For CSPs, the path from 5G Standalone to 5G-Advanced and the eventual 6G architecture represents a paradigm shift. We are no longer simply provisioning capacity; we are transitioning to intent-driven, AI-native platforms capable of exposing distributed edge compute, ensuring deterministic ultra-reliable low-latency communications (URLLC), and orchestrating multi-domain autonomous operations.

As MWC 2026 is round the corner, let’s take a look at some of the latest developments across the Telco ecosystem and deep-dive into seven of the most pivotal announcements and white papers shaping the immediate future of our network infrastructure.

1. Scaling Autonomous Networks: Deutsche Telekom’s MINDR and the Agent-to-Agent Protocol

The operational complexity of managing multi-domain, multi-vendor telecommunications networks has long outpaced the capabilities of legacy, script-based automation. In a massive leap toward Level 4/5 Autonomous Networks, Deutsche Telekom, in collaboration with Google Cloud, has announced the development of MINDR (Multi-Agentic Intelligent Network Diagnostics & Remediation). This platform fundamentally shifts network operations from reactive alarm-chasing to predictive, service-driven automation that resolves anomalies before the end-user experience is impacted.

From a technical architecture perspective, MINDR is built utilizing Google’s Gemini models deployed on Vertex Cloud and incorporates Google Cloud’s Autonomous Network Operations framework. Unlike isolated automation silos, MINDR operates as a collaborative multi-agent system. It is designed to utilize the Agent-to-Agent (A2A) protocol to orchestrate specialized AI agents across the Radio Access Network (RAN), transport, and core domains. These agents continuously ingest and correlate network telemetry to build a real-time, end-to-end view of service performance, enabling autonomous root-cause analysis and explainable remediation actions.

The commercial viability and operational ROI of this agentic approach have already been proven in the field. MINDR is an evolution of Deutsche Telekom’s RAN Guardian Agent, which has been operating live in Germany’s commercial network. During high-traffic events, RAN Guardian autonomously triggered over 100 remediation actions within its first month, reducing the operational time required to manage major network events from several hours down to approximately one minute—a staggering >95% operational improvement.

During the February Carnival season, the system autonomously pre-checked 611 different mobile sites serving over 130 events. When five of these sites experienced unexpected peak loads, the AI agent dynamically optimized the radio parameters in real-time. With MINDR extending these capabilities beyond the RAN into the transport and core domains, Deutsche Telekom is actively scaling this self-healing infrastructure across its European footprint, beginning with the Czech Republic and Croatia. For telco professionals, MINDR represents the blueprint for deploying governed, multi-agentic AI to drastically lower OPEX and secure strict Service Level Agreements (SLAs).

Read more here.

2. Eliminating Handover Jitter: Ericsson’s L1/L2 Triggered Mobility (LTM)

For time-critical enterprise use cases—such as immersive Extended Reality (XR), automated guided vehicles (AGVs), and remote industrial robotics—seamless cellular mobility is a strict technical prerequisite. Ericsson, partnering with KDDI and MediaTek, has successfully completed the world’s first in-field joint demonstration of Layer 1/Layer 2 (L1/L2) Triggered Mobility (LTM) on a live commercial Radio Access Network.

Standardized as part of the 3GPP Release 18 specifications, LTM introduces a fundamental architectural enhancement to the 5G Advanced Critical IoT subscription tier. Historically, cellular handovers and mobility signaling have relied on legacy Layer 3 (RRC) messaging. Layer 3 mobility inherently introduces processing overhead and scheduling delays, leading to data interruption during cell changes that can trigger safety hazards in autonomous operations or cause severe user nausea in XR environments.

LTM bypasses this bottleneck by executing mobility commands using lower-layer (L1/L2) signaling. Ericsson’s proprietary software algorithms leverage this 3GPP standard to drastically reduce signaling overhead, shortening the data interruption period during cell changes by a definitive 25%. Furthermore, the technical design of Ericsson’s LTM implementation is highly efficient; it smartly reuses existing Layer 3 network measurements while enabling early downlink and uplink synchronization using a single trigger. It also lowers User Equipment (UE) requirements, ensuring broader compatibility across devices with varying capabilities.

For communications service providers, this is a highly monetizable capability. By transitioning from standard 5G Standalone to 5G-Advanced architectures equipped with LTM, operators can provide the near-seamless connectivity required by latency-sensitive AI and cloud applications. KDDI has explicitly highlighted that this low-latency mobility is foundational for supporting AI-powered real-time applications and ensuring operational efficiency and safety in Japan’s industrial sectors. Adopting standards-based LTM allows telcos to future-proof their 5G Advanced capital investments while accelerating the introduction of premium, time-critical enterprise services.

Read more here.

3. Architecting for Value: TM Forum’s 6G Monetization Blueprint

The telecommunications industry learned a difficult lesson during the initial rollout of 5G: deploying advanced radio capabilities utilizing Non-Standalone (NSA) architectures and fragmented legacy IT systems severely bottlenecked service readiness and limited monetization. To ensure the industry does not repeat these mistakes, the TM Forum—in collaboration with major operators—has released a comprehensive white paper (IG1485) outlining a monetization-driven architecture for the 6G era.

The TM Forum postulates that 6G must evolve beyond a “dumb pipe” connectivity foundation into an AI-native platform capable of on-demand experiences and programmable network exposure. To achieve this, the architecture must tightly couple a 6G RAN featuring native intelligence with a 6G AI-native Core that embeds AI-driven control, policy, and analytics directly into core network functions. The white paper outlines three primary 6G monetization models. The first is “Differentiated Experience,” which extends traditional data plans with static QoS tiers. The second, “On-Demand Experience,” introduces dynamic, time-bound connectivity (e.g., temporary QoS boosts for factory production windows), requiring substantial upgrades to real-time policy and charging functions. The third, “Enablement Beyond Connectivity,” exposes sensing, AI, and edge compute via APIs to developers, utilizing outcome-linked B2B2X contracts.

