Is AI-RAN the future of telco?

 AI-RAN has emerged recently as an interesting evolution of telecoms networks. The Radio Access Network (RAN) has been undergoing a transformation over the last 10 years, from a vertical, proprietary highly concentrated market segment to a disaggregated, virtualized, cloud native ecosystem.

Product of the maturation of a number of technologies, including telco cloudification, RAN virtualization and open RAN and lately AI/ML, AI-RAN has been positioned as a means to disaggregate and open up further the RAN infrastructure.

This latest development has to be examined from an economic standpoint. RAN accounts roughly for 80% of a telco deployment (excluding licenses, real estate...) costs. 80% of these costs are roughly attributable to the radios themselves and their electronics. The market is dominated by few vendors and telecom operators are exposed to substantial supply chain risks and reduced purchasing power.

The AI RAN alliance was created in 2024 to accelerate its adoption. It is led by network operators (T-Mobile, Softbank, Boost Mobile, KT, LG Uplus, SK Telecom...) telecom and IT vendors (Nvidia, arm, Nokia, Ericsson Samsung, Microsoft, Amdocs, Mavenir, Pure Storage, Fujitsu, Dell, HPE, Kyocera, NEC, Qualcomm, Red Hat, Supermicro, Toyota...).

If you are familiar with this blog, you already know of the evolution from RAN to cloud RAN and Open RAN, and more recently the forays into RAN intelligence with the early implementations of near and non real time RAN Intelligence Controller (RIC). 

AI-RAN goes one step further in proposing that the specialized electronics and software traditionally embedded in RAN radios be deployed on high compute, GPU based commercial off the shelf servers and that these GPUs manage the complex RAN computation (beamforming management, spectrum and power optimization, waveform management...) and double as a general high compute environment for AI/ML applications that would benefit from deployment in the RAN (video surveillance, scene, object, biometrics recognition, augmented / virtual reality, real time digital twins...). It is very similar to the edge computing early market space.

The potential success of AI-RAN relies on a number of techno / economic assumptions:

For Operators:

• It is desirable to be able to deploy RAN management, analytics, optimization, prediction, automation algorithms in a multivendor environment that will provide deterministic, programmable results.
• Network operators will be able and willing to actively configure, manage and tune RAN parameters.
• Deployment of AI-RAN infrastructure will be profitable (combination of compute costs being offloaded by cost reduction by optimization and new services opportunities).
• AI-RAN power consumption, density, capacity, performance will exceed traditional architectures in time.
• Network Operator will be able to accurately predict demand and deploy infrastructure in time and in the right locations to capture it.
• Network Operators will be able to budget the CAPEX / OPEX associated with this investment before revenue materialization.
• An ecosystem of vendors will develop that will reduce supply chain risks

For vendors:

• RAN vendors will open their infrastructure and permit third parties to deploy AI applications.
• RAN vendors will let operators and third parties program the RAN infrastructure.
• There is sufficient market traction to productize AI-RAN.
• The rate of development of AI and GPU technologies will outpace traditional architecture.
• The cost of roadmap disruption and increased competition will be outweighed by the new revenues or is the cost to survive.
• AI-RAN represents an opportunity for new vendors to emerge and focus on very specific aspects of the market demand without having to develop full stack solutions.

For customers:

• There will be a market and demand for AI as a Service whereas enterprises and verticals will want to use a telco infrastructure that will provide unique computing and connectivity benefits over on-premise or public cloud solutions.
• There are AI/ML services that (will) necessitate high performance computing environments, with guaranteed, programmable connectivity with a cost profile that is better mutualized through a multi tenant environment
• Telcom operators are the best positioned to understand and satisfy the needs of this market
• Security, privacy, residency, performance, reliability will be at least equivalent to on premise or cloud with a cost / performance benefit. 

As the market develops, new assumptions are added every day. The AI-RAN alliance has defined three general groups to create the framework to validate them: 

1. AI for RAN: AI to improve RAN performance. This group focuses on how to program and optimize the RAN with AI. The expectations is that this work will drastically reduce the cost of RAN, while allowing sophisticated spectrum, radio waves and traffic manipulations for specific use cases.
2. AI and RAN: Architecture to run AI and RAN on the same infrastructure. This group must find the multitenant architecture allowing the system to develop into a platform able to host a variety of AI workloads concurrently with the RAN. 
3. AI on RAN: AI applications to run on RAN infrastructure. This is the most ambitious and speculative group, defining the requirements on the RAN to support the AI workloads that will be defined

As for Telco Edge Computing, and RAN intelligence, while the technological challenges appear formidable, the commercial and strategic implications are likely to dictate whether AI RAN will succeed. Telecom operators are pushing for its implementation, to increase control over spending, and user experience of the RAN, while possibly developing new revenue with the diffusion of AIaaS. Traditional RAN vendors see the nascent technology as further threat to their capacity to sell programmable networks as black boxes, configured, sold and operated by them. New vendors see the opportunity to step into the RAN market and carve out market share at the expense of legacy vendors.

