IoT devices in private 5G networks bring new verification tests

With private networks connecting to many IoT devices, testing the device’s user interface requires updating test processes.

Many IoT use cases rely on private 5G networks because they offer greater network control, better security, more reliable performance, and dedicated coverage and capacity as opposed to using a public network. With these advantages, private networks play an important role in specialized use cases for vertical markets.

Based on current GSA data (Figure 1), manufacturing is the top industry vertical for private 5G, followed by mining and education. Other industries are expected to grow as long as one key hurdle — UE operation — can be overcome.

Figure 1. Vertical markets implementing private 5G networks (image: GSA).

Device performance in private 5G is a challenge because, while operator and private 5G networks have similar building blocks, UE is very device-centric based on use case. Additionally, 5G introduces control user plane separation (CUSP), which enables vendors to combine RAN and core network hardware components with software from other sources. With so many varieties in vendors, testing only against 3GPP specifications compliance is not enough.

You should properly test IoT devices against different configurations and combinations and ensure the key performance indicators (KPIs) are properly measured. For engineers, understanding all elements of how users can use the UE, as well as the environments in which they are being deployed, are necessary to ensure devices meet performance parameters.

3GPP Release 16 opens doors
3GPP Release 16 paves the way to private 5G networks. It lets 5G become a substitute for private wired Ethernet, Wi-Fi, and LTE networks by including multiple capabilities for industrial environments.

3GPP also provides standards and guidance on private 5G network deployment. Network architecture and deployment environment affect how you need to test an IoT device’s UE.

The most “private” architecture is a non-public network (NPN), which is an enterprise with a dedicated, on-premises network. 3GPP categorizes NPNs in two ways:

• Stand-alone non-public network (SNPN): this design does not rely on network functions from a public land mobile network (PLMN). An SNPN-enabled UE must be configured with a subscriber identifier (SUPI) and credentials for each subscribed SNPN identified by the combination of PLMN ID and NID (Network identifier).In addition, 3GPP Release 16 specifies the ability for a UE to obtain PLMN services while on a stand-alone non-public RAN. This is related to when the UE has a subscription and credentials to obtain services from both PLMN and SNPN.
• Public network integrated NPN (PNI-NPN): in this model, a PLMN ID recognizes the network, while a closed-access group (CAG) ID locates appropriate cells. A CAG cell broadcasts the designated CAG identifiers per PLMN, which must be supported by UE operating on the network. Only devices that have access credentials for that specific CAG ID can latch on to such cells, thus providing access restriction.

Hybrid private 5G networks use a mix of public mobile network components and dedicated on-premises elements. UE for hybrid networks has its own set of performance parameters, depending on network configuration. Three hybrid designs exist:

• Radio access is shared with the public network; everything else is private.
• The user plane is private, but the control plane and radio access are shared.
• Network slice option; one virtual slice is dedicated to the private network while all other elements reside on a public network.

Because private 5G networks use unlicensed and shared spectrum, device integration can become complex. Systems integrators, who have become key players in private 5G, must verify that UE operates according to specification, elements are integrated properly to guarantee end-to-end quality of service (QoS), and connectivity between UE and network is reliable.

Ensuring UE performance in private 5G
QoS and connectivity take on an added layer of complexity in many private 5G use cases. For example, in a smart factory, there can be robots with hundreds of sensors and machinery with multiple actuators operating in an environment with considerable interference sources. Such a setting has created the need for stress testing to determine how the UE will operate under such extreme conditions.

Given the proprietary nature of many private 5G networks, the prevalence of Open RAN architecture, and data sensitivities, security is a main priority. Many UE manufacturers employ practical security testing, which uses a network simulator to conduct necessary tests, such as functional security measurements. They thoroughly test all security-related functions inside UE or other systems under test to ensure correct behavior and operational robustness (Figure 2).

Figure 2. A typical test configuration for cybersecurity covers functionality, vulnerability, fuzzing, and penetration.

Stress tests and security are primary considerations but hardly the only issues for engineers. Private 5G networks have unique requirements that are more specific and varied than open public networks. Not only are there a tremendous amount of frequency/band combinations that must be considered for sunny-day testing but attention needs to be given to ensure devices that are supposed to work exclusively in an NPN environment do not connect to macro networks and unauthorized UE do not connect to an NPN. For this reason, other tests must be conducted to ensure performance, including:

• Connectivity — 5G IoT devices need proper testing to verify call connection, cell selection/reselection, access control, and any mobility implications in NPN environments. There are new features of 5G NPN that allow the device to selectively connect to the correct network. Verify that a private 5G network is truly only catering to private 5G devices.
• Compatibility — many devices used in a private network support cellular, Wi-Fi, and short-range wireless technologies, such as Bluetooth and Zigbee. Ensuring UE can seamlessly transfer from one technology to another is essential to private 5G network performance.
• Interference — given most private 5G network use cases, interference testing is critical. In addition to supporting multiple technologies, devices must operate in less-than-ideal real-world environments and in mission-critical scenarios. Engineers must have confidence product performance will not degrade due to interference before they are shipped to customers.

Creating a test environment
Implementing a test process to support private 5G UE requires a practical approach. The test environment must simulate real-world scenarios to efficiently verify that the UE will perform when deployed into a private 5G network. Design your test system with intuitive software to more efficiently support various and ever-changing test conditions and evolving standards, which will help to control test costs.

Conclusion
Private 5G networks play a significant role in the fourth industrial revolution. Engineers responsible for developing UE in these use cases must implement test processes that follow 3GPP standards and create real-world scenarios that precisely mirror the specific private 5G network environment. Such an approach will provide greater confidence that the UE will meet established KPIs.

Source: by Emma Lutjen – https://www.testandmeasurementtips.com/iot-devices-in-private-5g-networks-bring-new-verification-tests/

Top Five Questions About 6G Technology

As 5G continues to roll out, work is already well underway on its successor. 6G wireless technology brings with it a promise for a better future. Among other goals, 6G technology intends to merge the human, physical, and digital worlds. In doing so, there is a hope that 6G can significantly aid in achieving the UN Sustainable Development Goals.

This article answers some of the most common questions surrounding 6G and provides more insight into the vision for 6G and how it will achieve these critical goals.

1. What is 6G?

In a nutshell, 6G is the sixth generation of the wireless communications standard for cellular networks that will succeed today’s 5G (fifth generation). The research community does not expect 6G technology to replace the previous generations, though. Instead, they will work together to provide solutions that enhance our lives.

While 5G will act as a building block for some aspects of 6G, other aspects need to be new for it to meet the technical demands required to revolutionize the way we connect to the world in a fashion.

The first area of improvement is speed. In theory, 5G can achieve a peak data rate of 20 Gbps even though the highest speeds recorded in tests so far are around 8 Gbps. In 6G, as we move to higher frequencies – above 100 GHz – the goal peak data rate will be 1,000 Gbps (1 Tbps), enabling use cases like volumetric video and enhanced virtual reality experiences.

In fact, we have already demonstrated an over-the-air transmission at 310 GHz with speeds topping 150 Gbps.

In addition to speed, 6G technology will add another crucial advantage: extremely low latency. That means a minimal delay in communications, which will play a pivotal role in unleashing the internet of things (IoT) and industrial applications.

6G technology will enable tomorrow’s IoT through enhanced connectivity. Today’s 5G can handle one million devices connected simultaneously per square kilometer (or 0.38 square miles), but 6G will make that figure jump up to 10 million.

But 6G will be much more than just faster data rates and lower latency. Below we discuss some of the new technologies that will shape the next generation of wireless communications.

2. Who will use 6G technology and what are the use cases?

We began to see the shift to more machine-to-machine communication in 5G, and 6G looks to take this to the next level. While people will be end users for 6G, so will more and more of our devices. This shift will affect daily life as well as businesses and entire industries in a transformational way.

Beyond faster browsing for the end user, we can expect immersive and haptic experiences to enhance human communications. Ericsson, for example, foresees the emergence of the “internet of senses,” the possibility to feel sensations like a scent or a flavor digitally. According to one Next Generation Mobile Networks Alliance (NGMN) report, holographic telepresence and volumetric video – think of it as video in 3D – will also be a use case. This is all so that virtual, mixed, and augmented reality could be part of our everyday lives.

However, 6G technology will likely have a bigger impact on business and industry – benefiting us, the end users, as a result. With the ability to handle millions of connections simultaneously, machines will have the power to perform tasks they cannot do today.

The NGMN report anticipates that 6G networks will enable hyper-accurate localization and tracking. This could bring advancements like allowing drones and robots to deliver goods and manage manufacturing plants, improving digital health care and remote health monitoring, and enhancing the use of digital twins.

Digital twin development will be an interesting use case to keep an eye on. It is an important tool that certain industries can use to find the best ways to fix a problem in plants or specific machines – but that is just the tip of the iceberg. Imagine if you could create a digital twin of an entire city and perform tests on the replica to assess which solutions would work best for problems like traffic management. Already in Singapore, the government is working to build a 3D city model that will enable a smart city in the future.

3. What do we need to achieve 6G?

New horizons ask for new technology. It is true that 6G will greatly benefit from 5G in areas such as edge computing, artificial intelligence (AI), machine learning (ML), network slicing, and others. At the same time, we need changes to match new technical requirements.