To execute these models, the TM Forum insists on deploying the Open Digital Architecture (ODA), which acts as a “Marketplace OS”. ODA replaces rigid, siloed legacy BSS/OSS with a component-based, cloud-native architecture, enabling “Composable Commerce” so operators can rapidly assemble billing engines for specific verticals (like drone traffic management) without bespoke IT projects. Additionally, achieving Level 4/5 Autonomous Networks is critical; automated, closed-loop service assurance is necessary to dynamically enforce SLAs in real-time and prevent SLA penalty payouts on high-value guaranteed-performance contracts. By standardizing multi-sided marketplace platforms and utilizing Open APIs, telcos can securely expose these 6G capabilities to third parties, transitioning from selling raw capacity to orchestrating high-margin platform ecosystems.

Read more about TMForum 6G White-paper here.

4. Resolving Un-Scripted Anomalies: NTT DOCOMO & AWS Agentic AI Operations

As mobile network architectures scale to support both 4G and 5G non-standalone/standalone environments alongside multi-domain and multi-vendor equipment, the complexity of network maintenance has skyrocketed. Legacy operational support systems (OSS) typically rely on script-based automation, which is highly effective for predefined, well-understood network failures. However, when complex, un-scripted anomalies occur, operations teams are forced to manually collect and parse through massive volumes of data from disparate domains to identify the root cause, resulting in unacceptable Mean Time to Repair (MTTR) metrics.

To directly combat this operational bottleneck, NTT DOCOMO has announced the commercial deployment of a massive-scale agentic AI system for network maintenance, developed in partnership with AWS. Deployed across their commercial mobile network as of early February 2026, this platform is engineered on Amazon Bedrock AgentCore, ensuring the secure governance and execution of agentic AI workloads at scale.

The technical scope of this deployment is unprecedented. The platform ingests and correlates real-time traffic and alarm telemetry from over one million network devices, spanning both base stations and core network equipment. To process this massive data lake, DOCOMO utilizes high-performance databases specifically optimized for time-series, tabular, and graph data workloads. By leveraging a graph-modeled network topology, multiple AI agents are orchestrated to autonomously analyze network behavior, detect anomalies, pinpoint suspected failure nodes, and present deterministic remediation recommendations to maintenance engineers.

By training and operating this agentic architecture on one of the world’s largest telecommunications datasets, DOCOMO has achieved a greater than 50% reduction in response times for complex network failures that previously demanded intensive manual analysis. For network operations professionals, this deployment validates that utilizing cloud-native agentic AI and graph-based topology modeling is the definitive path to achieving Autonomous Network operations, slashing service disruption windows, and guaranteeing the high reliability required for advanced 5G and 6G services.

Read more here.

5. Deterministic Slicing for Robotics: Configured Grant and Real Haptics

Providing connectivity for remote robot teleoperation is one of the most demanding URLLC use cases in the enterprise 5G portfolio. For remote operators to perform delicate tasks using advanced robotics, bidirectional force feedback must be transmitted with absolute precision. High or fluctuating latency (jitter) disrupts the synchronization between the operator (“leader”) and the remote robot (“follower”), rendering precise force reproduction impossible.

NTT DOCOMO and Keio University’s Haptics Research Center have successfully addressed this physical layer constraint, demonstrating the world’s first stable, high-fidelity robot teleoperation over a commercial 5G Standalone network. The trial utilized Keio’s Real Haptics technology—which bidirectionally transmits tactile and contact information—layered over a specific 5G SA network slicing technology known as Configured Grant.

In standard 5G SA deployments, devices communicate using a “Dynamic Grant” scheduling method. When a User Equipment (UE) needs to transmit data, it must first send a resource request to the base station. The base station processes this request and allocates resources, introducing a “scheduling delay” that fluctuates wildly depending on background network congestion. Configured Grant completely bypasses this bottleneck. By pre-allocating exclusive communication resources to a specific device line for a defined period, the UE can transmit data instantly without executing a resource request. This effectively eliminates scheduling delays, flattening jitter and ensuring deterministic, ultra-low latency.

The technical trial routed control data through DOCOMO’s commercial 5G SA network and a docomo MEC private network, terminating at a virtual server running the Bilateral Edge Platform. To simulate harsh, real-world conditions, 20 Mbps of background traffic was injected alongside the control data. The empirical results heavily validate the architecture: utilizing Configured Grant increased the force-feedback reproduction rate by an impressive 40%, delivering highly precise tactile feedback. Concurrently, the smoothness of the robotic movements—quantified via Dimensionless Jerk Cost—decreased by 59%, ensuring highly stable control. This proves Configured Grant is an essential slicing capability for monetizing industrial B2B robotics.

Read more here.

6. Monetizing the Edge: SoftBank and Nokia’s AI-RAN Orchestrator

The transition to virtualized Radio Access Networks (vRAN) has laid the groundwork for entirely new infrastructure utilization models. SoftBank and Nokia have announced a critical functional expansion to the AITRAS Orchestrator—part of SoftBank’s AI-RAN product portfolio—that transforms the telco edge into a brokered, distributed AI execution platform.

The AITRAS platform is designed to natively converge AI workloads and vRAN control functions onto a single, unified virtualization platform. Previously, the AITRAS Orchestrator dynamically balanced computing resources solely between SoftBank’s internal RAN control requirements and internal AI processing tasks. However, the cyclical nature of mobile traffic dictates that RAN compute demand fluctuates significantly based on the time of day. Restricting the platform to internal workloads inevitably results in stranded, underutilized computing resources during off-peak hours, diminishing the capital investment efficiency of the infrastructure.

To resolve this and generate net-new revenue streams, SoftBank integrated Nokia Bell Labs’s AI platform—the Nokia AI-RAN External Compute Engine—into the AITRAS Orchestrator. This powerful integration allows the orchestrator to securely broker, partition, and manage telecommunications computing resources for external enterprise clients. External customers can now dynamically access high-performance AI compute power directly at the telco edge, entirely on-demand, without requiring heavy capital expenditure in their own AI hardware.