HPE acquires Juniper Networks

On January 8, the first rumors started to emerge that HPE was entering final discussions to acquire Juniper Networks for $13b. By January 9th, HPE announced that they have entered into a definitive agreement for the acquisition.

Juniper Networks, known for its high-performance networking equipment, has been a significant player in the networking and telecommunications sector. It specializes in routers, switches, network management software, network security products, and software-defined networking technology. HPE, on the other hand, has a broad portfolio that includes servers, storage, networking, consulting, and support services.

 The acquisition of Juniper Networks by HPE could be a strategic move to strengthen HPE’s position in the networking and telecommunications sector, diversify its product offerings, and enhance its ability to compete with other major players in the market such as Cisco.

Most analysis I have read so far have pointed out AIOps and Mist AI as the core thesis for acquisition, enabling HPE to bridge the gap between equipment vendor and solution vendor, particularly in the Telco space.

While this is certainly an aspect of the value that Juniper Networks would provide to HPE, I believe that the latest progress from Juniper Networks in Telco beyond transport, particularly as an early leader in the emerging field of RAN Intelligence and SMO (Service Management and Orchestration) was a key catalyst in HPE's interest.

After all, Juniper Networks has been a networking specialist and leader for a long time, from SDN, SD WAN, Optical to data center, wired and wireless networks. While the company has been making great progress there, gradually virtualizing  and cloudifying its routers, firewalls and gateway functions, no revolutionary technology has emerged there until the application of Machine Learning and predictive algorithms to the planning, configuration, deployment and management of transport networks.

What is new as well, is Juniper Networks' efforts to penetrate the telco functions domains, beyond transport. The key area ready for disruption has been the Radio Access Network (RAN), specifically with Open RAN becoming an increasingly relevant industry trend to pervade networks architecture, culminating with AT&T's selection of Ericsson last month to deploy Open RAN for $14B.

Open RAN offers disaggregation of the RAN, with potential multivendor implementations, benefitting from open standard interfaces. Juniper Networks, not a traditional RAN vendor, has been quick to capitalize on its AIOps expertise by jumping on the RAN Intelligence marketspace, creating one of the most advanced RAN Intelligent Controller (RIC) in the market and aggressively integrating with as many reputable RAN vendors as possible. This endeavor, opening up the multi billion $ RAN and SMO markets is pure software and heavily biased towards AI/ML for automation and prediction.

HPE has been heavily investing in the telco space of late, becoming a preferred supplier of Telco CaaS and Cloud Native Functions (CNF) physical infrastructures. What HPE has not been able to do, is creating software or becoming a credible solutions provider / integrator. The acquisition of Juniper Networks could help solve this. Just like Broadcom's acquisition of VMWare (another early RAN Intelligence leader), or Cisco's acquisition of Accedian, hardware vendors yearn to go up the value chain by acquiring software and automation vendors, giving them the capacity to provide integrated end to end solutions and to achieve synergies and economy of scale through vertical integration.

The playbook is not new, but this potential acquisition could signal a consolidation trend in the telecommunications and networking industry, suggesting a more competitive landscape with fewer but larger players. This could have far-reaching implications for customers, suppliers, and competitors alike.

Is this the Open RAN tipping point: AT&T, Ericsson, Fujitsu, Nokia, Mavenir

The latest publications around Open RAN deliver a mixed bag of progress and skepticism. How to interpret these conflicting information?

A short retrospective of the most recent news:

• Vodafone group announces that it is releasing an RFQ to refresh its ~125,000 RAN sites up for renewal and that up to 30% of these will be Open RAN (October 9, 2023).
• Ericsson's head of networks, Fredrik Jejdling, confirmed the company's support of Open RAN announced earlier this year  (October 9, 2023).
• Open RAN: Winter is coming (October 19)
• Nokia announces Open RAN interoperability achieved with Mavenir (November 15, 2023)
• Verizon says Open RAN still fails at Massive MIMO (November 30,2023)
• AT&T selects Ericsson for Open RAN deployment, jointly with Fujitsu (December 4, 2023) 

On the surface, Open RAN seems to be benefiting from a strong momentum and delivering on its promise of disrupting traditional RAN with the introduction of new suppliers, together with the opening of traditional architecture to a more disaggregated and multi vendor model. The latest announcement from AT&T and Ericsson even would point out that the promise of reduced TCO for brownfield deployments is possible:

AT&T's yearly CAPEX guidance is supposed to reduce from a high of ~$24B to about 20B$ per year starting in 2024. If the 14B$ for 5 years spent on Ericsson RAN yield the announced 70% of traffic on Open RAN infrastructure, AT&T might have dramatically improved their RAN CAPEX with this deal.