The most sensible demand is understanding how to work in the sub terahertz frequency. While 5G needs to operate in the millimeter wave (mmWave) bands of 24.25 GHz to 52.6 GHz to achieve its full potential, the next generation of mobile connectivity will likely move to frequencies above 100 GHz in the ranges called sub-terahertz and possibly as high as true terahertz.

Why does this matter? Because as we go up in frequency, the wave behaves in a different way. Before 5G, cellular communications used only spectrum below 6GHz, and these signals can travel up to 10 miles. As we go up into the mmWave frequency band, the range is dramatically reduced to around 1,000 feet. With sub THz signals like those being proposed for 6G, the distance the waves can travel tends to be smaller – think 10s to 100s of feet not 1000s.

That said, we can maximize the signal propagation and range by using new types of antennas. An antenna’s size is proportional to the signal wavelength, so as the frequency gets higher and the wavelength gets shorter, antennas are small enough to be deployed in a large number. In addition, this equipment uses a technique known as beamforming – directing the signal toward one specific receiver instead of radiating out in all directions like the omnidirectional antennas commonly used prior to LTE.

Another area of interest is designing 6G networks for AI and ML. 5G networks are starting to look at adding AI and ML to existing networks, but with 6G we have the opportunity to build networks from the ground up that are designed to work natively with these technologies.

According to one International Telecommunication Union (ITU) report, the world will generate over 5,000 exabytes of data per month by 2030. Or 5 billion terabytes a month. With so many people and devices connected, we will have to rely on AI and ML to perform tasks such as managing data traffic, allowing smart industrial machines to make real-time decisions and use resources efficiently, among other things.

Another challenge 6G aims to tackle is security – how to ensure the data is safe and that only authorized people can have access to it – and solutions to make systems foresee complex attacks automatically.

One last technical demand is virtualization. As 5G evolves, we will start to move to the virtual environment. Open RAN (O-RAN) architectures are moving more processing and functionality into the cloud today. Solutions like edge computing will be more and more common in the future.

4. Will 6G technology be sustainable?

Sustainability is at the core of every conversation in the telecommunications sector today. It is true that as we advance 5G and come closer to 6G, humans and machines will consume increasing data. Just to give you an idea of our carbon footprint in the digital world, one simple email is responsible for 4 grams of carbon dioxide in the atmosphere.

However, 6G technology is expected to help humans improve sustainability in a wide array of applications. One example is by optimizing the use of natural resources in farms. Using real-time data, 6G will also enable smart vehicle routing, which will cut carbon emissions, and better energy distribution, which will increase efficiency.

Also, researchers are putting sustainability at the center of their 6G projects. Components like semiconductors using new materials should decrease power consumption. Ultimately, we expect the next generation of mobile connectivity to help achieve the United Nations’ Sustainable Development Goals.

5. When will 6G be available?

The industry consensus is that the first 3rd Generation Partnership Project (3GPP) standards release to include 6G will be completed in 2030. Early versions of 6G technologies could be demonstrated in trials as early as 2028, repeating the 10-year cycle we saw in previous generations. That is the vision made public by the Next G Alliance, a North American initiative of which Keysight is a founding member, to foster 6G development in the United States and Canada.

Before launching the next generation of mobile connectivity into the market, international bodies discuss technical specifications to allow for interoperability. This means, for example, making sure that your phone will work everywhere in the world.

The ITU and the 3GPP are among the most well-known standardization bodies and hold working groups to assess research on 6G globally. Federal agencies also play a significant role, regulating and granting spectrum for research and deployment.

Amid all this, technology development is another aspect to keep in mind. Many 6G capabilities demand new solutions that often use nontraditional materials and approaches. The process of getting these solutions in place will take time.

The good news? The telecommunications sector is making fast progress toward the next G.

Here at Keysight, for instance, we are leveraging our proven track record of collaboration in 5G and Open RAN to pioneer solutions needed to create the foundation of 6G. We partner with market leaders to advance testing and measurement for emerging 6G technologies. Every week, we come across a piece of news informing that a company or a university has made a groundbreaking discovery.

The most exciting thing is that we get an inch closer to 6G every day. Tomorrow’s internet is being built today. Join us in this journey; it is just the beginning.

Learn more about the latest advancements in 6G research.

View additional multimedia and more ESG storytelling from Keysight Technologies on 3blmedia.com.

SOURCE: Keysight Technologies – https://www.accesswire.com/717630/Top-Five-Questions-About-6G-Technology – 28 09 22

5G-Advanced (not yet defined by ITU-R) will include AI/ML and network energy savings

Despite no work even started by ITU-R WP 5D (responsible for all IMT xG’s), global technology intelligence firm ABI Research expects that 75% of 5G base stations will be upgraded to 5G-Advanced by 2030, five years after the estimated commercial launch in 2025.

3GPP approved their Release-18 package in December 2021, making the official start of 5G-Advanced with the planned freeze date in December 2023.

But that really doesn’t mean much since Release-16 was frozen in June 2020, yet 2+ years later the spec for URLLC in the RAN has not been completed.  As a result, neither 3GPP or ITU-R recommendation M.2150 (formerly IMT 2020) meets the ITU-R M.2410 performance requirements for URLLC use case.  Also, less than 5% of deployed 5G networks are 5G SA with a 5G Core network, which is required for implementation of ALL 5G features, e.g. network slicing, security, automation, as well as MEC.

Some network operators like Verizon have already admitted that it could take up to a decade before they profit from their 5G investments.

ABI Research claims that 5G-Advanced will bring continuous enhancements to mobile network capabilities and use case-based support to help mobile operators with 5G commercialization, long-term development of Artificial Intelligence (AI)/Machine Learning (ML), and network energy savings for a fully automated network and a sustainable future.

“In 5G-Advanced, Extended Reality (XR) applications will promise monetary opportunities to both the consumer markets with use cases like gaming, video streaming, as well as enterprise opportunities such as remote working and virtual training. Therefore, XR applications are a major focus of 3GPP working groups to significantly improve XR-specific traffic performance and power consumption for the mass market adoption,” explains Gu Zhang, 5G & Mobile Network Infrastructure Principal Analyst at ABI Research. “Another noticeable feature is AI/ML which will become essential for future networks given the predictive rapid growth in 5G network usage and use case complexities which can’t be managed by legacy optimization approaches with presumed models. System-level network energy saving is also a critical aspect as operators need to reduce the deployment cost but assure network performance for various use cases.”

The upgrade of 5G network infrastructure is expected to be faster in the consumer market than in enterprises. ABI Research forecasts that 75% of 5G base stations will be upgraded to 5G-Advanced, while in the enterprise market the ratio is about half. 5G-Advanced devices per radio base station will quickly gain traction around 2024 to 2026 at the early stage of the commercial launch because devices will grow more aggressively than network deployments over the period.

“The commercial launch of 5G-Advanced will take two or three years, but the competition has already started, Zhang points out. “Taking AI/ML development as an example, industrial leaders such as Ericson, Huawei, Nokia, ZTE, and Qualcomm have trialed their solutions with mobile operators across the world. Ongoing development in this area will continue to bring improvements on traffic throughputs, network coverage, power saving, anomaly detection, etc.”

Different from previous generations, 5G creates an ecosystem for vertical markets such as automotive, energy, food and agriculture, city management, government, healthcare, manufacturing, and public transportation. “The influence on the domestic economy from the telco players will be more significant than before and that trend will continue for 5G-Advanced onward. Network operators and vendors should keep close to the regulators and make sure all parties involved grow together when the time-to-market arrives,” Zhang concludes.

These findings are from ABI Research’s 5G-Advanced and the Road to 6G application analysis report. This report is part of the company’s 5G & Mobile Network Infrastructure research service, which includes research, data, and ABI Insights. Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

References:

https://www.abiresearch.com/press/5g-advanced-to-launch-in-2025-and-75-of-5g-networks-will-upgrade-by-2030/

https://www.sdxcentral.com/articles/analysis/5g-advanced-evolution-advances-abi-says/2022/07/

https://www.nokia.com/networks/5g/5g-advanced/

https://www.news18.com/news/tech/what-is-5g-advanced-how-is-it-different-from-5g-all-details-explained-4954355.html

https://www.itu.int/pub/R-REP-M.2410

Want to get ahead in service management and orchestration?

Service management and orchestration (SMO) is an exciting and innovative development in our industry. A new concept defined within the O-RAN Alliance architecture for RAN management, SMO provides automation and orchestration of the Open RAN domain and is critical for driving automation within the operator network. We explore how to extend it across different RAN architectures, and why now’s the time for CSPs to ‘get one’s skates on’ to stay ahead of the competition.

The capability to drive automation within the operator network makes SMO immediately attractive to every mobile operator who wants to increase automation to drive down operational costs and, ultimately, improve profitability.

However, the fundamental problem with SMO is that it’s designed to automate Open RAN networks which only make up one to two percent of deployed networks today.

To put that in context, it’s like inventing a perpetual motion machine to replace the internal combustion engine and then, in the small print, explaining it only works in Porches or Ferraris. Great news in principle, but not actually that helpful for 98 percent of motorists.

So, the key question is: “Is there a way to make the SMO concept applicable to 100 percent of radio access networks deployed globally?”