This technical achievement realizes the “Execution of External AI Workloads” use case formally defined by the AI-RAN Alliance’s Working Group. For telco strategists, this represents a fundamental evolution of the business model. By expanding the AI-RAN architecture to seamlessly accommodate external B2B AI demands, operators can efficiently monetize dormant compute cycles. We are no longer simply selling connectivity; we are operating a distributed, high-margin computing utility service that maximizes the ROI of our localized edge infrastructure.

Read more here.

7. Optimizing Data Pipelines for AI Agents: 6G and IOWN Integration

As we project toward the 6G horizon, the proliferation of continuously operating AI agents will introduce crushing capacity demands on mobile networks. If a user wearing Augmented Reality (AR) glasses utilizes an AI agent to constantly monitor their environment for safety risks, the continuous ingestion of multimodal sensor data (high-resolution video, audio, spatial telemetry) creates three distinct technical bottlenecks: severe wireless bandwidth starvation, immense computational processing loads, and unsustainable power consumption at the edge and core. Furthermore, processing every raw frame cumulatively increases end-to-end (E2E) latency, destroying the real-time feedback loop required for AR assistance.

To solve this, The University of Tokyo, NTT, and NEC have successfully demonstrated an integrated 6G/IOWN architectural platform combining three groundbreaking technologies. First, to address wireless bandwidth constraints, the platform utilizes Streaming Semantic Communication. Instead of transmitting raw bit-level video streams, this protocol detects contextual changes and transmits only the semantic differences, radically compressing the required wireless payload.

Second, to mitigate the computational load of continuous inference, the system employs AI-Oriented Media Control. This technology applies data identifiers to the incoming stream, selectively filtering and feeding only the most critical, relevant sensor frames to the AI agent. Finally, to address massive AI model scaling, the architecture leverages In-Network Computing (INC). INC distributes small, specialized AI processing tasks deep within the network core, eliminating the need to haul all data to a centralized cloud and drastically reducing latency.

In a trial utilizing a 60-second, 1,800-frame critical situation video dataset, the integration of these three technologies yielded exceptional results. The platform successfully maintained an almost constant E2E latency profile without any cumulative processing wait times. Crucially, this massive reduction in communication traffic and computational load was achieved with zero degradation in AI inference accuracy. This trial definitively proves that optimizing data transmission pipelines at the semantic level is an absolute requirement for supporting real-time, AI-native 6G applications.

Read more here.

Conclusion

As we analyze the technical roadmaps presented by these industry leaders, a cohesive vision for the future of telecommunications emerges. The network is no longer a passive conduit for data. By implementing Configured Grant and Layer 1/Layer 2 Triggered Mobility, we are guaranteeing the strict determinism required for industrial automation and XR. By deploying governed, multi-agentic AI architectures like MINDR and AWS Bedrock-powered maintenance platforms, we are achieving the Level 4/5 autonomy necessary to manage multi-domain complexity and defend service level agreements.

Furthermore, by adopting the TM Forum’s Open Digital Architecture and SoftBank’s AI-RAN external compute brokering, we are actively unlocking the next generation of B2B2X revenue streams. Moving forward into the 6G and IOWN era, the optimization of semantic data pipelines and In-Network Computing will ensure our infrastructure can scale to support the massive influx of autonomous AI agents. For telco professionals, the technical foundations for a highly programmable, vastly monetizable, and fully autonomous intelligent edge are officially here.

On the streets of Hong Kong, a notable trend is emerging in response to mobile-based cybersecurity concerns. Individuals and employees are increasingly adopting ‘burner phones’ – secondary mobile devices used to discreetly handle sensitive communications and transactions. This is no quirky tech trend however, it reflects increasingly widespread concerns about the ability of network operators to protect against intrusions, exfiltration of data, and exploitation of unauthorized access by threat actors.

The Changing Face of Cybersecurity in Hong Kong

The advent of such concerns brings with it a new level of complexity for companies – and individuals – doing business in Hong Kong. This has had a notable impact on how companies, both local and international, structure their own data security and privacy policies. The challenge lies in navigating this new terrain where the lines between safeguarding individual privacy, company data confidentiality, and national security appear increasingly blurred.

The Emergence of ‘Burner Phones’ as a Defensive Measure

The growing use of ‘burner phones’ in Hong Kong is a direct response to the heightened cybersecurity awareness in the region. These secondary devices, typically less advanced than a user’s primary smartphone, are being adopted as a practical measure to safeguard sensitive information. The rationale behind this trend is clear: in an environment where the risk of data breaches is perceived to be high, having a separate device can provide not just an additional layer of security but a way to avoid or at least to minimize the exposure of personal and company data to unauthorized access by not having to connect those devices, which present direct gateways to such data for attackers, to local network services at all. This practice is not just limited to tech-savvy individuals, but is increasingly being seen as a necessary precaution by businesses concerned about protecting their client data and proprietary information.

But this isn’t just a question of good security housekeeping. It underscores a broader crisis of confidence in the ability of network operators to protect against sophisticated cyber threats. In this context, the humble ‘burner phone’ has emerged as a symbolic and practical tool for individuals and organizations striving to exercise control over perceived risks to digital privacy, data confidentiality and personal security.

The Challenge of Securing Mobile Networks

The unique nature of mobile network security presents a distinct challenge that sets it apart from conventional cybersecurity. In mobile communications, threats and vulnerabilities exist at a network level, often beyond the control of individual users or businesses. The European Union Agency for Cybersecurity (ENISA) has long pointed out that individuals are largely powerless in protecting themselves against such threats, as the attacks and resultant data leakage occur within the providers’ core networks. This situation places a significant portion of the responsibility for cybersecurity on the shoulders of the network providers, rather than the end-users.