What is driving these announcements?

For network operators, Open RAN has been about strategic supply chain diversification. The coalescence of the market into an oligopoly, and a duopoly after the exclusion of Chinese vendors to a large number of Western Networks has created an unfavorable negotiating position for the carriers. The business case of 5G relies heavily on declining costs or rather a change in the costs structure of deploying and operating networks. Open RAN is an element of it, together with edge computing and telco clouds.

For operators

The decision to move to Open RAN is mostly not any longer up for debate. While the large majority of brownfield networks will not completely transition to Open RAN they will introduce the technology, alongside the traditional architecture, to foster cloud native networks implementations. It is not a matter of if but a matter of when.

When varies for each market / operator. Operators do not roll out a new technology just because it makes sense even if the business case is favorable. A window of opportunity has to present itself to facilitate the introduction of the new technology. In the case of Open RAN, the windows can be:

• Generational changes: 4G to 5G, NSA to SA, 5G to 6G
• Network obsolescence: the RAN contracts are up for renewal, the infrastructure is aging or needs a refresh. 
• New services: private networks, network slicing...
• Internal strategy: transition to cloud native, personnel training, operating models refresh
• Vendors weakness: Nothing better than an end of quarter / end of year big infrastructure bundle discount to secure and alleviate the risks of introducing new technologies

For traditional vendors

For traditional vendors, the innovator dilemma has been at play. Nokia has endorsed Open RAN early on, with little to show for it until recently, convincingly demonstrating multi vendor integration and live trials. Ericsson, as market leader has been slower to endorse Open RAN has so far adopted it selectively, for understandable reasons.

For emerging vendors

Emerging vendors have had mixed fortunes with Open RAN. The early market leader, Altiostar was absorbed by Rakuten which gave the market pause for ~3 years, while other vendors caught up. Mavenir, Samsung, Fujitsu and others offer credible products and services, with possible multi vendors permutations. 

Disruptors, emerging and traditional vendors are all battling in RAN intelligence and orchestration market segment, which promises to deliver additional Open RAN benefits (see link).

Open RAN still has many challenges to circumvent to become a solution that can be adopted in any network, but the latest momentum seem to show progress for the implementation of the technology at scale.

More details can be found through my workshops and advisory services.

Telco edge compute, RAN and AI

In recent years, the telecommunications industry has witnessed a profound transformation, driven by the rapid penetration of cloud technologies. Cloud Native Functions have become common in the packet core, OSS BSS, transport and are making their way in the access domain, both fixed and mobile. CNFs mean virtual infrastructure management and data centers have become an important part of network capex strategies. 

While edge computing in telecoms, with the emergence of MEC (Multi Access Edge Computing), has been mostly confined to telco network functions (UPF, RAN CU/DU...) network operators should now explore the opportunities for retail and wholesale of edge computing services. My workshop examines in details the strategies, technologies and challenges associated with this opportunity.

Traditional centralized cloud infrastructure is being augmented with edge computing, effectively bringing computation and data storage closer to the point of data generation and consumption.

What are the benefits of edge computing for telecom networks?

• Low Latency: One of the key advantages of edge computing is its ability to minimize latency. This is of paramount importance in telecoms, especially in applications like autonomous vehicles, autonomous robots / manufacturing, and remote-controlled machinery.
• Bandwidth Efficiency: Edge computing reduces the need for transmitting massive volumes of data over long distances, which can strain network bandwidth. Instead, data processing and storage take place at the edge, significantly reducing the burden on core networks. This is particularly relevant for machine vision, video processing and AI use cases.
• Enhanced Security: Edge computing offers improved security by allowing sensitive data to be processed locally. This minimizes the exposure of critical information to potential threats in the cloud. Additionally, privacy, data sovereignty and residency concerns can be efficiently addressed by local storage / computing.
• Scalability: Edge computing enables telecom operators to scale resources as needed, making it easier to manage fluctuating workloads effectively.
• Simpler, cheaper devices: Edge computing allows devices to be cheaper and simpler while retaining sophisticated functionalities, as storage, processing can be offloaded to a nearby edge compute facility.

Current Trends in Edge Computing for Telecoms

The adoption of edge computing in telecoms is rapidly evolving, with several trends driving the industry forward:

• 5G and private networks Integration: The deployment of 5G networks is closely intertwined with edge computing. 5G's high data transfer rates and low latency requirements demand edge infrastructure to deliver on its promises effectively. The cloud RAN and service based architecture packet core functions drive demand in edge computing for the colocation of UPF and CU/DU functions, particularly for private networks.
• Network Slicing: Network operators are increasingly using network slicing to create virtualized network segments, allowing them to allocate resources and customize services for different applications and use cases.
• Ecosystem Partnerships: Telcos are forging partnerships with cloud providers, hardware manufacturers, and application developers to explore retail and wholesale edge compute services.