Thankfully, we think that there is an answer – a simple answer – to this ultimate question. And that answer is to extend this to address the existing, purpose-built RAN.

But where are with this now?

Open RAN is an exciting and disruptive development in our industry, but the technology is still at the early stage. We see a couple of new, greenfield entrants, looking to deploy new 100 percent Open RAN networks. We also see lots of leading communication service providers (CSPs) testing the technology, often in remote, rural areas or controlled environments such as university or business campuses.

Industry analyst firm, Gartner, developed an interesting model to look at the introduction of new technologies called the ‘Gartner Hype Cycle.’  The hype cycle outlines a number of stages between the ‘innovation trigger,’ where the new technology is conceived and the ‘plateau of productivity,’ where the technology is widely adopted and starts to deliver results. It’s difficult to estimate where Open RAN is on the cycle, but widespread media coverage and speculation makes it feel like Open RAN is just passing the ‘peak of inflated expectations:’ essentially, it’s still in a relatively early stage.

 

Open RAN and the O-RAN Alliance architecture

Let’s briefly look at the O-RAN Alliance. The O-RAN Alliance is a service provider-led consortium of over 300 companies working on the Open RAN concept. They have defined an Open RAN architecture which, as the name suggests, has openness at its heart.

Figure 1. The O-RAN Alliance – Open RAN architecture

The Open RAN architecture defines, or is in the process of defining, a number of open interfaces imaginatively named the O1, O2, A1 and R1 interfaces. These interfaces are not yet standardized in the same way that, for example, the 3GPP Release 17 is standardized, but there are strong aspirations for openness and standardization within the O-RAN Alliance.

Ericsson is a major contributor to the O-RAN Alliance service management and orchestration working groups, and we have a number of ideas about how the SMO concept should be implemented.

 

Expanding SMO to support multi-technology

At the start of this blog, I gave the analogy that O-RAN Alliance SMO is a little bit like building a perpetual motion engine to replace the internal combustion engine, but having it only work with one brand of high-performance car. Brilliant, innovative, but not actually useful for 98 percent of motorists.

But what would happen if you could take the SMO concept and extend the automation capabilities brought about by SMO – non-real-time RAN intelligent controllers (non-RT-RIC) and their automation rApps – into the domain of the existing, purpose-built RAN networks? In effect, building a perpetual motion engine for every motorist.

At Ericsson, we believe this is very much possible. To achieve this, we will start with the existing centralized self-optimizing networks (C-SON) and network design and optimization (NDO) applications already deployed in networks. Today, these applications are often deployed on tightly integrated application platforms to perform specific automation operations. The SMO architecture effectively allows you to recreate these applications as RAN automation applications or rApps, as well as standardizing the underlying application environment: the SMO’s non-RT-RIC, which uses an R1 interface that enables interoperability between platforms and vendors.

“This report…analyzes the evolution of C-SON modules and use cases becoming apps in the RAN Intelligent Controller (RIC) needed in open RAN, open virtual RAN, and software defined RAN (SD-RAN) architectures”
– Stéphane Téral, Chief Analyst

A recent report from analyst firm Light Counting highlights the expectation that existing C-SON applications will become applications in the RAN intelligent controller (RIC).

The ability to run rApps to optimize both Open RAN and existing purpose-built 4G and 5G networks is profound. If this can be achieved one of the major barriers to adoption of the SMO will be crossed – scalability.

Obviously, it’s hard to justify investment in an automation platform for a tiny percentage of your current network. This is why we are expanding the scope of the SMO to cover Open RAN and purpose-built RAN.  –  this results in the investment, covering the entire network with the benefits of automation being similarly applied to the entire network. At Ericsson, we call this approach multi-technology: the ability to automate and orchestrate Open RAN and purpose-built RAN.

 

Avoiding SMO siloes

Another major barrier to adoption is the management of multiple vendors’ RAN. For Open RAN networks the open A1 and O1 interfaces enable a common approach to managing the new Open RAN technologies, but the challenge is how to manage multiple vendors’ purpose-built 4G and 5G RAN networks.

Figure 2. Extending SMO to address purpose-built RAN

These networks have a vendor specific equipment management system, or EMS, such as Ericsson’s own Ericsson Network Manager (ENM) or Nokia’s NetAct. There have been successful approaches, such as the Operational Support Systems interworking initiative (OSSii) to encourage interworking between equipment vendors, but not every vendor is actively participating. However, what OSSii proves is that there are ways to manage multiple vendors EMS and RAN.

The reason this is important is that without effective interworking in the existing purpose-built RAN networks, there will be a tendency to deploy vendor specific SMOs. Having one SMO per equipment vendor is counter-intuitive and would appear to add to complexity rather than reduce it.

Our belief is that an operator network should have a single SMO: deployed on-premises, in the cloud, or as-a-Service (aaS), depending on the wishes of the service provider. This single SMO will handle the new Open RAN technologies via the open A1 and O1 interfaces and purpose-built networks from multiple vendors through their own proprietary interfaces.

This approach gives a single ‘pane of glass’ operational overview of the network, which reduces complexity and ultimately drives down operational costs.

At Ericsson, we call this approach multi-vendor.

 

Recommendations to accelerate SMO adoption

In Ericsson’s new SMO white paper, An intelligent platform: The use of O-RAN’s SMO as the enabler for openness and innovation in the RAN domain, we outline five recommendations to accelerate the adoption of SMO and shorten the hype cycle:

1. Deploy in complex RAN environments: we recommend deploying SMO into complex multi-vendor, multi-technology networks where the automation platform can create the greatest operational impact.
2. Target addressable areas of high operational costs: network deployment and network operation are two of the largest addressable areas of OPEX, we recommend that these areas are early targets for new automation rApps and xApps.
3. Leverage proven use cases: there is an opportunity to take proven capabilities and deploy them at scale by taking the existing centralized SON and network design and optimization applications and using them to create new automation applications running on a common platform.
4. Use best in class applications: use automation applications from a wide selection of developers including network equipment vendors, CSPs and third-party specialists. Be prepared to test similar applications and select those that best utilize any tools that accelerate open innovation such as software development toolkits, partner development ecosystems and even marketplaces to maximize choice.
5. Deploy across multi-technology networks: SMO provides maximum return on investment (ROI) where it enables automation for 100 percent of the network. Having high levels of automation in an Open RAN network is a way of ensuring that the new technology is future-proof. However, adding high levels of automation to the existing purpose-built RAN that makes up more than 98 percent of CSP networks today is also highly desirable.

Learn more

• To find out more about extending SMO to multi-technology, multi-vendor RAN read Ericsson’s new white paper, An intelligent platform: The use of O-RAN’s SMO as the enabler for openness and innovation in the RAN domain
• Read the Ericsson blog post: How open development ecosystems drive rApp innovation
• Read the Ericsson blog post: Why intelligent RAN automation is going to rock your world

 

Source: https://www.ericsson.com/en/blog/2021/11/want-to-get-ahead-in-service-management-and-orchestration 12 11 21
By: Santiago Rodriguez – Head of Strategic Projects / Justin Paul – Senior Solutions Marketing Manager OSS

Rethinking of Optical Transport Network Design for 5G/6G Mobile Communication

Driven by the increasing use of emerging smart mobile applications, mobile technology is continuously and rapidly advancing towards the next generation communication systems such as 5G and 6G. However, the transport network, which needs to provide low latency and reliable connectivity between hundreds of thousands of cell sites and the network core, has not advanced at the same pace. This article provides insight into how we can solve the fundamental challenges of implementing cost-optimal transport and 5G and beyond mobile networks simultaneously while satisfying the network and user requirements irrespective of the radio access network’s architecture.

1. Introduction
The fifth generation (5G) mobile technology promises higher bandwidth capacities, lower latencies, and higher reliability for emerging time-sensitive and mission-critical applications [1]. According to Cisco, the number of connected devices will reach 500 billion by 2030, a number significantly higher than the expected world population. Providing connectivity for billions of devices and satisfying the stringent quality of service requirements of diverse applications is becoming a significant challenge. To address this ongoing issue, academia and industry recently started researching the sixth generation (6G) mobile technology [2].

6G mobile technology expects to provide 100 Gbps or higher data rates and ultra-low latency over ubiquitous 3D coverage areas. However, the transport network, which interconnects the cell sites with the network core, has not progressed at the same pace. For this reason, it has been identified as a potential bottleneck for high-performance and cost-efficient network deployment. Therefore, in order to fulfill the new and miscellaneous requirements of 5G and beyond mobile networks that are arising from the deployment of hundreds of thousands of wireless cell sites, increasingly diverse architectures, and sophisticated applications and services, “the transport network will require to undergo an evolutionary change with a complete rethink of the design and deployment strategies”.

In particular, optical technologies are positioned to be the most sustainable for the transport segment of 5G and beyond networks due to their inherent features such as secure data transmission and high capacity [3]. However, with the decreasing cell size and higher numbers of cells deployed, it becomes harder and more expensive to connect them to the network core with the optical network [4]. This is made even more challenging, given the diverse architectural requirements of radio access networks (RANs) of next-generation (xG) mobile networks [5]. Therefore, new methods are needed to jointly plan and design an xG RAN with its transport network while reducing the cost and meeting all the service requirements. In this article, we present new strategies that we can be applied to plan a 5G/6G network together with its optical transport infrastructure to provide ubiquitous, reliable, and cost-effective access for emerging applications.

