The Orchestrator Conundrum strikes again: Open RAN vs AI-RAN

10 years ago (?!) I wrote about the overlaps and potential conflicts of the different orchestration efforts between SDN and NFV. Essentially, observing that, ideally, it is desirable to orchestrate network resources with awareness of services and that service and resource orchestration should have hierarchical and prioritized interactions, so that a service deployment and lifecycle is managed within resource capacity and when that capacity fluctuates, priorities can be enforced.

Service orchestrators have not really been able to be successfully deployed at scale for a variety a reasons, but primarily due to the fact that this control point was identified early on as a strategic effort for network operators and traditional network vendors. A few network operators attempted to create an open source orchestration model (Open Source MANO), while traditional telco equipment vendors developed their own versions and refused to integrate their network functions with the competition. In the end, most of the actual implementation focused on Virtual Infrastructure Management (VIM) and vertical VNF management, while orchestration remained fairly proprietary per vendor. Ultimately, Cloud Native Network Functions appeared and were deployed in Kubernetes inheriting its native resource management and orchestration capabilities.

In the last couple of years, Open RAN has attempted to collapse RAN Element Management Systems (EMS), Self Organizing Networks (SON) and Operation Support Systems (OSS) with the concept of Service Management and Orchestration (SMO). Its aim is to ostensibly provide a control platform for RAN infrastructure and services in a multivendor environment. The non real time RAN Intelligent Controller (RIC) is one of its main artefacts, allowing the deployment of rApps designed to visualize, troubleshoot, provision, manage, optimize and predict RAN resources, capacity and capabilities.

This time around, the concept of SMO has gained substantial ground, mainly due to the fact that the leading traditional telco equipment manufacturers were not OSS / SON leaders and that Orchestration was an easy target for non RAN vendors wanting to find a greenfield opportunity. 

As we have seen, whether for MANO or SMO, the barriers to adoption weren't really technical but rather economic-commercial as leading vendors were trying to protect their business while growing into adjacent areas.

Recently, AI-RAN as emerged as an interesting initiative, positing that RAN compute would evolve from specialized, proprietary and closed to generic, open and disaggregated. Specifically, RAN compute could see an evolution, from specialized silicon to GPU. GPUs are able to handle the complex calculations necessary to manage a RAN workload, with spare capacity. Their cost, however, greatly outweighs their utility if used exclusively for RAN. Since GPUs are used in all sorts of high compute environments to facilitate Machine Learning, Artificial Intelligence, Large and Small Language Models, Models Training and inference, the idea emerged that if RAN deploys open generic compute, it could be used both for RAN workloads (AI for RAN), as well as workloads to optimize the RAN (AI on RAN and ultimately AI/ML workloads completely unrelated to RAN (AI and RAN).

While this could theoretically solve the business case of deploying costly GPUs in hundreds of thousands of cell site, provided that the compute idle capacity could be resold as GPUaaS or AIaaS, this poses new challenges from a service / infrastructure orchestration standpoint. AI RAN alliance is faced with understanding orchestration challenges between resources and AI workloads

In an open RAN environment. Near real time and non real time RICs deploy x and r Apps. The orchestration of the apps, services and resources is managed by the SMO. While not all App could be categorized as "AI", it is likely that SMO will take responsibility for AI for and on RAN orchestration. If AI and RAN requires its own orchestration beyond K8, it is unlikely that it will be in isolation from the SMO.

From my perspective, I believe that the multiple orchestration, policy management and enforcement points will not allow a multi vendor environment for the control plane. Architecture and interfaces are still in flux, specialty vendors will have trouble imposing their perspective without control of the end to end architecture. As a result, it is likely that the same vendor will provide SMO, non real time RIC and AI RAN orchestration functions (you know my feelings about near real time RIC). 

If you make the Venn diagram of vendors providing / investing in all three, you will have a good idea of the direction the implementation will take.

MWC 25 thoughts

 Back from Mobile World Congress 2025!

I am so thankful I get to meet my friends, clients, ex colleagues year after year and to witness how our industry is moving first hand.

2025 was probably my 23rd congress or so and I always find it invaluable for many reasons. 

Innovation from the East

What stood up for me this year was how much innovation is coming from Asian companies, while most Western companies seem to be focusing on cost control. 