Future Prospects

The future of edge computing in telecoms offers several exciting possibilities:

• Edge-AI Synergy: As artificial intelligence becomes more pervasive, edge computing will play a pivotal role in real-time AI processing, enhancing applications such as facial recognition, autonomous drones, and predictive maintenance. Additionally, AI/ML is emerging as a key value proposition in a number of telco CNFs, particularly in the access domain, where RAN intelligence is key to optimize spectrum and energy usage, while tailoring user experience.
• Industry-Specific Edge Solutions: Different industries will customize edge computing solutions to cater to their unique requirements. This could result in the development of specialized edge solutions for healthcare, manufacturing, transportation, and more.
• Edge-as-a-Service: Telecom operators are likely to offer edge services as a part of their portfolio, allowing enterprises to deploy and manage edge resources with ease.
• Regulatory Challenges: As edge computing becomes more integral to telecoms, regulatory challenges may arise, particularly regarding data privacy, security, and jurisdictional concerns.

New revenues streams can also be captured with the deployment of edge computing.

• For consumers, it is likely that the lowest hanging fruit in the short term is in gaming. While hyperscalers and gaming companies have launched their own cloud gaming services, their success has been limited due to the poor online experience. The most successful game franchises are Massive Multiplayer Online. They pitch dozens of players against each other and require a very controlled latency between all players for a fair and enjoyable gameplay. Only operators can provide controlled latency if they deploy gaming servers at the edge. Without a full blown gaming service, providing game caching at the edge can drastically reduce the download time for games, updates and patches, which increases dramatically player's service satisfaction.
• For enterprise users, edge computing has dozens of use cases that can be implemented today that are proven to provide superior experience compared to the cloud. These services range from high performance cloud storage, to remote desktop, video surveillance and recognition.
• Beyond operators-owned services, the largest opportunity is certainly the enablement of edge as a service (EaaS), allowing cloud developers to use edge resources as specific cloud availability zones.

Edge computing is rapidly maturing in the telecom industry by enabling low-latency, high-performance, and secure services that meet the demands of new use cases. As we move forward, the integration of edge computing with 5G and the continuous development of innovative applications will shape the industry's future. Telecom operators that invest in edge computing infrastructure and capabilities will be well-positioned to capitalize on the opportunities presented by this transformative technology. 

DOCOMO's 30% TCO Open RAN savings

DOCOMO announced last week, during Mobile World Congress Las Vegas the availability of its OREX offering for network operators. OREX, which stands for Open RAN Experience, was initially introduced by the Japanese operator in 2021 as OREC (Open RAN Ecosystem).

The benefits claimed by DOCOMO are quite extraordinary, as they expect to "reduce clients’ total cost of ownership by up to 30% when the costs of initial setup and ongoing maintenance are taken into account. It can also reduce the time required for network design by up to 50%. Additionally, OREX reduces power consumption at base stations by up to 50%".

The latest announcement clarifies DOCOMO's market proposition and differentiation. Since the initial communications of OREX, DOCOMO was presenting to the market a showcase of validated Open RAN blueprint deployments that the operator had carried out in its lab. What was unclear was the role DOCOMO wanted to play. Was the operator just offering best practice and exemplar implementation or were they angling for a different  play? The latest announcement clarifies DOCOMO's ambitions.

On paper, the operator showed an impressive array of vendors, collaborating to provide multi vendor Open RAN deployments, with choices and some possible permutations between each element of the stack. 

At the server layer, OREX provided options from DELL, HP and Fujitsu, all on x86 platforms, with various acceleration ASICS/FPGA... from Intel FlexRAN, Qualcomm, AMD and nvidia. While the COTS servers are readily interchangeable, the accelerator layer binds the open RAN software vendor and is not easily swappable.

At the virtualization O-Cloud layer, DOCOMO has integrated vmware, Red Hat, and WNDRVR which represents the current best of breed in that space.

The base station software CU / DU has seen implementations from Mavenir, NTT Data, and Fujitsu. 

What is missing in this picture and a little misleading is the Open Radio Unit vendors that have participated in these setups, since this where network operators need the most permutability. As of today, most Open RAN multi vendor deployments will see a separate vendor in the O-RU and CU/DU space. This is due to the fact that no single vendor today can satisfy the variety of O-RUs necessary to meet all spectrum / form factors a brownfield operator needs. More details about this in my previous state of Open RAN post here.

In this iteration, DOCOMO has clarified the O-RU vendors it has worked with most recently (Dengyo Technology, DKK Co, Fujitsu, HFR, Mavenir, and Solid). As always the devil is in the detail and unfortunately DOCOMO falls short from providing  a more complete view of the types of O-RU (mMIMO or small cell?) and the combination of O-RU vendor - CU/DU vendor - Accelerator vendor - band, which is ultimately the true measure of how open this proposition would be.