2. Network Planning Strategies for 5G and Beyond
To support the unprecedented growth of mobile traffic, RAN has been undergoing diverse changes that directly impact the performance and cost of the network. Consequently, several RAN architectures have been proposed in the recent years. A future RAN may consist of multiple RAN architectures as shown in Fig. 1. The network architecture shown Fig.1 comprises multiple RAN architectures and their transport networks.

Figure 1. Radio Access Network Architectures for 5G and Beyond

The centralized RAN (C-RAN) is one such paradigm in which the processing of mobile signals is carried out at the centralized base band unit (BBU) placed at the central office (CO). Consequently, in C-RAN, the remote radio heads (RRHs) placed at the cell sites have a very limited set of functions [6]. With the help of virtualization technology few other variants of C-RAN have been proposed and standardized including Open and Intelligent RAN (O-RAN) and fog RAN (F-RAN) [7,8]. In these RANs, the BBU can be further separated and radio control functions can be moved into the cloud, while the radio processing functions remain closer to the cell site, enabling functionalities like network slicing [7].

There is one thing in common irrespective of the RAN architecture in use, that is the requirement of reliable and cost-effective data transportation between RRH and CO. Each of these RAN architectures have different functions at BBU, the RRH and/or distribution node placed between the RRH and CO. These different options are standardized under the IEEE 1914 Next Generation Fronthaul Interface – NGFI (x-haul) [9]. Figure 2 shows 10 different RAN functional split options under consideration. For example, O-RAN considers functional split options 7.2 and 6 [7].

Figure 2. Functional Split Options [12]

The most important thing here is that the bandwidth required for the transport network varies with the functional split option. For example, under average 5G data rates, Option 1 where all functionalities are implemented at the cell site/RRH requires 1 Gbps of bandwidth in the transport (x-haul) link as we only need to transmit packetized processed data. On the other hand, if we use Option 8 where all the functions are centralized at BBU we need more than 800 Gbps on the transport link [10]. In Options 1-6, the bandwidth requirement scales with the number of active users and their traffic because only upper layer functions are centralized, while physical layer functions stay in the RRH. However, after Option 6, bandwidth requirement of x-haul increases exponentially as the bandwidth requirement depends on the physical parameters such as number of antenna ports because now, we move the physical layer functions to the BBU.

Now the challenge is to find the cost-effective and efficient transport network solution because different optical technologies can be used in the transport network depending on the x-haul bandwidth requirement. For example, up to Option 7.3, we can use the cost-effective point to multipoint (PMP) optical links such as passive optical networks (PON) and low data rate point to point (PtP) optical links. However, beyond Option 7.3, we have to use multiple high capacity PtP optical links [11]. In order to achieve the cost-effectiveness and effective operation, we now need to deploy several functional split options and optical technologies in a single network. This is a major challenge.

Therefore, we developed a generalized optimization framework that can be used to jointly plan both 5G wireless and optical transport network for all the functional split options whilst satisfying diverse network requirements such as coverage and capacity. Our framework is also capable of leveraging the resources associated with existing infrastructure to reduce cost and can be used in situations where we have limited availability of existing fiber resources. So that we can apply the framework to analyze the deployment cost and optimally plan the network under any given scenario and to identify the most effective design option. We develop the framework as an integer linear program. In this article, we present an overview of the framework. The details of the mathematical formulation of the framework can be found in [12].

Figure 3. The Optimization framework

Figure 3 illustrates the major components of the framework. As can be seen, the objective of the model is to minimize the total deployment cost of both 5G and its optical transport network. The total deployment cost consists of the cost of feeder fiber, the cost of distribution fiber (deployment of new fiber routes including trenching), the cost of equipment and installations we need at several locations such as CO (BBU, Optical line terminal (OLT) and line cards), splitter location (Splitter /MUX) and cell site (RRH and Optical network unit (ONU)). The framework also consists of multiple constraints to satisfy the network requirements such as coverage, capacity, split ratios, PtP/PMP connectivity and number of BBUs and OLTs in one location. The framework outputs the optimal locations of RRH/ONUs, splitters/MUXs, BBU placement and optimal fiber routes to deploy transport network.

3. Evaluating effectiveness of planning strategies

Figure 4. (a) Data set (b) Example of an optimal solution

We then validate our framework by using it to plan 5G and its optical transport network for a suburban area in Eastern Australia. The map of the considered suburban area with over 6000 residents is shown in Fig. 4 (a). The major intersections shown in brown dots are the possible locations for cell deployment, where a light pole/traffic light pole can be used to easily deploy small cells. Orange triangles are the existing fiber access points which are considered as the possible locations for splitter/MUX deployment and black squares are the locations of existing COs. The cost components we applied for the analyses can be found in [12].

We use CPLEX to solve the framework under diverse deployment scenarios considering the fixed wireless access deployment. For example, Fig. 4 (b) shows the optimal solution for the deployment scenario when we have functional split Options 1 to 6, dense wavelength division multiplexing (DWDM) PON as the transport network, RRHs have 300m radius and set the coverage requirement to 99% and per household capacity requirement to 25 Mbps. Figure 4 (b) shows the optimally selected locations for BBU, MUX, RRHs and fiber routes. Feeder fibers are optimally selected from the set of existing fiber network (black dotted lines) and the blue lines show the logical connectivity of optimally selected distribution fiber links that connect the optimally selected RRHs with the selected MUX locations.

We also analyzed the optimal deployment cost under different deployment scenarios as we wanted to find how the optimal cost varies with network requirements such as capacity, cell radius, coverage, and functional splits. Here, we highlight two sets of results. Figure 5 shows the normalized optimal cost under different optical transport networks and different cell radius. In this scenario, we set the coverage requirement to 99% and per user capacity requirement to 50 Mbps. We also consider different splits of DWDM PON and look at how the deployment cost is distributed among optical x-haul and wireless network. It can be seen that x-haul contributes to higher cost compared to the wireless network. For all the cell radius considered, 10G DWDM PON options save 30% of the deployment cost compared to the 10G PtP deployment.

Figure 5. Optimal cost with 10G WDM PON and PtP used as a transport network with coverage 99%

Figure 6. Optimal Cost Vs. Capacity requirement

Figure 6 shows how the deployment cost varies when we have diverse user capacity requirements. We considered a cell radius of 200m and both PtP and 10G PON-based transport network options were analyzed. As expected, the deployment cost increases when the capacity requirement increases. However, the deployment cost of PON-based option is significantly lower compared to the PtP scenario and the cost difference increases with the capacity requirement. For example, at 100 Mbps, the PON-based option saves more than 40% of the deployment cost compared to the PtP case. It is also clear that the main cost contributor among all the deployment scenarios is the installation/use of fiber routes.

After analyzing our results, we have identified that functional split Options 1-6 utilizing PON-based solution can save 30-40% of deployment cost compared to Option 7.2. The details of the evaluation results can be found in [12]. Most importantly, we developed a tool that can be used to optimally plan a 5G/6G and its transport network by simply entering the relevant cost values and the network requirements such as expected coverage percentage, capacity and split ratio.

4. Conclusion
This article highlighted the importance of joint optimal planning of optical transport and wireless networks considering the diverse network requirements in realizing cost-effective deployment of emerging mobile networks. We presented a versatile framework that can be used to provide a cost optimal solution irrespective of the functional split or optical technology in use. The research work presented in the article provide insight into best network design strategies that can be used in planning and dimensioning of optical transport networks for 5G and beyond networks.

Source: https://futurenetworks.ieee.org/tech-focus/april-2021/rethinking-of-optical-transport-network-design-for-5g-6g-mobile-communication 20 04 21

The importance of interoperability testing for O-RAN validation

Being ‘locked in’ to a proprietary RAN has put mobile network operators (MNOs) at the mercy of network equipment manufacturers.

Throughout most of cellular communications history, radio access networks (RANs) have been dominated by proprietary network equipment from the same vendor or group of vendors. While closed, single-vendor RANs may have offered some advantages as the wireless communications industry evolved, this time has long since passed. Being “locked in” to a proprietary RAN has put mobile network operators (MNOs) at the mercy of network equipment manufacturers and become a bottleneck to innovation.

Eventually, the rise of software-defined networking (SDN) and network function virtualization (NFV) brought to the network core greater agility and improved cost efficiencies. But the RAN, meanwhile, remained a single-vendor system.

In recent years, global MNOs have pushed the adoption of an open RAN (also known as O-RAN) architecture for 5G. The adoption of open RAN architecture offers a ton of benefits but does impose additional technical complexities and testing requirements.

This article examines the advantages of implementing an open RAN architecture for 5G. It also discusses the principles of the open RAN movement, the structural components of an open RAN architecture, and the importance of conducting both conformance and interoperability testing for open RAN components.

The case for open RAN

The momentum of open RAN has been so forceful that it can be challenging to track all the players, much less who is doing what.