The feeling was pervasive throughout the show and the GLOMO awards winners showed Huawei, ZTE, China Mobile, SK, Singtel… investing in discovering and solving problems that many in Western markets dismiss as futuristic or outside their comfort zone. In mature markets, where price attrition is the rule, differentiation is key.

On a related topic, being Canadian, I can’t help thinking that many companies and regulators who looked at the banning of some Chinese vendors from their markets due to security preoccupations are now finding themselves in the situation to evaluate whether American suppliers do not also represent a risk in the future. 

Without delving into politics, I saw and heard many initiatives to enhance security, privacy, sovereignty, either in the cloud or the supply chain categories. 

Open telco APIs

Open APIs and the progress of telco networks APIs is encouraging, but while it is a good idea, it feels late and lacking in comparison with webscalers tooling and offering to discover, consume, and manage network functions on demand. Much work remains to be done in my opinion to enhance the aaS portion of the offering, particularly if slicing APIs are to be offered. 

Open RAN & RIC

Open RAN threat has successfully accelerated cloud and virtualized RAN adoption. Samsung started the trend and Ericsson’s deployment at AT&T has crystalized the mMIMo +CU+DU+non RT RIC from a main vendor and small cells + rApps from others as a viable option. Vodafone’s RAN refresh should see maybe more players into the mix as Mavenir and Nokia are struggling to gain meaningful market share. 

The Juniper / HPE acquisition drama, together with the Broadcom / VMware commercial strategy seem to have killed the idea of an independent Non RT RIC vendor. Near RT RIC, remains in my mind a flawed proposition as host of 3rd party xApps, and as an expensive gadget for anything else than narrow use cases. 

AI

AI of course, was the belle of the ball at MWC. Everyone had a twist, a demo, a model, an agent but few were able to demonstrate utility beyond automated time series regression as predictions or LLM based natural language processing as nauseam…

Some were convincingly starting to show Small Models that were tailored to their technology, topology and network with promising results. It is still early but it feels that this is where the opportunity lies. The creation and curation of a dataset that can be used to plan, manage, maintain, predict the state of one’s network, with bespoke algorithms seems more desirable than the wholesale vague large and poorly trained models. 

Telco Cloud and Edge computing is having a bit of a moment with AI and GPU aaS strategies being enacted.

All in all, many are trying to develop an AI strategy, and while we are still far from the AI-Native Telco Network, there is some progress and some interesting ventures amidst the noise.

June 2024 Open RAN requirements from Vodafone, Telefonica, Deutsche Telekom, Tim and Orange

 As is now customary, the "big 5" European operators behind open RAN release their updated requirements to the market, indicating to vendors where they should direct their roadmaps to have the most chances to be selected in these networks.

As per previous iterations, I find it useful to compare and contrast the unanimous and highest priority requirements as indications of market maturity and directions. Here is my read on this year's release:

Scenarios:

As per last year, the big 5 unanimously require support for O-RU and vDU/CU with open front haul interface on site for macro deployments. This indicates that although the desire is to move to a disaggregated implementation, with vDU / CU potentially moving to the edge or the cloud, all the operators are not fully ready for these scenario and prioritize first a deployment like for like of a traditional gnodeB with a disaggregated virtualized version, but all at the cell site. 

Moving to the high priority scenarios requested by a majority of operators, vDU/vCU in a remote site with O-RU on site makes its appearance, together for RAN sharing. Both MORAN and MOCN scenarios are desirable, the former with shared O-RU and dedicated vDU/vCU and the latter with shared O-RU, vDU and optionally vCU. In all cases, RAN sharing management interface is to be implemented to allow host and guest operators to manage their RAN resource independently.

Additional high priority requirements are the support for indoor and outdoor small cells. Indoor sharing O-RU and vDU/vCU in multi operator environments, outdoors with single operator with O-RU and vDU either co-located on site or fully integrated with Higher Layer Split. The last high priority requirement is for 2G /3G support, without indication of architecture.

Security:

The security requirements are sensibly the same as last year, freely adopting 3GPP requirements for Open RAN. The polemic around Open RAN's level of security compared to other cloud virtualized applications or traditional RAN architecture has been put to bed. Most realize that open interfaces inherently open more attack surfaces, but this is not specific to Open RAN, every cloud based architecture has the same drawback. Security by design goes a long way towards alleviating these concerns and proper no trust architecture can in many cases provide a higher security posture than legacy implementations. In this case, extensive use of IPSec, TLS 1.3, certificates at the interfaces and port levels for open front haul and management plane provide the necessary level of security, together with the mTLS interface between the RICs. The O-Cloud layer must support Linux security features, secure storage, encrypted secrets with external storage and management system.