What DOCOMO clarifies most in this latest iteration, is their contribution and the role they expect to play in the market space. 

First, DOCOMO introduces their Open RAN compliant Service Management and Orchestration (SMO). This offering is a combination of NTT DOCOMO developments and third party contributions (details can be found in my report and workshop Open RAN RICs and Apps 2023). The SMO is DOCOMO's secret sauce when it comes to the claimed savings, resulting mainly from automation of design, deployment and maintenance of the Open RAN systems, as well as RU energy optimization.

At last, DOCOMO presents their vast integration experience and is now proposing these systems integration, support and maintenance services. The operator seeks the role of specialized SI and prime contractor for these O-RAN projects.

While DOCOMO's experience is impressive and has led many generations of network innovation, the latest movement to transition from leading operator and industry pioneer to O-RAN SI and vendor is reminiscent of other Japanese companies such as Rakuten with their Symphony offering. Japanese operators and vendors see the contraction of their domestic market as a strategic threat to their core business and they try to replicate their success overseas. While quite successful in greenfield environments, the hypothesis that brownfield operators (particularly tier 1) will buy technology and services from another carrier (even if not geographically competing) still needs to be validated. 

Is Ericsson's Open RAN stance that open?

 

An extract from the Open RAN RIC and Apps report and workshop.

Ericsson is one of the most successful Telecom Equipment Manufacturers of all time, having navigated market concentration phases, the emergence of powerful rivals from China and elsewhere, and the pitfalls of the successive generations and their windows of opportunity for new competitors to emerge.

With a commanding estimated global market share of 26.9% (39% excluding China) in RAN, the company is the uncontested leader in the space. While the geopolitical situation and the ban of Chinese vendors in many western markets has been a boon for the company’s growth, Open RAN has become the largest potential threat to their RAN business.

At first skeptical (if not outright hostile) to the new architecture, the company has been keeping an eye on its development and traction over the last years and has formulated a cautious strategy to participate and influence its development.

In 2023, Ericsson seems to have accepted that Open RAN is likely to stay and represents both a threat and opportunity for its telecom business. The threat is of course on the RAN infrastructure business, and while the company has been moving to cloud ran, virtualizing and containerizing its software, the company still in majority ships vertical, fully integrated base stations.

When it comes to Open RAN, the company seems to get closer to embracing the concept, with conditions.

Ericsson has been advocating that the current low layer split 7.2.x is not suitable for massive MIMO and high capacity 5G systems and is proposing an alternative fronthaul interface to the O-RAN alliance. Cynics might say this is a delaying tactic, as other vendors have deployed massive MIMO on 7.2.x in the field, but as market leader, Ericsson has some strong datasets to bring to the conversation and contest the suitability of the current implementation. Ericsson is now publicly endorsing Open RAN architecture and, having virtualized its RAN software, will offer a complete solution, with O-RU, vDU,.vCU, SMO and Non-RT RIC . The fronthaul interface will rely on the recently proposed fronthaul and the midhaul will remain the F1 3GPP interface.

On the opportunity front, while most Ericsson systems usually ship with an Element Management System (EMS), which can be integrated into a Management and Orchestration (MANO) or Service Management and Orchestration (SMO) framework, the company has not entirely dominated this market segment and Open RAN, in the form of SMO and Non-RT RIC represent an opportunity to grow in the strategic intelligence and orchestration sector.

Ericsson is using the market leader playbook to its advantage. First rejecting Open RAN as immature, not performing and not secure, then admitting that it can provide some benefits in specific conditions, and now embracing it with very definite caveats.

The front haul interface proposal by the company seems self-serving, as no other vendor has really raised the same concerns in terms of performance and indeed commercial implementations have been observed with performance profiles comparable to traditional vendors.

The Non-RT RIC and rApp market positioning is astute and allows Ericsson simultaneously to claim support for Open RAN and to attack the SMO market space with a convincing offer. The implementation is solid and reflects Ericsson’s high industrialization and quality practice. It will doubtless offer a mature implementation of SMO / Non-RT RIC and rApps and provide a useful set of capabilities for operators who want to continue using Ericsson RAN with a higher instrumentation level. The slow progress for 3^rd party integration both from a RIC and Apps perspective is worrisome and could be either the product of the company quality and administrative processes or a strategy to keep the solution fairly closed and Ericsson-centric, barring a few token 3^rd party integrations.

O-RAN alliance rApps and xApps typology

 An extract from the Open RAN RIC and Apps report and workshop.