The O-RAN Alliance — an organization made up of more than 25 MNOs and nearly 200 contributing organizations from across the wireless landscape — has since its founding in 2018 been developing open, intelligent, virtualized, and interoperable RAN specifications. The Telecom Infra Project (TIP) — a separate coalition with hundreds of members from across the infrastructure equipment landscape ­—maintains an OpenRAN project group to define and build 2G, 3G, and 4G RAN solutions based on general-purpose hardware-neutral hardware and software-defined technology. Earlier this year, TIP also launched the Open RAN Policy Coalition, a separate group under the TIP umbrella focused on promoting policies to accelerate and spur adoption innovation of open RAN technology.

Figure 1. The major components of the 4G LTE RAN versus the O-RAN for 5G. Source: Keysight Technologies

In February, the O-RAN Alliance and TIP announced a cooperative agreement to align on the development of interoperable open RAN technology, including the sharing of information, referencing specifications, and conducting joint testing and integration efforts.

The O-RAN Alliance has defined an O-RAN architecture for 5G and has defined a 5G RAN architecture that breaks down the RAN into several sections. Open, interoperable standards define the interfaces between these sections, enabling mobile network operators, for the first time, to mix and match RAN components from several different vendors. The O-RAN Alliance has already created more than 30 specifications, many of them defining interfaces.

Interoperable interfaces are a core principle of open RAN.  Interoperable interfaces allow smaller vendors to quickly introduce their own services. They also enable MNOs to adopt multi-vendor deployments and to customize their networks to suit their own unique needs. MNOs will be free to choose the products and technologies that they want to utilize in their networks, regardless of the vendor. As a result, MNOs will have the opportunity to build more robust and cost-effective networks leveraging innovation from multiple sources.

Enabling smaller vendors to introduce services quickly will also improve cost efficiency by creating a more competitive supplier ecosystem for MNOs, reducing the cost of 5G network deployments. Operators locked into a proprietary RAN have limited negotiating power. Open RANs level the playing field, stimulating marketplace competition, and bringing costs down.

Innovation is another significant benefit of open RAN. The move to open interfaces spurs innovation, letting smaller, more nimble competitors develop and deploy breakthrough technology. Not only does this create the potential for more innovation, it also increases the speed of breakthrough technology development, since smaller companies tend to move faster than larger ones.

Figure 2. Test equipment radio in the O-RAN conformance specification.

Other benefits of open RAN from an operator perspective may be less obvious, but no less significant. One notable example is in the fronthaul — the transport network of a Cloud-RAN (C-RAN) architecture that links the remote radio heads (RRHs) at the cell sites with the baseband units (BBUs) aggregated as centralized baseband controllers some distance (potentially several miles) away. In the O-RAN Alliance reference architecture, the IEEE Radio over Ethernet (RoE) and the open enhanced CPRI (eCPRI) protocols can be used on top of the O-RAN fronthaul specification interface in place of the bandwidth-intensive and proprietary common public radio interface (CPRI). Using Ethernet enables operators to employ virtualization, with fronthaul traffic switching between physical nodes using off-the-shelf networking equipment. Virtualized network elements allow more customization.

Figure 1 shows the layers of the radio protocol stack and the major architectural components of a 4G LTE RAN and a 5G open RAN. Because of the total bandwidth required and fewer antennas involved, the CPRI data rate between the BBU and RRH was sufficient for LTE. With 5G,  higher data rates and the increase in the number of antennas due to massive multiple-input / multiple-output (MIMO) means passing a lot more data back and forth over the interface. Also, note that the major components of the LTE RAN, the BBU and the RRH, are replaced in the O-RAN architecture by O-RAN central unit (O-CU), the O-RAN distributed unit (O-DU), and the O-RAN radio unit (O-RU), all of which are discussed in greater detail below.

The principles and major components of an open RAN architecture

As stated earlier (and implied by the name), one core principle of the open RAN architecture is openness — specifically in the form of open, interoperable interfaces that enable MNOs to build RANs that feature technology from multiple vendors. The O-RAN Alliance is also committed to incorporating open source technologies where appropriate and maximizing the use of common-off-the-shelf hardware and merchant silicon while minimizing the use of proprietary hardware.

A second core principle of open RAN, as described by the O-RAN Alliance, is the incorporation of greater intelligence. The growing complexity of networks necessitates the incorporation of artificial intelligence (AI) and deep learning to create self-driving networks. By embedding AI in the RAN architecture, MNOs can increasingly automate network functions and minimize operational costs. AI also helps MNOs increase the efficiency of networks through dynamic resource allocation, traffic steering, and virtualization.

The three major components of the O-RAN for 5G (and retroactively for LTE) are the O-CU, O-DU, and the O-RU.

• The O-CU is responsible for the packet data convergence protocol (PDCP) layer of the protocol.
• The O-DU is responsible for all baseband processing, scheduling, radio link control (RLC), medium access control (MAC), and the upper part of the physical layer (PHY).
• The O-RU is the component responsible for the lower part of the physical layer processing, including the analog components of the radio transmitter and receiver.

Two of these components can be virtualized. The O-CU is the component of the RAN that is always centralized and virtualized. The O-DU is typically a virtualized component; however, virtualization of the O-DU requires some hardware acceleration assistance in the form of FPGAs or GPUs.

At this point, the prospects for virtualization of the O-RU are remote. But one O-RAN Alliance working group is planning a white box radio implementation using off-the-shelf components. The white box enables the construction of an O-RU without proprietary technology or components.

Interoperability testing required

While the move to open RAN offers numerous benefits for MNOs, making it work means adopting rigorous testing requirements. A few years ago, it was sufficient to simply test an Evolved Node B (eNB) as a complete unit in accordance with 3GPP requirements. But the introduction of the open RAN and distributed RANs change the equation, requiring testing each component of the RAN in isolation for conformance to the standards and testing combinations of components for interoperability.

Why test for both conformance and interoperability? In the O-RAN era, it is essential to determine both that the components conform to the appropriate standards in isolation and that they work together as a unit. Skipping the conformance testing step and performing only interoperability testing would be like an aircraft manufacturer building a plane from untested parts and then only checking to see if it flies.

Conformance testing usually comes first to ensure that all the components meet the interface specifications. Testing each component in isolation calls for test equipment that emulates the surrounding network to ensure that the component conforms to all capabilities of the interface protocols.

Conformance testing of components in isolation offers several benefits. For one thing, conformance testing enables the conduction of negative testing to check the component’s response to invalid inputs, something that is not possible in interoperability testing. In conformance testing, the test equipment can stress the components to the limits of their stated capabilities — another capability not available with interoperability testing alone. Conformance testing also enables test engineers to exercise protocol features that they have no control over during interoperability testing.

The conformance test specification developed by the O-RAN Alliance open fronthaul interfaces working group features several sections with many test categories to test nearly all 5G O-RAN elements.

Interoperability testing of a 5G O-RAN is like interoperability testing of a 4G RAN. Just as 4G interoperability testing amounts to testing the components of an eNB as a unit, the same procedures apply to testing a gNodeB (gNB) in 5G interoperability testing. The change in testing methodology is minimal.

Conformance testing, however, is significantly different for 5G O-RAN and requires a broader set of equipment. For example, the conformance test setup for an O-RU includes a vector signal analyzer, a signal source, and an O-DU emulator, plus a test sequencer for automating the hundreds of tests included in a conformance test suite. Figure 2 shows the test equipment radio in the O-RAN conformance test specification.

Conclusion: Tools and Methodologies Matter

As we have seen, the open RAN movement has considerable momentum and is a reality in the era of 5G. while the adoption of open RAN architecture brings significant benefits in terms of greater efficiency, lower costs, and an increase in innovation. However, the test and validation of a multi-vendor open RAN is no small endeavor. Simply cobbling together a few instruments and running a few tests is not an adequate solution. Testing each section individually to the maximum of its capabilities is critical.

Choosing and implementing the right equipment for your network requires proper testing with the right tools, methodologies, and strategies.

Source: https://www.ept.ca/features/the-importance-of-interoperability-testing-for-o-ran-validation/ 06 04 21

What Will It Take to Make 6G a Reality by 2030? A Theoretical Conversation

From telepresence holograms to machines as the network’s primary users, 6G will be very different from today’s network. But does the hardware for this network even exist?

While 6G networks are a nascent concept in 2021, corporations like Samsung and Nokia are already analyzing the potential hardware and software options required to make 2030 a target year for early commercialization.

 

Applications of 1G networks from the1980s to the proposed applications of 6G in 2030. Image used courtesy of Arxiv

 

6G Requires Unprecedented Throughput

The Internet of Things will be a significant driving force to develop the sixth-generation network infrastructure.

For the first time, machines will be the principal users of the network resources in “machine-to-machine (M2M) communication.” Secondary human users may use the expanded bandwidth for virtual/augmented reality, telepresence holography, and tactile control of robotics for high-precision tasks.

Today, 5G technologies rely on disaggregated network functions in the radio access network (RAN), edge computing, and virtualized network hardware to reduce cost and increase performance. These functions exist as trade-offs to each other to deliver the 5G network as it is now: enhanced mobile broadband, ultra-low latency communications, and M2M communications.

 

Visual of the design requirements for 6G. The 5G trade-offs requiring various RAN configurations are replaced by a heterogeneous online system. Image used courtesy of Samsung

 

However, for 6G to succeed, the trade-offs will need to be eliminated, allowing for a fully-connected, always-online world. This connectivity represents an exponential increase in RAN throughput and computes capability that isn’t accomplishable with discrete hardware/software functions.