CaaS:

As per last year, the cloud native infrastructure requirements have been refined, including Hardware Accelerator (GPU, eASIC) K8 support, Block and Object Storage for dedicated and hyper converged deployments, etc... Kubernetes infrastructure discovery, deployment, lifecycle management and cluster configuration has been further detailed. Power saving specific requirements have been added, at the Fan, CPU level with SMO driven policy and configuration and idle mode power down capabilities.

CU / DU:

CU DU interface requirements remain the same, basically the support for all open RAN interfaces (F1, HLS, X2, Xn, E1, E2, O1...). The support for both look aside and in-line accelerator architecture is also the highest priority, indicating that operators havent really reached a conclusion for a preferable architecture and are mandating both for flexibility's sake (In other words, inline acceleration hasn't convinced them that it can efficiently (cost and power) replace look aside). Fronthaul ports must support up to 200Gb by 12 x 10/25Gb combinations and mid haul up to 2 x 100Gb. Energy efficiency and consumption is to be reported for all hardware (servers, CPUs, fans, NIC cards...). Power consumption targets for D-RAN of 400Watts at 100% load for 4T4R and 500 watts for 64T64R are indicated. These targets seem optimistic and poorly indicative of current vendors capabilities in that space.

O-RU:

The radio situation is still messy and my statements from last year still mostly stand: "While all operators claim highest urgent priority for a variety of Radio Units with different form factors (2T2R, 2T4R, 4T4R, 8T8R, 32T32R, 64T64R) in a variety of bands (B1, B3, B7, B8, B20, B28B, B32B/B75B, B40, B78...) and with multi band requirements (B28B+B20+B8, B3+B1, B3+B1+B7), there is no unanimity on ANY of these. This leads vendors trying to find which configurations could satisfy enough volume to make the investments profitable in a quandary. There are hidden dependencies that are not spelled out in the requirements and this is where we see the limits of the TIP exercise. Operators cannot really at this stage select 2 or 3 new RU vendors for an open RAN deployment, which means that, in principle, they need vendors to support most, if not all of the bands and configurations they need to deploy in their respective network. Since each network is different, it is extremely difficult for a vendor to define the minimum product line up that is necessary to satisfy most of the demand. As a result, the projections for volume are low, which makes the vendors only focus on the most popular configurations. While everyone needs 4T4R or 32T32R in n78 band, having 5 vendor providing options for these configurations, with none delivering B40 or B32/B75 makes it impossible for operators to select a single vendor and for vendors to aggregate sufficient volume to create a profitable business case for open RAN." This year, there is one configuration of high priority that has unanimous support: 4T4R B3+B1. The other highest priority configurations requested by a majority of operators are 2T4R B28B+B20+B8, 4T4R B7, B3+B1, B32B+B75B, and 32T32R B78 with various power targets from 200 to 240W.

Open Front Haul:

The Front Haul interface requirements only acknowledge the introduction of Up Link enhancements for massive MIMO scenarios as they will be introduced to the 7.2.x specification, with a lower priority. This indicates that while Ericsson's proposed interface and architecture impact is being vetted, it is likely to become an optional implementation, left to the vendor's s choice until / unless credible cost / performance gains can be demonstrated.

Transport:

Optical budgets and scenarios are now introduced.

RAN features:

Final MoU positions are now proposed. Unanimous items introduced in this version revolve mostly around power consumption and efficiency counters, KPIs and mechanisms. other new requirements introduced follow 3GPP rel 16 and 17 on carrier aggregation, slicing and MIMO enhancements.

Hardware acceleration:

a new section introduced to clarify the requirements associated with L1 and L2 use of look aside and inline. The most salient requirement is for multi RAT 4G/5G simultaneous support.

Near RT RIC:

The Near Real Time RIC requirements continue to evolve and be refined. My perspective hasn't changed on the topic. and a detailed analysis can be found here. In short letting third party prescribe policies that will manipulate the DU's scheduler is anathema for most vendors in the space and, beyond the technical difficulties would go against their commercial interests. operators will have to push very hard with much commercial incentive to see xapps from 3rd party vendors being commercially deployed.