1.    O-RAN defined rApps and xApps

1.1.        Traffic steering rApp and xApp

Traditional RAN provides few mechanisms to load balance and force traffic on specific radio paths. Most deployments see overlaps of coverage between different cells in the same spectrum, as well as other spectrum layered in, allowing performance, coverage, density and latency scenarios to coexist. The methods by which a UE is connected to a specific cell and a specific channel are mostly static, based on location of the UE, signal strength, service profile and the parameters to handover a connection from one cell to another, or within the same cell from one bearer to another or from one sector to another. The implementation is vendor specific and statically configured.

Figure 9: Overlapping cells and traffic steering

Non-RT RIC and rApps offer the possibility to change these handover and assignments programmatically and dynamically, taking advantage of policies that can be varied (power optimization, quality optimization, performance or coverage optimization…) and that can change over time. Additionally, the use of AI/ML technology can provide predictive input capability for the selection or creation of policies allowing a preferable outcome.

The traffic steering rApp is a means to design and select traffic profile policies and to dynamically allow the operator to instantiate these policies, either per cell, per sector, per bearer or even per UE or per type of service. The SMO or the Non-RT RIC collect RAN data on traffic, bearer, cell, load, etc. from the E2 nodes and instruct the Near-RT RIC to enforce a set of policies through the established parameters.

1.2.       QoE rApp and xApp

This rApp is assuming that specific services such as AR/VR will require different QoE parameters that will need to be adapted in a semi dynamic fashion. It proposes the use of AI/ML for prediction of traffic load and QoE conditions to optimize the traffic profiles.

UE and network performance data transit from the RAN to the SMO layer over the O1 interface, QoE AI/ML models are trained, process the data and infer the state and predict its evolution over time, the rApp transmits QoE policy directives to the Near-RT RIC via the Non-RT RIC.

1.3.       QoS based resource optimization rApp and xApp

QoS based resource optimization rApp is an implementation of network slicing optimization for the RAN. Specifically, it enables the Non-RT RIC to guide the Near-RT RIC in the allocation of Physical Resource Blocks to a specific slice or sub slice, should the Slice Level Specification not be satisfied by the static slice provisioning.

1.4.       Context-based dynamic handover management for V2X rApp and xApp

Since mobile networks have been designed for mobile but relatively low velocity users, the provision of high speed, reliable mobile service along highways requires specific designs and configurations. As vehicles become increasingly connected to the mobile network and might rely on network infrastructure for a variety of uses, Vehicle to infrastructure (V2X) use cases are starting to appear primarily as research and science projects. In this case, the App is supposed to use AI/ML models to predict whether a UE is part of a V2X category and its trajectory in order to facilitate cell handover along its path.

1.5.       RAN Slice Assurance rApp and xApp

3GPP has defined the concept of creating a connectivity product with specific attributes (throughput, reliability, latency, energy consumption) applicable to specific devices, geographies, enterprises… as slices. In an O-RAN context, the Non-RT RIC and Near-RT RIC can provide optimization strategies for network slicing. In both cases, the elements can monitor the performance of the slice and perform large or small interval adjustments to stay close the slice’s Service Level Agreement (SLA) targets.

Generally speaking, these apps facilitate the allocation of resource according to slice requirements and their dynamic optimization over time.

1.6.       Network Slice Instance Resource Optimization rApp

The NSSI rApp aims to use AI/ML to model traffic patterns of a cell through historical data analysis. The model is then used to predict network load and conditions for specific slices and to dynamically and proactively adjust resource allocation per slice.

1.7.       Massive MIMO Optimization rApps and xApps

Massive MIMO (mMIMO) is a key technology to increase performance in 5G. It uses complex algorithms to create signal beams which minimize signal interference and provide narrow transmission channels. This technology, called beamforming can be configured to provide variations in the vertical and horizontal axis, azimuth and elevation resulting in beams of different shapes and performance profiles. Beamforming and massive MIMO are a characteristic of the Radio Unit, where the DU provides the necessary data for the configuration and direction of the beams.

In many cases, when separate cells overlap a given geography, for coverage or density with either multiple macro cells or macro and small cells mix, the mMIMO beams are usually configured statically, manually based on the cell situation. As traffic patterns, urban environment and interference / reflection, change, it is not rare that the configured beams lose efficiency over time.

In this instance, the rApp collects statistical and measurement data of the RAN to inform a predictive model of traffic patterns. This model, in turn informs a grid of beams that can be applied to a given situation. This grid of beams is transmitted to the DU through the Near-RT RIC and a corresponding xApp, responsible for assigning the specific PRB and beam parameters to the RU. A variant of this implementation does not require grid of beams or AI/ML, bit a list of statically configured beams that can be selected based on specific threshold or RAN measurements.

Additional apps leveraging unique MIMO features such as downlink transmit power, Multiple User MIMO and Single User MIMO allow, by reading UE performance to adjust the transmit power or the beam parameters to improve the user experience or the overall spectral efficiency.