A new spectrum is necessary to overcome these challenges, and engineers will need to develop accommodating hardware and metamaterials. Finally, AI and ML technology for 6G technologies will need to be “taught” and deployed in as few as nine years.

 

Pushing Microwave Frequencies to the Limits

In 2019 the FCC released the Spectrum Horizons Experimental Radio License to support the development of terahertz frequency communications technologies.

According to a group of researchers associated with the IEEE, terahertz frequencies are one contender for communication technologies applied to 6G, the other being visible light communications (VLC).

Once thought of as unusable frequencies, the terahertz bands may become a reality in the next decade. However, according to Samsung, major roadblocks exist in the propagation and reception of frequencies beyond 100 GHz, including:

• Path loss due to absorption and loss of line-of-sight (LoS)
• Electronics hardware dimensions, inducing losses in transmission, reception, and processing
• Advanced antenna lens and beamforming requirements to achieve LoS
• RF channel optimization, allocation, and the possible development of a replacement for orthogonal frequency-division multiplexing (OFDM)

 

LoS analysis of the various frequency bands operating today, both in practice and experimental. Image used courtesy of Arxiv

 

According to the IEEE research group, visible light communications will offer a cost-effective alternative to THz technologies by modulating LEDs and piggybacking on existing RF applications indoors to extend cellular coverage.

 

6G Requires New Hardware and Materials Research

Printed electronics may be key to the adoption of THz technologies, according to IDTechEx. These printed electronics would take the form of reconfigurable intelligent surfaces (RIS), measure only a few microns thick, and apply to many of the issues surrounding LoS communications.

 

A future metasurface structure steers the wave from an antenna in a more direct beam. Samsung believes RIS could replace antennas as well. Image used courtesy of Samsung

 

Metamaterials could address the issue of beamforming the signals for propagation to targets at various elevations on the ground, in the air, or around obstacles.

 

A high-level depiction of RIS. Developers will need to deploy RIS in high densities to overcome line-of-sight obstacles. This will re-broadcast or redirect signals to their target. Image used courtesy of Samsung 

 

Network Requirements for Disaggregated Compute

Covering the generational shift to 6G, Peter Vetter (head of Nokia Bell Labs access and devices research) notes something of particular interest to hardware designers.

In a webinar, he explains that within the next 10 years, designers may see the advent of specialized hardware performing one function with limited onboard compute, aggregated into one application. This compatibility means that the network itself would be responsible for cloud edge processing and decision-making based on the increased hardware outputs.

 

Climbing the 6G Mountain Requires All Engineering Disciplines

To overcome the challenges associated with high-reliability, high-throughput 6G networks, engineers from all disciplines will need to work together. Hardware engineers will develop sensor and RF technology, AI/ML experts will develop self-optimizing networks, and computer engineers will create disaggregated compute capability.

Regulatory bodies such as the FCC will also play an essential role in protecting and allocating the spectrum required to facilitate this new digital domain.

5G may be here in 2021, but 6G development is accelerating already, and 2030 doesn’t seem so far away.

Source: https://www.allaboutcircuits.com/news/6g-reality-2030-theoretical-conversation/ 16 03 21

AIMM Leverages Reconfigurable Intelligent Surfaces Alongside Machine Learning

Reconfigurable Intelligent Surfaces (RIS) goes by several names as an emerging technology. According to Marco Di Renzo, CNRS Research Director at CentraleSupélec of Paris-Saclay University, it is also known as Intelligent Reflecting Surfaces (IRS), Large Intelligent Surfaces (LIS), and Holographic MIMO. However it is referred to though, it’s a key factor in an ambitious collaborative project entitled AI-enabled Massive MIMO (AIMM), on which Di Renzo is about to start work.

Early Stages of RIS Research

Di Renzo refers to “RIS,” as does the recently established Emerging Technology Initiative of the Institute of Electrical and Electronics Engineers (IEEE). Furthermore, Samsung used that same acronym in its recent 6G Vision whitepaper, calling it a means “to provide a propagation path where no [line of sight] exists.” The description is arguably fitting considering there is no clear line of sight in the field, with a lot still to be discovered.

The intelligent surfaces, as the name suggests, possess reconfigurable reflection, refraction, and absorption properties with regard to electromagnetic waves. “We are doing a lot of fundamental research. The idea is really to push the limits and the main idea is to look at future networks,” Di Renzo said.

The project itself is two years in length, slated to conclude in September 2022. It’s also large in scale, featuring a dozen partners including InterDigital and BT, the former of which is steering the project. Arman Shojaeifard, Staff Engineer at InterDigital, serves as AIMM Project Lead. According to Shojaeifard, the “MIMO” in the name is just as much a nod to Holographic MIMO (or RIS) as it is to Massive MIMO.

“We are developing technologies for both in AIMM: Massive MIMO, which comprises sector antennas with many transmitters and receivers, and RIS, utilising reconfigurable reflect arrays for Holographic MIMO radios and smart wireless environments,” he explained.

Whereas reflective surfaces have generally been around for a while to passively improve coverage indoors, RIS is a recent development, with NTT Docomo demonstrating the first 28GHz 5G meta-structure reflect array in 2018. Compared to passive reflective surfaces, RIS also has many other potential use cases.

Slide courtesy of Marco Di Renzo, CentraleSupélec

“Two main applications of metasurfaces as reconfigurable reflect arrays are considered in AIMM,” said Shojaeifard. “One is to create smart wireless environments by placing the reflective surface between the base station and terminals to help existing antenna system deployments. And two is to realise low-complexity and energy-efficient Holographic MIMO. This could be a terminal or even a base station.”

Optimising the Operation through Machine Learning

The primarily European project includes clusters of companies in Canada, the UK, Germany, and France. In France specifically there are three partners: Nokia Bell Labs; Montimage, a developer of tools to test and monitor networks; and Di Renzo’s CentraleSupélec, for which he serves as Principal Investigator. Whereas Nokia is contributing to the machine-learning-based air interface of the project, Di Renzo is working on the RIS component.

“From a technological point of view, the idea is that you have many antennas in Massive MIMO, but behind each of them there is a lot of complexity, such as baseband digital signal processing units, RF chains, and power amplifiers,” he said. “What we want to do with [RIS] is to try to get the same benefits or close to the same benefits as Massive MIMO, as much as we can, but […] get the complexity, power consumption, and cost as low as we can.”

The need for machine learning is two-pronged, according to Di Renzo. It helps resolve a current deficiency regarding the analytical complexity of accurately modeling the electromagnetic properties of the surfaces. It also helps to optimise the surfaces when they’re densely deployed in large-scale wireless networks through the use of algorithms.

“[RIS] can transform today’s wireless networks with only active nodes into a new hybrid network with active and passive components working together in an intelligent way to achieve sustainable capacity growth with low cost and power consumption,” he said.

Ready, AIMM…

According to Shojaeifard, the AIMM consortium is targeting efficiency dividends and service differentiation through AI in 5G and Beyond-5G Radio Access Networks. He said InterDigital’s work here is closely aligned with its partnerships with University of Southampton and Finland’s 6G Flagship research group.

Meanwhile, Di Renzo believes the findings to be made can provide the interconnectivity and reliability required for applications such as those in industrial environments. As for the use of RIS in telecoms networks, it’s a possibility at the very least.

“I can really tell you that this is the moment where we figure out whether [RIS] is going to be part of the use of the telecommunications standards or not,” he said. “During the summer, many initiatives were created within IEEE concerning [RIS] and a couple of years ago for machine learning applied to communications.”

“We will see what is going to happen in one year or a couple of years, which is the time horizon of this project…This project AIMM really comes at the right moment on the two issues that are really relevant, the technology which is [RIS] and the algorithmic component which is machine learning […] It’s the right moment to get started on this project.”

Source: https://www.6gworld.com/exclusives/aimm-leverages-reconfigurable-intelligent-surfaces-alongside-machine-learning/ 01 12 20

NTT Docomo’s 5G RAN Infrastructure

In this post we will look at the 5G Infrastructure that Docomo is using in their network. It is detailed in their latest Technical Journal here. In this post we will look at the infrastructure part only.

The 5G network configuration is shown in Figure 4. With a view to 5G service development, NTT DOCOMO developed a Central Unit (CU) that consolidates the Base Band (BB) signal processing section supporting 5G, extended existing BB processing equipment known as high-density Base station Digital processing Equipment (BDE), and developed a 5G Radio Unit (RU) having signal transmit / receive functions. Furthermore, to have a single CU accommodate many RUs, NTT DOCOMO developed a 5G version of the FrontHaul Multiplexer (FHM) deployed in LTE. Each of these three types of equipment is described below.

1) CU
(a) Development concept: With the aim of achieving a smooth rollout of 5G services, NTT DOCOMO developed a CU that enables area construction without having to replace existing equipment while minimizing the construction period and facility investment. This was accomplished by making maximum use of the existing high-density BDE that performs BB signal processing, replacing some of the cards of the high-density BDE, and upgrading the software to support 5G.
(b) CU basic specifications: An external view of this CU is shown in Photo 1. This equipment has the features described below (Table 3). As described above, this equipment enables 5G-supporting functions by replacing some of the cards of the existing high-density BDE. In addition, future software upgrades will load both software supporting conventional 3G/LTE/LTE-Advanced and software supporting 5G. This will enable the construction of a network supporting three generations of mobile communications from 3G to 5G with a single CU.