E2E use cases:

End-to-end use cases are being introduced to clarify the operators' priorities for deployments. There are many  but offer a good understanding of their priorities. Traffic steering for dynamic traffic load balancing, QoE and QoS based optimization, to optimize resource allocation based on a desired quality outcome... RAN sharing, Slice assurance, V2x, UAV, energy efficiency... this section is a laundry list of desiderata , all mostly high priority, showing here maybe that operators are getting a little unfocused on what real use cases they should focus on as an industry. As a result, it is likely that too many priorities result in no priority at all.

SMO

With over  260 requirements, SMO and non RT RIC is probably a section that is the most mature and shows a true commercial priority for the big 5 operators.

All in all, the document provides a good idea of the level of maturity of Open RAN for the the operators that have been supporting it the longest. The type of requirements, their prioritization provides a useful framework for vendors who know how to read them.

More in depth analysis of Open RAN and the main vendors in this space is available here.

How to manage mobile video with Open RAN

Ever since the launch of 4G, video has been a thorny issue to manage for network operators. Most of them had rolled out unlimited or generous data plans, without understanding how video would affect their networks and economics. Most videos streamed to your phones use a technology called Adaptive Bit Rate (ABR), which is supposed to adapt the video’s definition (think SD, HD, 4K…) to the network conditions and your phone’s capabilities. While this implementation was supposed to provide more control in the way videos were streamed on the networks, in many cases it had a reverse effect.

 

The multiplication of streaming video services has led to ferocious competition on the commercial and technological front. While streaming services visibly compete on their pricing and content attractiveness, a more insidious technological battle has also taken place. The best way to describe it is to compare video to a gas. Video will take up as much capacity in the network as is available.

When you start a streaming app on your phone, it will assess the available bandwidth and try to deliver the highest definition video available. Smartphone vendors and streaming providers try to provide the best experience to their users, which in most cases means getting the highest bitrate available. When several users in the same cell try to stream video, they are all competing for the available bandwidth, which leads in many cases to a suboptimal experience, as some users monopolize most of the capacity while others are left with crumbs.

 

In recent years, technologies have emerged to mitigate this issue. Network slicing, for instance, when fully implemented could see dedicated slices for video streaming, which would theoretically guarantee that video streaming does not adversely impact other traffic (video conferencing, web browsing, etc…). However, it will not resolve the competition between streaming services in the same cell.

 

Open RAN offers another tool for efficiently resolving these issues. The RIC (RAN Intelligent Controller) provides for the first time the capability to visualize in near real time a cell’s congestion and to apply optimization techniques with a great level of granularity. Until Open RAN, the means of visualizing network congestion were limited in a multi-vendor environment and the means to alleviate them were broad and coarse. The RIC allows to create policies at the cell level, on a per connection basis. Algorithms allow traffic type inference and policies can be enacted to adapt the allocated bandwidth based on a variety of parameters such as signal strength, traffic type, congestion level, power consumption targets…

 

For instance, an operator or a private network for stadiums or entertainment venues could easily program their network to not allow upstream videos during a show, to protect broadcasting or intellectual property rights. This can be easily achieved by limiting the video uplink traffic while preserving voice, security and emergency traffic.

 

Another example would see a network actively dedicating deterministic capacity per connection during rush hour or based on threshold in a downtown core to guarantee that all users have access to video services with equally shared bandwidth and quality.

 

A last example could see first responder and emergency services get guaranteed high-quality access to video calls and broadcasts.

 

When properly integrated into a policy and service management framework for traffic slicing, Open RAN can be an efficient tool for adding fine grained traffic optimization rules, allowing a fairer apportioning of resource for all users, while preserving overall quality of experience.

 

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.

RAN Intelligence leaders 2023

RAN intelligence is an emerging market segment composed of RAN Intelligent Controllers (RICs) and their associated Apps. I have been researching this field for the last two years and after an exhaustive analysis of the vendors and operators offerings and strategies, I am glad to publish here an extract of my findings. A complete review of the findings and rankings can be found through the associated report or workshop (commercial products).