1.8.       Network energy saving rApps and xApps

These apps are a collection of methods to optimize power consumption in the open RAN domain.

    Carrier and cell switch off/on rApp:

A simple mechanism to identify within a cell the capacity needed and whether it is possible to reduce the power consumption by switching off frequency layers (carriers) or the entire cell, should sufficient coverage / capacity exist with other adjoining overlapping cells. AI/ML model on the Non- RT RIC might assist in the selection and decision, as well as provide a predictive model. The prediction in this case is key, as one cannot simply switch off a carrier or a cell without gracefully hand over its traffic to an adjoining carrier or cell before to reduce quality of experience negative impact.

    RF Channel reconfiguration rApp:

mMIMO is achieved by the combination of radiating elements to form the beams. A mMIMO antenna array 64 64 transceivers and receivers (64T64R) can be configured to reduce its configuration to 32, 16 or 8 T/R for instance, resulting in a linear power reduction. An AI/ML model can be used to determine the optimal antenna configuration based on immediate and predictive traffic patterns.

What RICs and Apps developers need to succeed

 

We spoke a bit about my perspective on the Non and Near-Real Time RIC likely trajectories and what value rApps and xApps have for operators and the industry. As I conclude the production of my report and workshop on Open RAN RICs and Apps, after many discussions with the leaders in that field, I have come with a few conclusions.

There are many parameters for a company to be successful in telecoms, and in the RIC and Apps area, there are at least three key skill sets that are necessary to make it.

Artificial Intelligence is a popular term many in the industry use as a shorthand for their excel macros linear projection and forecast mastery. Data literacy is crucial here, as big data / machine learning / deep learning / artificial intelligence terms are bandied around for marketing purposes. I am not an expert in the matter, but I have a strong feeling that the use cases for algorithmic fall into a few categories. I will try to expose them in my terms, apologies in advance to the specialists as the explanation will be basic and profane.

• Anomaly / pattern detection provide a useful alarming system if the system's behavior has a sufficiently long time series and the variance is somewhat reduced or predictable. This does not require more than data knowledge, it is a math problem.
• Optimization / correction should allow, provided the anomaly / pattern detection is accurate to pinpoint specific actions that would allow a specific outcome. This where RAN knowledge is necessary. It is crucial to be able to identify from the inputs whether the output is accurate and to which element it corresponds. Again, a long time series of corrections / optimizations and their impact / deviation is necessary for the model to be efficient.
• Prediction / automation is the trickiest part. Ideally, given enough knowledge of the system's patterns, variances and deviations, one can predict with some accuracy its behavior over time in steady state and when anomalies occur and take a preemptive /corrective action. Drawn to its logical conclusion, full automation and autonomy would be possible. This is where most companies overpromise in my mind. The system here is a network. Not only is it vast and composed of millions of elements (after all that is just a computing issue), it is also always changing. Which means that there no steady state and that the time series is a collection of dynamically changing patterns. Achieving full automation under these conditions seems impossible. Therefore, it is necessary to reframe expectations, especially in a multi vendor environment and to settle for pockets of automation, with AI/ML augmented limited automation.

Platform and developer ecosystem management is also extremely important in the RIC and Apps segment if one wants to deploy multi vendor solutions. The dream of being able to instantiate Apps from different vendors and orchestrate them harmoniously is impossible without a rich platform, with many platform services attributes (lifecycle management, APIs, SDK, Data / messaging bus, orchestration...). This does not necessarily require much RAN knowledge and this why we are seeing many new entrants in this field.

The last, but foremost, in my mind, is the RAN knowledge. The companies developing RAN Intelligent Controllers and apps need to have deep understanding of the RAN, its workings and evolution. Deep knowledge may probably not necessary for the most pedestrian use cases around observability and representation of the health and performance of the system or the network, but any App that would expect a retro feedback and to send instruction to the lower elements of the architecture needs understanding of not only of the interfaces, protocols and elements but also their function, interworking and capabilities. If the concept of RICs and Apps is to be successful, several Apps will need to be able to run simultaneously and ideally from different vendors. Understanding the real-life consequences of an energy efficiency App and its impact on quality of service, quality of experience, signaling is key in absolute. It becomes even more crucial to understand how Apps can coexist and simultaneously, or by priority implement power efficiency, spectrum optimization and handover optimization for instance. The intricacies of beamforming, beam weight, beam steering in mMIMO systems, together with carrier aggregation and dynamic spectrum sharing mandate a near / real time control capability. The balance is delicate and it is unlikely that scheduler priorities could conceivably be affected by an rApp that has little understanding of these problematics. You don't drive a formula one car while messing about the gear settings.