The existing LTE-Advanced system employs advanced Centralized RAN (C-RAN) architecture proposed by NTT DOCOMO. This architecture is also supported in 5G with the connection between CU and RUs made via the fronthaul. Standardization of this fronthaul was promoted at the Open RAN (O-RAN) Alliance jointly established in February 2018 by five operators including NTT DOCOMO.  Since the launch of 5G services, the fronthaul in the NTT DOCOMO network was made to conform to these O-RAN fronthaul specifications that enable interoperability between different vendors, and any CU and RU that conform to these specifications can be interconnected regardless of vendor. The specifications for inter-connecting base-station equipment also con-form to these O-RAN specifications, which means that a multi-vendor connection can be made between a CU supporting 5G and a high-density BDE supporting LTE-Advanced. This enables NTT DOCOMO to deploy a CU regardless of the vendor of the existing high-density BDE and to quickly and flexibly roll out service areas where needed while making best use of existing assets. In addition, six or more fronthaul connections can be made per CU and the destination RU of each fronthaul connection can be se-lected. Since 5G supports wideband trans-mission beyond that of LTE-Advanced, the fronthaul transmission rate has been extend-ed from the existing peak rate of 9.8 Gbps to a peak rate of 25 Gbps while achieving a CU/RU optical distance equivalent to that of the existing high-density BDE.
2) RU
(a) Development concept: To facilitate flexible area construction right from the launch of 5G services, NTT DOCOMO developed the low-power Small Radio Unit (SRU) as the RU for small cells and developed, in particular, separate SRUs for each of the 3.7 GHz, 4.5 GHz, and 28 GHz frequency bands provided at the launch of the 5G pre-commercial service in September 2019. Furthermore, with an eye to early expansion of the 5G service area, NTT DOCOMO developed the Regular power Radio Unit (RRU) as the RU for macrocells to enable the efficient creation of service areas in suburbs and elsewhere.
A key 5G function is beamforming that aims to reduce interference with other cells and thereby improve the user’s quality of experience. To support this function, NTT DOCOMO developed a unit that integrates the antenna and 5G radio section (antenna-integrated RU). It also developed a unit that separates the antenna and 5G radio section (antenna-separated RU) to enable an RU to be placed alongside existing 3G/LTE/LTE-Advanced Radio Equipment (RE) and facilitate flexible installation even for locations with limited space or other constraints.

(b) SRU basic specifications: As described above, NTT DOCOMO developed the SRU to enable flexible construction of 5G service areas. It developed, in particular, antenna-integrated SRUs to support each of the 3.7 GHz, 4.5 GHz, and 28 GHz frequency bands provided at the launch of the 5G pre-commercial service and antenna-separated SRUs to support each of the 3.7 GHz and 4.5 GHz frequency bands (Photo 2). These two types of SRUs have the following features (Table 4).

The antenna-integrated RU is equipped with an antenna panel to implement the beamforming function. In the 3.7 GHz and 4.5 GHz bands, specifications call for a maximum of 8 beams, and in the 28 GHz band, for a maximum of 64 beams. An area may be formed with the number of transmit/receive beams tailored to the TDD Config used by NTT DOCOMO. In addition, the number of transmit/receive branches is 4 for the 3.7 GHz and 4.5 GHz bands and 2 for the 28 GHz band, and MIMO transmission/reception can be performed with a maximum of 4 layers for the former bands and a maximum of 2 layers for the latter band.
The antenna-separated SRU is configured with only the radio as in conventional RE to save space and facilitate installation. With this type of SRU, the antenna may be installed at a different location. Moreover, compared to the antenna-integrated SRU operating in the same frequency band, the antenna-separated SRU reduces equipment volume to 6.5ℓ or less. The antenna-separated SRU does not support the beamforming function, but features four transmit/receive branches the same as the antenna-integrated SRU for the same frequency band.
(c) RRU basic specifications: The RRU was developed in conjunction with the 5G service rollout as high-power equipment compared with the SRU with a view to early expansion of the 5G service area (Photo 3). This type of equipment has the following features (Table 5).

Compared with existing Remote Radio Equipment (RRE) for macrocells, the volume of RRU equipment tends to be larger to support 5G broadband, but in view of the latest electronic device trends, NTT DOCOMO took the lead in developing and deploying an antenna-separated RRU that could save space and reduce weight. Maximum transmission power is 36.3 W/100 MHz/branch taking the radius of a macrocell area into account. The RRU features four transmit/receive branches and achieves the same number of MIMO transmission/reception layers as the antenna-separated SRU.
NTT DOCOMO also plans to deploy an antenna-integrated RRU at a later date. The plan here is to construct 5G service areas in a flexible manner making best use of each of these models while taking installation location and other factors into account.
3) 5G FHM
The 5G FHM is equipment having a multiplexing function for splitting and combining a maximum of 12 radio signals on the fronthaul. It was developed in conjunction with the 5G service rollout the same as RRU (Photo 4).

If no 5G FHM is being used, each RU is accommodated as one cell, but when using a 5G FHM, a maximum of 12 RUs can be accommodated as one cell in a CU. At the launch of 5G services, this meant that more RUs could be accommodated in a single CU when forming a service area in a location having low required radio capacity (Figure 5). Additionally, since all RUs transmit and receive radio signals of the same cell, the 5G FHM can inhibit inter-RU interference and the occurrence of Hand-Over (HO) control between RUs as in the conventional FHM. Furthermore, the 5G FHM supports all of the 5G frequency bands, that is, the 3.7 GHz, 4.5 GHz, and 28 GHz bands, which means that service areas can be constructed in a flexible manner applying each of these frequency bands as needed.

All the fronthaul and other interfaces that Docomo used in their network was based on O-RAN alliance specifications. In a future post, we will look at some of the details.

Source: https://www.telecomsinfrastructure.com/2020/11/ntt-docomos-5g-ran-infrastructure.html 26 11 20

Open RAN 101–Role of RAN Intelligent Controller: Why, what, when, how?

History

In 2G and 3G, the mobile architectures had controllers that were responsible for RAN orchestration and management. With 4G, overall network architecture became flatter and the expectation was that, to enable optimal subscriber experience, base stations would use the X2 interface to communicate with each other to handle resource allocation. This created the proverbial vendor lock-in as different RAN vendors had their own flavor of X2, and it became difficult for an MNO to have more than one RAN vendor in a particular location. The O-RAN Alliance went back to the controller concept to enable best-of-breed Open RAN.

Why

As many 5G experiences require low latency, 5G specifications like Control and User Plane Separation (CUPS), functional RAN splits and network slicing, require advanced RAN virtualization combined with SDN. This combination of virtualization (NFV and containers) and SDN is necessary to enable configuration, optimization and control of the RAN infrastructure at the edge before any aggregation points. This is how the RAN Intelligent Controller (RIC) for Open RAN was born – to enable eNB/gNB functionalities as X-Apps on northbound interfaces. Applications like mobility management, admission control, and interference management are available as apps on the controller, which enforces network policies via a southbound interface toward the radios. RIC provides advanced control functionality, which delivers increased efficiency and better radio resource management. These control functionalities leverage analytics and data-driven approaches including advanced ML/AI tools to improve resource management capabilities.

The separation of functionalities on southbound and northbound interfaces enables more efficient and cost-effective radio resource management for real-time and non-real-time functionalities as the RIC customizes network optimization for each network environment and use case.

Virtualization (NVF or containers) creates software app infrastructure and a cloud-native environment for RIC, and SDN enables those apps to orchestrate and manage networks to deliver network automation for ease of deployment.

Though originally RIC was defined for 5G OpenRAN only, the industry realizes that for network modernization scenarios with Open RAN, RIC needs to support 2G 3G 4G Open RAN in addition to 5G.

The main takeaway: RIC is a key element to enable best-of-breed Open RAN to support interoperability across different hardware (RU, servers) and software (DU/CU) components, as well as ideal resource optimization for the best subscriber QoS.

What

There are 4 groups in the O-RAN Alliance that help define RIC architecture, real-time and non-real-time functionality, what interface to use and how the elements are supposed to work with each other.

Source: O-RAN Alliance

Working group 1 looks after overall use cases and architecture across not only the architecture itself, but across all of the working groups. Working group 2 is responsible for the Non-real-time RAN Intelligent Controller and A1 Interface, with the primary goal that Non-RT RIC is to support non-real-time intelligent radio resource management, higher layer procedure optimization, policy optimization in RAN, and providing AI/ML models to near-RT RIC. Working group 3 is responsible for  the Near-real-time RIC and E2 Interfaces, with the focus to define an architecture based on Near-Real-Time Radio Intelligent Controller (RIC), which enables near-real-time control and optimization of RAN elements and resources via fine-grained data collection and actions over the E2 interface. Working group 5 defines the Open F1/W1/E1/X2/Xn Interfaces to provide fully operable multi-vendor profile specifications which are compliant with 3GPP specifications.

The RAN Intelligent Controller consists of a Non-Real-time Controller (supporting tasks that require > 1s latency) and a Near-Real Time controller (latency of <1s). Non-RT functions include service and policy management, RAN analytics and model-training for the Near-RT RAN.