ENISA says, “One important factor to mention is that in most cases, the subscriber cannot do too much in order to protect themselves from these risks. As most of the attacks are developed at the providers’ level (as both SS7 and Diameter are protocols functioning within the providers’ core network), the possible actions available for subscribers are very limited (e.g. encryption). Most of the security work has to be done at the providers’ level.”

ENISA isn’t alone in this perspective. For instance, the new US National Cybersecurity Strategy highlights that too much responsibility for cybersecurity has historically been placed on individual users. Similarly, Australia’s Cyber Security Strategy emphasizes the need to block cyber threats before they may reach end users. These strategies indicate a growing recognition of the need for a more proactive approach by network operators and governments to resource protection at the network level against unauthorized access by threat actors.

In this context, the growing adoption of ‘burner phone’ usage not merely as informal practice but as a matter of policy is a cry for help amid a crisis of confidence in mobile network security.

Should Telcos choose Public Cloud (or hyperscalers) for 5G Network Function #NF deployments?

It’s an interesting question.

Two years back, when we saw hyperscalars thronged to MWC, Barcelona (MWC21), everyone talked about how public cloud is going to win over the complex beast named telcos. It appeared easy win at first glance!

Two years down the line (or two and half now), hyperscalers momentum couldn’t win many telcos as customer esp. from Network perspective. By network I mean, telcos choosing public cloud for deploying their 5G NSA or SA Core, RAN and other network functions. Barring Dish (AWS), AT&T (Azure) and few more, I don’t see many adopters for public cloud for Network workloads (NFV). There are just handful of examples and some of them are pilot stages or couldn’t scale to pan nation network yet.

But why aren’t many telcos adopting public cloud for network workloads?

There are few obvious reasons for not choosing public cloud. Let’s dive deep into them.

1. Complexity of Network Functions (NFs): Both VNF CNF, are complex stateful applications. They may involve hundreds of microservices as one application or more. They could include databases, message queues, cache and storage apart from stateless containers or VMs. Typically, these NFs are quite large in size than enterprise applications getting deployed on any public cloud platforms. Deploying this scale of NFs on any virtualised or containerised platform is extremely complex process. Although public cloud offers rich tools for automated deployment, telcos haven’t yet zeroed down completely on adopting DevOps based automation. They are in early stage of automation adoption, which could not only complicate deployment of NFs but also upgrade and entire life cycle aspects of it.
2. TCO: Cost or TCO is another crucial factor which weighs in slowly with hyperscalers. Public cloud providers use Pay-As-You-Go model. Running the complex and extremely large NFs all the time, puts the hole in pocket. Despite all discounts and ephemeral instances positioning, running heavy workloads on public cloud is expensive. All benefits of saving #CAPEX and simplicity of usages are gone, come 3-5 years down the line. I believe there’s no TCO benefit running #NFs for 5 years or beyond with hyperscalers. Moreover, with public cloud, you are entirely operating with OPEX model. The benefits of cloud elasticity, easy scale out and scale in doesn’t fit the OPEX math as well.
3. Operations & Management: Operating public cloud with complex #NFs bring another layer of complexity in addition to managing existing private cloud deployments. Telcos already running a large network on-prem, including 4G LTE VNFs. Only with 5G, and arrival of cloud native, they are opting out for public cloud. Proponents might believe in creating a single pane of glass of observability across multi-cloud, doing resourceops, automation, security & policy automation with day1 and day2 ops is quite challenging and adds to ongoing opex costs budgets. Moreover, telcos typically lack skillset of manage and operate multi-cloud setup efficiently and cost effectively. It could add burden to telcos, if they decide to move large NFs to public cloud.
4. Vendor Lock-in: While it is known fact by know that it’s easy to move in to public cloud, it’s not possible to leave them, if at all you wish to. Reason? The tech stack differences. Every cloud provider has its own way of building virtualisation and containerisation layers. On important level, they may appear same, but they are quite different in nutshell. They use different OS and their libraries to build platforms. Your applications containers, once sit on those platform layers, it’s extremely difficult to move them out to another platform, offering separate set of OS and libraries. We normally call it as vendor lock-in. Would a telco want to lock their big fat NFs to single public cloud provider platform? The cost associated to move workloads from one cloud to another is also huge and takes weeks or months to even execute the migration.
5. Security/Data Sovereignty: Moving your NFs workloads to public cloud could lead to loss of control, esp. the infra and platform layers. You just don’t own those layers, as hyperscalars had invested millions or billions to build their own state of art DCs. It’s big deal. Moreover, as you move workloads to them, your data, which was until now was well guarded within on premises boundaries, now reside on some other public network (still private), gets processed and stored outside. It could be quite challenging to deal with this, albeit fact that, hyperscalars offer excellent set of security of their own stack, leaving door open for managing security to your workloads on your own. Moreover, there’s ingress and egress costs associated with data transfer, which is quite complex to factor in.
6. Solutions Stitching: It’s important to understand why public cloud doesn’t solve your problems unless you do. Although you get multitude of ready to consume services with single click, in the end it’s telcos responsibility to create a complete solution blueprint out of those services. Moreover, you need to now manage and stitch multiple moving parts, as part of blueprint, even on single public cloud provider. Telcos have always relied on their vendors to help them here and possibly lack of skills could lead to not building right blueprints on cloud. Managing these architecture blueprints, and version controlling architectures over longer period is immensely challenging in multi-cloud environments.
7. Workload Performance: While many telco grade workloads need to run at specific performance benchmark of network and compute latencies, including RAN (DU), it’s not easy to get similar set of performances on public cloud platforms. There are all sorts of hardware acceleration (SRIOV, DPDKs) required for faster data paths to meet latency requirements of workloads. While public cloud has evolved to great extent, it’s not easy to deliver similar performances always, and telcos could face challenges in delivery requisites performance.