If you want to know how I rank the market leaders in each of these categories, including Accelleran, Aira technologies, Airhop, Airspan, Capgemini, Cohere technologies, Ericsson, IS - Wireless, Fujitsu, Juniper, Mavenir, Nokia, Northeastern University, NTT DOCOMO, Parallel Wireless, Radisys, Rakuten, Rimedo Labs, Samsung, VIAVI, VMware and others, you'll have to read my report or register for my workshop.

The RICs - brothers from a different mother?

As you might know, if you have been an erstwhile reader of my previous blogs and posts, I have been a spectator, advocate and sometime actor in telecoms open and disaggregated networks for quite some time.

From my studies on SDN /NFV, to my time with Telefonica, the TIP forum, the ONF or more recently my forays into Open RAN at NEC, I have been trying to understand whether telecom networks could, by adopting cloud technologies and concepts, evolve towards more open and developer friendly entities.

Unfortunately, as you know, we love our acronyms in telecom. It is a way for us to reconcile obtuse technology names into even more impenetrable concepts that only "specialists" understand. So, when you come across acronyms that contain other acronyms, you know that you are dealing with extra special technology.

Today, let's spend some time on the RICs. RIC stands for RAN (Radio Access Network) Intelligent Controller. The RICs are elements of open RAN, as specified by the O-RAN alliance. The O-RAN alliance is a community of telecoms actors looking at opening and disaggregating the RAN architecture, one of the last bastions of monolithic, proprietary naughtiness in telecoms networks. If you want to understand more about why O-RAN was created, please read here.  There are two types of RIC, a non real time and a near real time.

O-RAN logical architecture

The RICs are frameworks with standardized interfaces to each others, and the other elements of the SMO,  the cloud and the RAN. They are supposed to be platforms onto which independent developers would be able to develop RAN specific features, packaged as Apps that would theoretically be deployable on any standard compliant RIC. The Apps are called rAPPs for non RT and xApps for near RT RIC.

Although the RICs share the same last name, they are actually quite different and are more distant cousins than siblings, which makes the rApps and xApps unlikely to be compatible in a multivendor environment.

The non real time RIC and the rApps

The non RT RIC is actually not part of the RAN itself, it is part of the Service Management and Orchestration (SMO) framework. The non-real time aspect means that it is not intended to act on the O-RAN subsystems (Near RT RIC, Centralized Units (CU), Distributed Units (DU), Radio Units (RU)) with a frequency much lower than once per second. Indeed, the non RT RIC can interface with these systems even in the range of minutes or days. 

Its purpose is a combination of an evolution of SON (Self Organizing Networks) and the creation of a vendor-agnostic RAN OSS. SON was a great idea initially. It was intended to provide operators with a means to automate and optimize RAN configuration. Its challenges raised from the difficulty to integrate and harmonize rules across vendors. One of O-RAN's tenets is to promote multi vendor ecosystems, thereby providing a framework for RAN automation. As part of the SMO, the non RT RIC is also an evolution of the proprietary OSS and Element Management Systems, which for the first time provide open interfaces for RAN configuration.

Because of its dual legacy from OSS and SON, and its less stringent integration needs, the non RT RIC has been the first entity to see many new entrant vendors, either from the OSS or the SON or the transport or the cloud infrastructure management communities. 

Because of their non real time nature (and because many non RT RIC and rApps vendors are different from the RAN vendors, the rApps have somewhat limited capabilities in multivendor environments. Most vendors provide visualization / topology / dashboards capabilities and enhancements revolving around neighbouring and handover management.

The near real time RIC and the xApps

The near real time RIC is part of the RAN and comprises a set of functionalities that are, in a traditional RAN implementation part of the feature set of the gNodeB base station. O-RAN has separated these capabilities into a framework, exposing open interfaces to the RAN components, theoretically allowing vendor agnostic RAN automation and optimization. The near real-time would infer sub-second frequency of interaction between the elements. And..here's the rub.

Millisecond adjustments are necessary in the RAN to account for modulation, atmospheric or interference conditions. This frequency requires a high level of integration between the CU, DU and RU to not lose performance. As often in telecoms,  the issue is not with the technology, but rather the business model. O-RAN's objective is to commoditize and disrupt the RAN, which is an interesting proposition for its consumers (the operators) and for new entrants, less so for legacy vendors. The disaggregation of the RAN with the creation of near RT RIC and xApps goes one step further, commoditizing the RU, CU and DU and extracting the algorithmic value and differentiation in the xApps. The problem with disruption is that it only works in mature, entrenched market segments. While traditional RAN might be mature enough for disruption, it is uncertain that open RAN itself has the degree of maturity whereas new entrants in RU, CU and DU would be commoditized by xApps.

For this reason, it is likely that if near RT RIC and xApps are to be successful, only the dominant RU, CU, DU vendors will be able to develop it and deploy it, which will lead to some serious dependencies against vendor independence.

 I am currently working on my next report on Open RAN and RIC and will provide more updates as I progress there.