The companies who participated in this study are AccelleRAN, AIRA, Airhop, Airspan, Cap Gemini, Cohere Technologies, Ericsson, Fujitsu, I-S Wireless, Juniper, Mavenir, Nokia, Northeastern. NTT Docomo, Parallel Wireless, Radisys, Rakuten Symphony, Rimedo Labs, Samsung, Viavi, VMWare.

They were separated in two overall categories:

• Generalists: companies offering both RIC(s) and Apps implementations
• Specialists: companies offering only Apps

The Generalist ranking is:

#1 Mavenir
#2 ex aequo Juniper and vmware
#4 Cap Gemini

The Specialists ranking is:

#1 Airhop
#2 Rimedo Labs
#3 Cohere Technologies

The study features a review of a variety of established and emerging vendors in the RAN space. RAN intelligence is composed of:

• Non Real Time RIC - a platform for RIC intelligence necessitating more than 1 second to process and create feedback loops to the underlying infrastructure. This platform is an evolution of SON (Self Organizing Networks) systems, RAN EMS (Element Management Systems) and OSS (Operations Support Systems). The Non RT RIC is part of the larger SMO (Service Management and Orchestration) framework.
• rApps -  Applications built on top of the Non RT RIC platform.
• Near Real Time RIC - a platform for RIC intelligence necessitating less than 1 second to process and create feedback loops to the underlying infrastructure. This platform is a collection of capabilities today embedded within the RUs (Radio Units), DUs (Distributed Units) and CUs (Centralized Units).
• xApps - Applications built on top of the Near RT RIC platform.

The vendors and operators were ranked on their strategy, vision and implementation across six dimensions, based on primary research from interviews, publicly available information, Plugfests participation and deployments observation:

• Platform - the ability to create a platform and a collection of processes facilitating the developers' capability to create Apps that can be ported from one vendor to the other with minimum adaptation. Considerations were given to Apps lifecycle management, maturity of APIs / SDK, capability to create enabling apps / processes for hosted Apps.
• Integrations / partnerships - one of the key tenets of Open RAN is the multi vendor or vendor agnostic implementation. From this perspective, companies that gave demonstrated their integration capabilities in multi vendor environments of the hosting of third party applications were ranked higher.
• Non Real Time RIC - ranking the vision, implementation and maturity of the Non RT RIC capabilities.
• Near Real Time RIC - ranking the vision, implementation and maturity of the Near RT RIC capabilities.
• rApps - ranking the vision, implementation and maturity of the rApps offering
• xApps - ranking the vision, implementation and maturity of the xApps offering

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.

Open RAN technical priorities release 3

The Open RAN technical priorities release 3, was published in March 2023 by Deutsche Telekom, Orange, Telefonica, TIM and Vodafone as part of the Open RAN MoU group at the Telecom Infra Project.

A review of the mandatory, highest priority unanimous requirements shed lights on what the big 5 operators consider essential for vendors to focus on this year, and more importantly highlights how much efforts are still necessary by the industry to meet markets expectations.

Scenarios

In this section, the big 5 regard virtualized DU and CU with open Front Haul on site as a must, for Macro and indoor / outdoor small cell deployments. This indicates that 7.2.x remains the interface of choice, despite recent attempts by other vendors to change its implementation. It also shows that as a first step, at least, they are looking at deploying Open RAN in the conventional fashion, replacing traditional e/g Node  B with like-for-like O-RU, DU. CU on site. The benefits of resource pooling due to disaggregation and virtualization, enabling either CU or CU and DU to be centralized is the highest priority by the majority of operators, but not all yet. Network sharing of O-RU and vDU/CU is also a highest priority for the majority of operators.

Security

The security requirements have increased dramatically in this latest version, with the vast majority of the requirements (166 out of 180) considered highest priority by al the MoU operators. This evolution marks the effort that have been dedicated to the topic over the last 24 months. Open RAN has been openly criticized and accused of lax security and the O-RAN alliance has dedicated a working group to assess and shore up criticism in that space. My assessment is that most of the security concerns of Open RAN are either linked to virtualization / O-Cloud implementation or just mechanically a result of having more open interfaces, providing more attack surfaces. Open RAN is not inherently more or less secure than 3GPP implementation and the level of security by design necessary to satisfy the criticisms we have seen in the media is not today implemented by traditional RAN vendors either. Having said that, the requirements now spell out exhaustively the level of admission control, authentication, encryption, certification necessary for each interface, for each infrastructure block and for their implementation in a cloud native containerized environment.