While telcos are quite reluctant for adopting public cloud for network workloads, they are certainly embracing it for running their IT workloads namely BSS and OSS. Public cloud is quite well suited for these types of IT workloads and allow them the benefit of faster time to market for their offerings, and elasticity required for those services.

In the end, there’s no clear formulae here, if a telco should adopt public cloud for network, IT workloads or not. It depends on multiple factors including size and scale of workloads, current and future traffic growth, regional spread of services and also should be aligned with long term vision of telcos transformation. It could be seen that some medium to smaller telcos have quickly adopted public cloud for 5G, large telcos, with wider geographic spread, large customer base, mayn’t have agility to move to cloud easily. But again, there’s no thumb rule here.

People often cite names of early adopters when they debate about the subject. But I believe many of those adopter aren’t being able to run a nation-wide network, at decent traffic size. I would tag those names as outliers and not early adopters. It’s still to be seen that telcos can run their NFs on public cloud at scale for wider geographies handling decent traffic size, efficiently and cost effectively.

There’s an excellent discussion around the subject few months back from ABI Research, when they debated the topic about telco cloud vs public cloud. It could be seen that, ABI research found that, running NFs on telco cloud, on your premises is cost effective than running it on public cloud.

When we talk about public cloud, many proponents believe that, moving to OPEX model (Pay as You Go) wins over CAPEX model of DC build out. But again as per ABI Research, OPEX operating model of public cloud proven to be costly, compared to CAPEX based model. In other words, building your own private telco cloud, and running NFs on it, over a period of ten years, offers better TCO benefits.

While it is to be seen how telcos adopt public cloud, and address those challenges mentioned above. Telcos can still adopt public cloud for onboarding NFs, provided they address those challenges effectively by adopting solutions such as Red Hat OpenShift Container Platform, which offer no vendor lock-in approach for hybrid multi-cloud environment, offering complete control over costs, and manageability with single dashboard for containerised environments across on-prem, public cloud and edge.

(All views are personal and author doesn’t endorse or claim the credits of any research on subject by any third parties. Red Hat OpenShift and Public Cloud offerings are own by respective Organisations.)

ABI Research Whitepaper could be found here.

Deutsche Telekom today underlined its ongoing commitment to the development of Open RAN technology, by announcing details of a multi-vendor trial around programmable radio access networks that demonstrates the potential of the Non-RT RIC and rApp concept to automate and optimize disaggregated RAN. 

The Non-RT RIC brings intelligence, agility, and programmability to disaggregated radio access networks and enables third-party applications (rApps) that can perform closed-loop automation and optimization of RAN elements and resources. However, the multi-vendor integration of Non-RT RIC, rApps and SMO also introduces challenges that must be addressed. 

Working together with AirHop, Juniper Networks, VIAVI Solutions and VMware, the partners completed a RAN closed-loop optimization Proof of Concept (PoC) within Deutsche Telekom’s lab environment in a multi-vendor setup based on ONAP & O-RAN specifications. Closed loop rApp algorithms were onboarded and deployed on partners’ Non-RT RIC. During the PoC, partners successfully executed two use cases:

• Physical Cell Identifier (PCI) optimization focused on detection and resolution of PCI confusion and collision scenarios.
• Energy Savings dynamic Multi-Carrier management (ESMC) using an Artificial Intelligence and Machine Learning (AI/ML) model, trained to determine the optimum time to enable/disable sleep-mode on capacity cells in order to save energy while maintaining user quality of experience (QoE).

Initial tests were performed in a real end-to-end lab setup using a small O-RAN network to validate end-to-end configuration and performance management (CM & PM) integration for a real network environment. Most tests were executed on a more complex network setup using an O1 network emulator (RIC tester) to validate rApp logic and stress test the RIC components to benchmark the various solutions. 

About the setup

• Deutsche Telekom provided a self-developed SMO framework along with a Non-RT RIC solution based on the O-RAN SC Non-RT RIC
• Juniper Networks and VMware integrated their Non-RT RIC products into DT’s SMO framework
• AirHop integrated two rApps for PCI optimization and ESMC with each Non-RT RIC
• VIAVI provided their RIC tester to emulate the O1 interface

The multi-vendor framework presents integration challenges. However, this PoC has shown from a high-level perspective that the adoption of the SMO, Non-RT RIC and rApp framework is promising in how it allows for the decoupling of optimization algorithm development, the supporting platform development and the system integration – so that components from different parties can form a truly disaggregated RAN optimization concept.

“With this PoC, we set out to assess the technical integration complexity of the components delivered by each party, the level of customization required, to gauge the maturity of products and to identify potential future standardization requirements,” stated Petr Ledl, VP, Head of Network Trials and Integration Lab, Deutsche Telekom.

A detailed outline of these challenges, as well as customizations and future standardization requirements, plus areas of further focus, are detailed in the trial White Paper (pdf, 1.2 MB). 

“At DT our primary focus is always on driving innovation to support the best customer experience. The RIC and rApps are key to programmability, automation, and optimization in radio access networks. Taking the learnings from this successful trial, we will now continue the work with our ecosystem partners to accelerate Non-RT RIC/rApp development towards production readiness”, added Petr Ledl.    

RAN Intelligent Controller (RIC) and Apps

A RAN Intelligent Controller (RIC) is a software-defined component of the Open RAN architecture that’s responsible for controlling and optimizing RAN functions behavior. The RIC enables fast onboarding of third-party applications (Apps) that automate and optimize RAN operations at scale. It also supports innovative use cases while providing lower introduction time and ultimately lower total cost of ownership (TCO) of mobile operators as well as enhancements to customers’ quality of experience (QoE).

The non-real-time RIC (Non-RT RIC) is part of the Service Management and Orchestration (SMO) framework, centrally deployed in the service provider network. It enables greater-than-one-second control and policy guidance over the RAN elements and their resources through so called rApps.

It also enables AI/ML capabilities for the RAN and uses long-term network data, such as performance metrics as well as enrichment data from external applications to train and generate AI/ML-driven applications.

