of these will be full-capability devices while others will have reduced capabilities. For example, Red Cap devices only need to support a shortened set of specific capabilities, for example, video surveillance devices used for industrial quality control, process monitoring, sensing or tracking. However, all players, including chipset and device OEMs, must start working immediately to define the digital requirements for individual vertical use cases and applications in order to ensure that an ecosystem of suppliers is developed.

A significant recent development is the release of millimetre chipsets. For example, Qualcomm recently demonstrated its 5.5G Snapdragon chip, which offers 10 Gbps speed with 10CC carrier aggregation on millimetre wave and 5CC carrier aggregation on sub-6GHz frequencies. Similarly, MediaTek’s chipset offers downlink and uplink speeds of 7.67 Gbps and 3.76 Gbps respectively.

Upgrading Fibre to 5G Advanced

Achieving the “10 Gbps Everywhere” experience” will involve upgrading standards for fixed fibre broadband as well as for 5G RAN and Core. In fact, the evolution of Fibre Broadband 5G (F5G) to all-optical F5.5G has already progressed from proposals to specification design.

Performance improvements in fibre networks will be achieved by agreements on the use of key technologies such as 50G Passive Optical Network (PON) technology, Fibre to the Room (FTTR), etc. 50G PON is being standardized as the next-generation PON by the ITU-T. Together with technologies such as “uplink/downlink symmetry” and “multi-band in one,” this will pave the way for a smooth evolution to F5.5G. Last September, ETSI released its F5G Advanced White Paper and the standards body has been leading the formulation of F5.5G’s first release, Release 3, which will be frozen in first half of 2024.

The development of 5.5G and F5.5G will require a converged fixed/wireless IP network. Work on the definition of a new converged network – tentatively called Net5.5G – has already begun. Both the IETF and the IEEE are working on the first phase of Net5.5G standardization, but consensus is still needed on fixed/wireless bearer technologies such as 800GE backbone, 400GE MAN, etc. as well as on key aspects of other technologies such as WiFi-7, Segment Routing over IPv6 (SRv6), etc. before the new standard is released in 2024. With new capabilities, Net5.5G will enable operators maximise the potential of 5.5G and provide new opportunities for growth.

Viewpoint

The increasing popularity of immersive experiences and the emergence of the metaverse coupled with the demands of enterprise digital transformation mean that 5G networks will soon be unable to support the expected exponential growth in traffic. With 6G around 8-12 years away, 5.5G is the next obvious evolution of 5G and next-generation consumer and B2B opportunities will only be possible if operators and enterprises upgrade to 5.5G.

However, a successful and timely upgrade to 5.5G will require all industry stakeholders – from technical standards bodies, operators, network and device manufacturers to policy developers and regulators – to work closely together and collaborate on key 5.5G enablers, including standards, spectrum, networks and device specifications, etc. Major MNOs will be required to pilot new 5.5G technologies and build business cases. In addition, Counterpoint Research believes that an industry consensus on the digital requirements of new use cases needs to be developed, particularly with respect to enterprise vertical uses cases, as well as a focus on developing a diverse ecosystem of players encompassing all verticals. Finally, closer collaboration between the mobile and fixed telecoms communities will be essential in order to ensure synchronization of standards between wireless and fixed networks.

[1]统一通信传感

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Insights Tag:5G Advanced

Big Role for 5G RedCap in 5G Evolution, Massive IoT Adoption

What are 5G RedCap and 5G eRedCap?

Market opportunity for 5G RedCap

5G RedCap ecosystem and applications

Conclusion

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

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Top

What are 5G RedCap and 5G eRedCap?

Market opportunity for 5G RedCap

5G RedCap ecosystem and applications

Conclusion

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Related Posts:

Related Posts :

Intelligent Network Automation

RAN Enhancement

Network Management

Network Security

MIMO

Network Slicing

5G Advanced

Drawbacks of AI/ML

Related Posts

The “10 Gbps Everywhere” Experience

Benefits for Enterprises

Standards and Spectrum

Networks and Devices

Upgrading Fibre to 5G Advanced

Viewpoint

Related Posts

Coverage

Insights

Social media

Contact us

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