O-Cloud Infrastructure (CaaS)

The O-Cloud requirements are focused on ensuring a cloud-native architecture, while allowing acceleration hardware whenever necessary. As a result, the accent is put on bare metal or IaaS implementations of Kubernetes, with FPGA, eAsic, GPU acceleration support and management. The second theme that is prevalent in O-Cloud unanimous high priority requirements is the lifecycle management features which indicate a transition from the lab to more mature commercial implementations going forward.

CU and DU requirements

First and foremost the big 5 unanimously are looking at virtualized and containerized implementations of O-CU/O-DU with both look-aside and inline acceleration (this is contradictory, but I assume either one is acceptable). The next requirements are the usual availability, scalability, and performance related requirements we find in generic legacy RAN systems. All O-RAN interfaces support are mandatory.

Interestingly, power consumption targets are now spelled out per scenario.

RU requirements

The Radio Units requirements area good illustration of the difficulty to create a commercially viable Open RAN solution at scale. While all operators claim highest urgent priority for a variety of Radio Units with different form factors (2T2R, 2T4R, 4T4R, 8T8R, 32T32R, 64T64R) in a variety of bands (B1, B3, B7, B8, B20, B28B, B32B/B75B, B40, B78...) and with multi band requirements (B28B+B20+B8, B3+B1, B3+B1+B7), there is no unanimity on ANY of these. This leads vendors trying to find which configurations could satisfy enough volume to make the investments profitable in a quandary. There are hidden dependencies that are not spelled out in the requirements and this is where we see the limits of the TIP exercise. Operators cannot really at this stage select 2 or 3 new RU vendors for an open RAN deployment, which means that, in principle, they need vendors to support most, if not all of the bands and configurations they need to deploy in their respective network. Since each network is different, it is extremely difficult for a vendor to define the minimum product line up that is necessary to satisfy most of the demand. As a result, the projections for volume are low, which makes the vendors only focus on the most popular configurations. While everyone needs 4T4R or 32T32R in n78 band, having 5 vendor providing options for these configurations, with none delivering B40 or B32/B75 makes it impossible for operators to select a single vendor and for vendors to aggregate sufficient volume to create a profitable business case for open RAN.

The other RU related requirements helpfully spell out the power consumption, volume and weight targets for each type of configuration.

Open Front Haul requirements

There are no changes in the release 3, which shows the maturity of the interface implementation.

RAN features

The RAN features of the highest priority unanimously required by the big 5 operators remain mostly unchanged and emphasize the need for multi connectivity. Dual connectivity between 4G and 5G is essential for any western european operator to contemplate mass deployment of open RAN or replacement of their Chinese RAN vendor. The complexity does not stop to the support of the connectivity, but also necessitate advanced features such as Dynamic Spectrum Sharing (DSS) and Carrier Aggregation (CA) which is a complexity multiplier when associated with the RU band support requirements. These advanced features are probably some of the highest barriers to entry for new vendors in the space, as they have been developed for years by traditional vendors and require a high level of technological maturity and industrialization.

Near-RT RIC

The requirements for the Near-Real Time RAN Intelligent Controller are extremely ambitious. While they technically would enable better control of a multi-vendor RAN operation, they are unlikely to succeed in the short to medium term, in my opinion, as per previous analysis.

SMO and Non-RT RIC

The requirements for Service Management and Orchestration and Non-Real Time RIC are fairly mature and provide a useful framework for RAN domain automation and lifecycle management. The accent in this release is put on AI/ML support and management, which shows that the operators have been seduced by the promises of the technology, allowing a zero touch, automated, network, relying on historical analysis and predictive algorithms. The requirements are fairly high level, suggesting that the operators themselves might not have yet very clear targets in terms of algorithmics policy, performance and management.

In conclusion, this document provides useful data on Open RAN maturity and priorities. While the release 3 shows great progress in many aspects, it still fails to provide sufficient unanimous guidance from a commercial standpoint on the minimum set of end to end capabilities a vendor could reasonably develop to be selected for deployment at scale in these western european networks.