AIOps uses advanced analytics and machine learning algorithms to analyze large volumes of data generated by IT systems and applications. It can identify patterns, detect anomalies, and provide insights into the root cause of issues. AIOps can also automate routine tasks, such as monitoring, alerting, and incident management, freeing up IT staff to focus on more strategic activities.

One of the key challenges faced by modern organization is processing infinite amount of data, which could easily overwhelm operations teams. Moreover, beyond just processing, making sense of information, esp. Operational awareness is critical for success of today’s IT Operations.

Another key driver for adoption of AIOps in modern IT Organization, is adoption of multi-cloud. While many organizations, including Telco Service providers, still have large on-prem deployments, public cloud/hybrid cloud has entered the premises, where few workloads have already made shift to Cloud. Operationalizing (Day1/2 and beyond) multi-cloud environments, with enormous amount of data gathered, is next level challenges IT Ops teams must tackle daily.

While AIOps solutions catering to modern IT Organization, for Telco Service providers, AIOps is domain specific challenge to solve. The main difference is data they directly collect and use cases they solve, beyond typical AIOps Use cases offered in IT Organizations. It could be termed as Domain specific AIOps, while Domain Agnostic AIOps caters to wider IT landscape and use cases.

In the telecom industry, AIOps can be used to improve network performance, reduce downtime, and enhance the customer experience. AIOps can help telecom operators monitor and manage their network infrastructure, detect, and resolve issues in real-time, and optimize network resources to meet changing demands. AIOps can also help telecom operators analyze customer data to gain insights into customer behavior and preferences, enabling them to offer more personalized services and improve customer satisfaction.

AIOps is a rapidly evolving field, and new use cases and applications are emerging all the time. Some of the key trends in AIOps include the use of AI and ML to automate IT operations processes, the integration of AIOps with DevOps and other IT processes, and the use of AIOps to improve security and compliance. As AIOps continues to evolve, it is likely to become an increasingly valuable tool for IT teams in a wide range of industries, including telecom.

How AIOps differs with Modern IT Operations in Telecom:

One big question arises among many Telco Service providers is that, how AIOps differs from their tradition Operations. It’s quite an interesting question. Let’s discuss it briefly.

If you look at modern IT or even Telecom Service providers, Monitoring or even modern Observability stack is quite siloed. Multiple vendors/partners have built solutions to monitor and operationalize their own stacks. E.g. Network Vendors such as Cisco or Juniper offer Network Observability data of their devices separately or even Platform/Cloud Providers, offer Platform level metrics and logs stored in their respective Observability solutions. In addition, Application providers, they offer their application specific monitoring tools, which are also siloed in most cases.

For Service providers, it creates a huge challenge to get complete picture of their entire network and organization at one place. Moreover, amount of data needs to collect, gathered from multitude of systems, and processed at one place such as central data lake is daunting task for them.

Modern AIOps tends to solve these sets of challenge. Irrespective of if you have implemented central Observability solutions (refer ELK or EFK stack) in your organization, AIOps solutions can collect, gather, and process infinite amount of data with help of advance AI/ML/Deep Learning Algorithms and help to generate actionable insights from the data quickly. It certainly helps to leverage central data warehouses or data lakes, if already available, but we have seen many AIOps solutions doesn’t mandate to have your own data lake built, which is quite a big relief for most of the service provider.

Moreover, as mentioned, many service providers have adopted multi hybrid cloud solutions for their 4G/5G deployments, which create Operational silos and overheads. AIOps solutions can address these challenges with multi-cloud, multi-platform metrics collection and offer single pane of glass to Operational team without worrying about siloed monitoring stacks offered by multiple partners.

AIOps Use Cases in Telecom

AIOps in Telecom has several use cases that can help telecom companies to improve their operations and customer experience. One of the primary use cases is network management. AIOps can help telecom companies to monitor their network infrastructure, identify issues, and resolve them proactively. This can help to reduce downtime, improve network performance, and enhance customer satisfaction.

Another use case of AIOps in Telecom is customer service. AIOps can help telecom companies to analyze customer data and provide personalized recommendations to customers. This can help to improve customer satisfaction and reduce churn rate. AIOps can also help to automate customer service processes, such as ticket routing and resolution, which can save time and resources for telecom companies.

One of the most popular use cases of AIOps in Telecom is predictive maintenance & fault remediation. AIOps can help telecom companies to predict equipment failures and schedule maintenance activities proactively. This can help to reduce downtime, improve network performance, and save costs for telecom companies. AIOps can also help to optimize maintenance schedules based on real-time data, which can further improve efficiency and reduce costs.

There are few upcoming trends in Telecom including sustainability, driving AIOps in Telecom to gather power consumption data across various topologies, analyse it and predict the future power consumption or drive power efficiency to automate life cycle of various infra components. Red Hat Inc, the Open-Source Software giant, has recently announced one such CNCF initiative, Project Kepler, which gathers various energy level metrices of Kubernetes clusters including Pods and Nodes, and export it. Red Hat intends to integrate Kepler in future OpenShift releases.

Finally, AIOps can be used for capacity planning in the Telecom industry. AIOps can help telecom companies to analyze network traffic data and predict future demand for network resources. This can help to optimize network capacity and ensure that telecom companies can meet the growing demand for data services. AIOps can also help to identify underutilized resources and optimize resource allocation, which can further reduce costs for telecom companies.

Benefits of using AIOps

AIOps in Telecom offers several benefits over traditional methods. One of the primary advantages is the ability to automate and streamline operations. AIOps can monitor and analyze vast amounts of data in real-time, allowing for faster and more accurate decision-making. This can lead to increased efficiency, reduced downtime, and improved customer satisfaction.