Near Real-Time RAN Intelligent Controller (Near-RT RIC) is a near‐real‐time, micro‐service‐based software platform for hosting micro-service-based applications called xApps. They run on the near-RT RIC platform. The near-RT RIC software platform provides xApps cloud-based infrastructure for controlling a distributed collection of RAN infrastructure (eNB, gNB, CU, DU) in an area via the O-RAN Alliance’s E2 protocol (“southbound”). As part of this software infrastructure, it also provides “northbound” interfaces for operators: the A1 and O1 interfaces to the Non-RT RIC for the management and optimization of the RAN. The self-optimization is responsible for necessary optimization-related tasks across different RANs, utilizing available RAN data from all RAN types (macros, Massive MIMO, small cells). This improves user experience and increases network resource utilization, key for consistent experience on data-intensive 5G networks.

Source: O-RAN Alliance

The Near-RT RIC hosts one or more xApps that use the E2 interface to collect near real-time information (on a UE basis or a cell basis). The Near-RT RIC control over the E2 nodes is steered via the policies and the data provided via A1 from the Non-RT RIC. The RRM functional allocation between the Near-RT RIC and the E2 node is subject to the capability of the E2 node and is controlled by the Near-RT RIC. For a function exposed in the E2 Service Model, the near-RT RIC may monitor, suspend/stop, override or control the node via Non-RT RIC enabled policies. In the event of a Near-RT RIC failure, the E2 Node will be able to provide services, but there may be an outage for certain value-added services that may only be provided using the Near-RT RIC. The O-RAN Alliance has a very active WIKI page where it posts specs and helpful tips for developers and operators that want to deploy Near-RT RIC.

Non-Real-Time RAN Intelligent Controller (Non-RT RIC) functionality includes configuration management, device management, fault management, performance management, and lifecycle management for all network elements in the network. It is similar to Element Management (EMS) and Analytics and Reporting functionalities in legacy networks. All new radio units are self-configured by the Non-RT RIC, reducing the need for manual intervention, which will be key for 5G deployments of Massive MIMO and small cells for densification. By providing timely insights into network operations, MNOs use Non-RT RIC to better understand and, as a result, better optimize the network by applying pre-determined service and policy parameters. Its functionality is internal to the SMO in the O-RAN architecture that provides the A1 interface to the Near-Real Time RIC. The primary goal of Non-RT RIC is to support intelligent RAN optimization by providing policy-based guidance, model management and enrichment information to the near-RT RIC function so that the RAN can be optimized. Non-RT RIC can use data analytics and AI/ML training/inference to determine the RAN optimization actions for which it can leverage SMO services such as data collection and provisioning services of the O-RAN nodes.

Trained models and real-time control functions produced in the Non-RT RIC are distributed to the Near-RT RIC for runtime execution. Network slicing, security and role-based Access Control and RAN sharing are key aspects that are enabled by the combined controller functions, real-time and non-real-time, across the network.

The main takeaway: Near-RT RIC is responsible for creating a software platform for a set of xApps for the RAN; non-RT RIC provides configuration, management and analytics functionality. For Open RAN deployments to be successful, both functions need to work together.

How

O-RAN defined overall RIC architecture consists of four functional software elements: DU software function, multi-RAT CU protocol stack, the near-real time RIC itself, and orchestration/NMS layer with Non-Real Time RIC. They all are deployed as VNFs or containers to distribute capacity across multiple network elements with security isolation and scalable resource allocation. They interact with RU hardware to make it run more efficiently and to be optimized real-time as a part of the RAN cluster to deliver a better network experience to end users.

Source: O-RAN Alliance

An A1 interface is used between the Orchestration/NMS layer with non-RT RIC and eNB/gNB containing near-RT RIC. Network management applications in non-RT RIC receive and act on the data from the DU and CU in a standardized format over the A1 Interface. AI-enabled policies and ML-based models generate messages in non-RT RIC and are conveyed to the near-RT RIC.

The control loops run in parallel and, depending on the use case, may or may not have any interaction with each other. The use cases for the Non-RT RIC and Near-RT RIC control loops are fully defined by O-RAN, while for the O-DU scheduler control loop – responsible for radio scheduling, HARQ, beamforming etc. – only the relevant  interactions with other O-RAN nodes or functions are defined to ensure the system acts as a whole.

Multi-RAT CU protocol stack function supports protocol processing and is deployed as a VNF or a CNF. It is implemented based on the control commands from the near-RT RIC module. The current architecture uses F1/E1/X2/Xn interfaces provided by 3GPP. These interfaces can be enhanced to support multi-vendor RANs, RUs, DUs and CUs.

The Near-RT RIC leverages embedded intelligence and is responsible for per-UE controlled load-balancing, RB management, interference detection and mitigation. This provides QoS management, connectivity management and seamless handover control. Deployed as a VNF, a set of VMs, or CNF, it becomes a scalable platform to on-board third-party control applications. It leverages a Radio-Network Information Base (R-NIB) database which captures the near real-time state of the underlying network and feeds RAN data to train the AI/ML models, which are then fed to the Near-RT RIC to facilitate radio resource management for subscriber. Near-RT RIC interacts with Non-RT RIC via the A1 interface to receive the trained models and execute them to improve the network conditions.

The Near-RT RIC can be deployed in a centralized of distributed model, depending on network topology.

Source: O-RAN Alliance

Bringing it all together: Near-RT RIC provides a software platform for xAPPS for RAN management and optimization. A large amount of network and subscriber data and Big Data, counters, RAN and network statistics, and failure information are available with L1/L2/L3 protocol stacks, which are collected and used for data features and models in Non-RT RIC. Non-RT RIC acts as a configuration layer to DU and CU software as well as via the E2 standard interface. They can be learned with AI and/or abstracted to enable intelligent management and control the RAN with Near-RT RIC. Some of the example models include, but are not limited to, spectrum utilization patterns, network traffic patterns, user mobility and handover patterns, service type patterns along with the expected quality of service (QoS) prediction patterns, and RAN parameters configuration to be reused, abstracted or learned in Near-RT RIC from the data collected by Near-RT RIC.

This abstracted or learned information is then combined with additional network-wide context and policies in Near-RT RIC to enable efficient network operations via Near-RT RIC.

The main takeaway: Non-RT RIC feeds data collected from RAN elements into Near-RT RIC and provides element management and reporting. Near-RT RIC makes configuration and optimization decisions for multi-vendor RAN and uses AI to anticipate some of the necessary changes.

When

The O-RAN reference architecture enables not only next generation RAN infrastructures, but also the best of breed RAN infrastructures. The architecture is based on well-defined, standardized interfaces that are compatible with 3GPP to enable an open, interoperable RAN. RIC functionality delivers intelligence into the Open RAN network with near-RT RIC functionality providing real-time optimization for mobility and handover management, and non-RT RIC providing not only visibility into the network, but also AI-based feeds and recommendations to near-RT RIC, working together to deliver optimal network performance for optimal subscriber experience.

Recently, ATT and Nokia tested the RAN E2 interface and xApp management and control, collected live network data using the Measurement Campaign xApp, neighbor relation management using the Automated Neighbor Relation (ANR) xApp, and tested RAN control via the Admission Control xApp – all over the live commercial network.

Source: Nokia

AT&T and Nokia ran a series of xApps at the edge of AT&T’s live 5G mmWave network on an Akraino-based Open Cloud Platform. The xApps used in the trial were designed to improve spectrum efficiency, as well as offer geographical and use case-based customization and rapid feature onboarding in the RAN.

AT&T and Nokia are planning to officially release the RIC into open source, so that other companies and developers can help develop the RIC code.

Parallel Wireless is another vendor that has developed RIC, near-RT and non-RT. What makes their approach different is that the controller works not only for 5G, but also for legacy Gs: 2G, 3G, and 4G. Their xApps or microservices are virtualized functions of BSC for 2G, RNC for 3G, x2 gateway for 4G among others.

Source: Parallel Wireless

As a result of having 2G 3G 4G and 5G related xApps, 5G-like features can be delivered today to 2G, 3G, and 4G networks utilizing this RIC including: 1. Ultra-low latency and high reliability for coverage or capacity use cases. 2. Ultra-high throughput for consumer applications such as real-time gaming. 3. Scaling from millions to billions of transactions, with voice and data handling that seamlessly scales up from gigabytes to petabytes in real-time, with consistent end user experience for all types of traffic. The solution is a pre-standard near real-time RAN Intelligent Controller (RIC) and will adapt O-RAN open interfaces with the required enhancements and can be upgraded to them via a software upgrade. This will enable real-time radio resource management capabilities to be delivered as applications on the platform.

Main takeaway: The RIC platform provides a set of functions via xApps and using pre-defined interfaces that allow for increased optimizations in Near-RT RIC through policy-driven, closed loop automation, which leads to faster and more flexible service deployments and programmability within the RAN. It also helps strengthen a multi-vendor open ecosystem of interoperable components for a disaggregated and truly open RAN.

Source: https://www.rcrwireless.com/20200730/opinion/readerforum/open-ran-101-role-of-ran-intelligent-controller-why-what-when-how-reader-forum 31 07 20