Near real time RIC and xApps market considerations

An extract from my upcoming report "Open RAN RIC and Apps 2023"   

As mentioned, near real time RIC and xApps capabilities are today embedded in gNodeB and RU/CU/DU code. The constraints of developing applications that have an actual effect in milliseconds on the RAN offer two main challenges, one technical, the second commercial.

The technical challenge associated with the development and roll out of xApps and the near real time RIC itself is related to the RAN Scheduler. The RAN scheduler, within the radio architecture is extremely processing intensive and is responsible, among other operations of the real time the uplink and downlink radio decoding and encoding.

Running concurrently with the L1/Phy, RLC and running on the MAC layer, the scheduler reads data from the upstream RLC and transmits to the downstream PHY. The scheduler effectively determines the number of bytes to transmit to each UE in real time.

Since the scheduler is in essence a real time forwarding engine, it is instantiated in the DU and the fronthaul connectivity should have less than 1ms latency to the RU. This stringent latency envelope requires extremely tight integration between the DU, the RU and the near real time RIC (and its associated xApps). While theoretically functionally feasible, the level of integration between all these vendors necessary to realize xApp with the appropriate level of control and performance is generally not there.

The vendors, naturally, first prioritize integration between their own products and in this case, the DU vendors are in control of that value chain.

Understanding that today, there is a very limited number of DU vendors, who are all in the process of realizing the O-RAN first generation implementation and integration, and understanding that all their resources are mobilized on commercial deployments where the priority is the functional, stable and performing implementation of RU, CU and DU, it is not a surprise that we do not see much multi vendor activity on near real time RIC, xApps integration with real RU and CU DU.

 

While we have several examples of trials with either non MIMO CU DU RU or proof of concepts with RU, CU, DU emulators, we are still far from real end to end deployment even in trial situation of a an end to end implementation close to commercial grade.

The second impediment to near real time RIC xApp multi vendor implementation is commercial and can be found in the report.

RICs and Apps executive summary

I first came across open RAN as a concept in 2016, as one of the teams I was supporting at Telefonica was looking into connecting the unconnected in Latin America. After ascertaining the demand, the primary problem to solve was affordability. It just wasn’t economical to deploy traditional RAN networks designed for dense urban environment in the middle of the jungle. The team came out with an interesting idea, the disaggregation of the RAN into components, allowing to pool resources and enabling multiple vendors to compete for different parts of the deployment.

After a few iterations and field trials, the team went on to write the first Open RAN RFI, jointly with Vodafone, released at TIP. From this idea, an ecosystem was born, with a community of operators and vendors, issuing specifications at the Open RAN alliance, elaborating and testing blueprints, issuing requirements and roadmap at the Telecom Infra Project and commercial deployments slowly spreading from Japan to Western Europe and North America.

As the first layer of disaggregation has split the gNodeB into the Radio Units, Centralized Units and Distributed Units, an ecosystem of vendors has emerged, addressing each or all the parts of this new architecture. That first layer of disaggregation was aimed at disrupting the traditional RAN ecosystem, breaking the oligopoly of vendors that have come to dominate the market.

The next stage of disaggregation is aimed at disrupting further the market, by introducing elements of vendor-independent centralized management, optimization, visualization and orchestration with the introduction of the Radio Interface Controllers (RICs). Two RICs have been defined, and although they share the same name, they are quite different in nature and ambition.

The non real time RIC is part of the Service Management and Orchestration layer and is the evolution of Self Organizing Networks (SON) and the RAN OSS / Element management. Features instantiated on the non real time RIC can be deployed as rApps, a standard-defined set of interfaces for multiple vendors to deploy.

The near real time RIC is a feature set belonging to the RAN layer and aimed at disaggregating it further. It extracts capabilities today embedded in the gNodeB, or the RU, CU, DU and provides a layer of abstraction and platform for multiple vendors to theoretically pilot and tune the Radio Network. xApps are the applications that can be developed to be deployed on near real time RIC.

From my experience at Telefonica, as an operator or at Bell Canada or Deutsche Telekom as advisor and consultant or from my time at NEC as global VP Product Management overlooking the development and partnerships surrounding open RAN products, or as an independent analyst researching the market, I have derived a unique perspective on the maturity and effectiveness of open RAN, RICs and Apps. 

This report examines the architecture, strength, drawbacks of open RAN RICs and apps as well as provides an inventory of the main players in the space.