The benefit to automate and streamline remediation (Respond) is becoming critical aspect in evaluating benefits of AIOps solutions. Many IT Organization and Telco Service providers are adopting various IT Automation solutions including Ansible Automation to automate various manual repetitive tasks including various Operational processes. One such example is Red Hat Event Driven Ansible, announced in Red Hat Summit 2023. With event driven Ansible, now, AIOps solutions such as Dynatrace AIOps, can trigger execution of certain Ansible Playbooks directly without any manual intervention, leading to auto-remediation of operational issues discovered by AIOps solutions.

AIOps in Telecom can also help to reduce costs by optimizing resource utilization. By analyzing data on network traffic, usage patterns, and other factors, AIOps can identify areas where resources are being underutilized or overprovisioned. This information can be used to make more informed decisions about resource allocation, leading to cost savings and improved efficiency.

Another benefit of AIOps in Telecom is the ability to detect and resolve issues before they become critical. According to IBM, AIOps can reduce network downtime by up to 80%. A By leveraging machine learning and predictive analytics, AIOps can identify patterns and anomalies in data that may indicate a potential problem. This allows for proactive measures to be taken to prevent issues from occurring, rather than simply reacting to them after the fact.

Finally, AIOps in Telecom can help to improve the overall quality of service. By monitoring and analyzing data on network performance, customer behavior, and other factors, AIOps can identify areas where improvements can be made. This can lead to better service delivery, increased customer satisfaction, and improved business outcomes.

How Telcos using AIOps

Telecom companies are currently implementing AIOps in their operations in numerous ways. One of the most common use cases is in network management. AIOps can help telecom companies to monitor their networks in real-time, identify potential issues, and take proactive measures to prevent downtime. This can lead to improved network performance, increased customer satisfaction, and reduced costs. And as mentioned above, Predictive Maintenance and Fault remediation is one the popular use cases under network management category.

Telecom companies are also using AIOps in their marketing and sales operations. AIOps can help to analyze customer data and provide insights into customer behavior and preferences. This can help telecom companies to develop targeted marketing campaigns and improve their sales strategies.

Another way that telecom companies are using AIOps is in customer service. AIOps can help to automate customer service processes, such as chatbots and virtual assistants, which can provide customers with quick and efficient support. This can lead to improved customer satisfaction and reduced costs for telecom companies.

Finally, telecom companies are using AIOps in their security operations. AIOps can help to detect and respond to security threats in real-time, which can help to prevent data breaches and other security incidents. This can lead to improved security and reduced costs for telecom companies.

Trends in AIOps for Telcos

A trend that is emerging in the use of AIOps in the Telecom industry is the integration of machine learning and artificial intelligence algorithms into network operations. This allows for real-time monitoring and analysis of network performance, enabling proactive identification and resolution of issues before they impact customers.

According to BMC, approx. 75% of telecom organization plan to adopt AIOps in near future. Moreover, there are certain market reports, which states that telecom industry is expected to be one of the fastest-growing sectors in AIOps adoption.

One major trend we see clearly emerging is the use of AIOps for predictive maintenance and fault rectification/remediation. By analyzing data from network devices and sensors, equipment providers, ticketing systems, weather monitoring system etc AIOps tools can identify potential equipment failures before they occur, allowing for proactive maintenance and reducing downtime. Moreover, there’s growing trend to use IT Automation tools such as Ansible Automation to automate auto-remediation of Operational issues, discovered by AIOps solutions, without manual intervention.

Telecom companies are also using AIOps to improve customer experience. By analyzing customer data and behavior, AIOps can provide personalized recommendations and solutions, as well as identify potential issues before they become complaints. AIOps is also being used for network automation, enabling the creation of self-healing networks that can automatically detect and resolve issues without human intervention. This reduces the need for manual troubleshooting and allows for faster resolution times.

Moreover, we do see growing interest of using AIOps solutions to drive Telco sustainability goals. It’s likely to drive AIOps adoption faster among Telco service providers, esp. where tools such as Red Hat Kepler capturing Kubernetes Pod and Node power metrics and export it for further analysis and automate infra life cycles.

Finally, there is a trend towards the use of AIOps for security and fraud detection. By analyzing network traffic and user behavior, AIOps can identify potential security threats and fraudulent activity, enabling proactive measures to be taken to prevent them.

(All views are personal. All reference citations are for reference purpose and owned by respective Organization).

Generative AI works by using a neural network to analyze and learn from large datasets. The neural network is made up of layers of interconnected nodes, each of which performs a specific function. The first layer of the network takes in raw data, such as an image or a piece of text, and processes it in a way that makes it easier for the subsequent layers to analyze. Each subsequent layer builds on the work of the previous layer, gradually refining the data until the network can generate new content that is like the original data.

There are many diverse types of generative AI algorithms, each of which is designed to generate a specific type of content. For example, some algorithms are designed to generate realistic images, while others are designed to generate natural language text. Regardless of the type of algorithm, however, the goal is always the same: to create new content that is indistinguishable from content created by humans.

What are potential use cases of Generative AI for Telcos?

Generative AI has many potential use cases for telcos. For example, it could be used to generate personalized marketing content for individual customers, or to create virtual assistants that can interact with customers in a more natural way. It could also be used to generate new products and services based on customer data, or to analyze large datasets to identify trends and patterns that could be used to improve network performance. Overall, generative AI has the potential to revolutionize the way that telcos operate, by enabling them to create new content and services that are tailored to the needs of individual customers.

Generative AI has the potential to revolutionize the telecommunications industry by enabling telcos to automate and optimize various processes. One of the key benefits of using generative AI is that it can help telcos to improve their network performance by predicting and preventing network outages. This is achieved by analyzing large amounts of data from network devices and identifying patterns that could lead to outages. By proactively addressing these issues, telcos can reduce downtime and improve customer satisfaction.

Another potential benefit of using generative AI in the telecommunications industry is that it can help telcos to personalize their services and offerings. By analyzing customer data, generative AI can identify patterns and preferences that can be used to create personalized recommendations and offers. This can help telcos to improve customer loyalty and retention, as well as increase revenue by offering targeted promotions and services.

Generative AI can also help telcos to optimize their operations by automating various processes. For example, it can be used to automate customer service interactions, such as chatbots that can handle simple queries and requests. This can help to reduce the workload on human customer service agents, freeing them up to handle more complex issues. Additionally, generative AI can be used to optimize network routing and traffic management, which can help to improve network performance and reduce costs.

Finally, generative AI can help telcos to improve their security and prevent fraud. By analyzing network traffic and customer data, generative AI can identify potential security threats and fraudulent activity. This can help telcos to take proactive measures to prevent these issues, such as blocking suspicious traffic or alerting customers to potential fraud attempts. By improving security and preventing fraud, telcos can protect their customers and their reputation, as well as reduce financial losses.

Real world examples of Generative AI in Telecom:

There are many examples of real-world examples of generative AI used in Telecom as on today.

Generative AI has been successfully used in the telecom industry to improve customer experience. One example is the use of chatbots powered by generative AI to provide 24/7 customer support. These chatbots can understand natural language and provide personalized responses to customers, reducing the need for human intervention and improving response times.

Another successful use case of generative AI in telecom is predictive maintenance. By analyzing data from network equipment, generative AI algorithms can predict when equipment is likely to fail and alert technicians to perform maintenance before a failure occurs. This reduces downtime and improves network reliability.

Generative AI has also been used to optimize network performance. By analyzing network data in real-time, generative AI algorithms can identify network congestion and adjust network resources to improve performance. This can result in faster data transfer speeds and improved overall network performance.

Finally, generative AI has been used in the telecom industry to improve fraud detection. By analyzing call data records and other network data, generative AI algorithms can identify patterns of fraudulent activity and alert network operators to act. This can help reduce losses due to fraud and improve overall network security.

Challenges & Opportunities of implementing Generative AI in Telecom:

Implementing generative AI in the telecom industry presents several challenges. One of the main challenges is the lack of quality data. Generative AI requires enormous amounts of high-quality data to train the models effectively. However, the telecom industry generates vast amounts of data, but not all of it is relevant or useful for generative AI. Therefore, telecom companies need to invest in data quality management to ensure that the data used for generative AI is accurate, complete, and relevant.

Another challenge is the complexity of the telecom industry. The telecom industry is overly complex, with multiple layers of technology, including hardware, software, and networks. This complexity makes it difficult to implement generative AI, which requires a deep understanding of the underlying technology. Telecom companies need to invest in specialized talent and expertise to develop and implement generative AI solutions effectively.

Data privacy and security is another challenge when implementing generative AI in the telecom industry. Telecom companies collect and store vast amounts of sensitive customer data, including personal information, call records, and location data. This data must be protected from cyber threats and breaches. Therefore, telecom companies need to ensure that their generative AI solutions comply with data privacy and security regulations and implement robust security measures to protect customer data.

Finally, the cost of implementing generative AI in the telecom industry is a significant challenge. Developing and implementing generative AI solutions requires significant investment in technology, talent, and infrastructure. Telecom companies need to carefully evaluate the potential benefits of generative AI against the costs and risks of implementation. They need to ensure that the benefits of generative AI outweigh the costs and that the investment aligns with their business objectives and strategy.

For past few months, I noticed there’s deep sense of dilemma among Telcos on various transformational aspects, esp. when they are adopting hybrid cloud for digital transformation. Understandably this dilemma is good thing on Telcos part, but making right choices, with dilemma is not an easy task, and let me elaborate a bit further.

Telcos indeed embarked on digital transformation journey few years back. Few are ahead in the journeys, while others are just getting started. As part of the journeys, telcos need to undertake multiple journeys to transform various aspects of their entire business, be it network getting transformed or their IT or even their customer experience. Everything should or would undergo transformation, and the end goal is to build digitally transformed, more agile organization, ready to adapt and drive market to next level.

This isn’t easy goal to achieve. Its multi-year committed transformation journey, would require millions or even billions of USD, and would not only help telcos be more agile, but also earn more revenue through transformation and new value-added services portfolio but offer better overall customer experience in general.

Now coming back to dilemma, which I raised at start. To transform themselves, telcos need to build robust platforms, on top of which transformed telco would reside, build, scale their services offerings. Imagine platform is a layer of infrastructure, which is agile enough to adapt to market needs, and help telcos build their newer services on top, to cater, not only existing but newer market opportunities in long run. Now, their existing platforms, on which they built their services, including network, IT, or even bit of enterprise, aren’t agile, robust enough to take them to next level.

Indeed, they need newer platforms for sure. But what platforms they need to choose and should they adopt Cloud as Platform is a real dilemma.

Let me clarify a bit more here.

A cloud (or specifically Public Cloud) is not just a platform, but holistic (or complete) platform, which comes with offerings such as marketplace with third party & community app store, billing/metering, DC/DR, Backups, Storage, Observability, AI/MLOps Automation, Serverless and many more capabilities, with inherent mix of infinite scalability, elasticity, and resiliency. If you look at telcos existing platform, there is already infrastructure in place, a layer mix of hardware and software, very niche, proprietary, siloed one, which does offer resiliency (to some extent) but not elasticity or scalability to the extent public cloud offers.

Do Telcos need all these Public Cloud like platform features to get transformed?

I wonder the answer is yes, but not all of capabilities are must to have for them. Eventually, for a transformed telco, they might need some of them or many of them but not all. It’s overkill to adopt Cloud (or Public Cloud) as Platform. Moreover, there are concerns over cloud sovereignty, where telcos need similar offerings on-prem or within their premises.

There comes on-prem private platforms. Pls be mindful, as I’m resisting to use term private cloud. The moment you mix private platforms with private cloud, the expectations begin to escalate. When we refer to private platforms (or on-prem platforms), they simply mean platforms, which offers scalability, elasticity, and resiliency. They help to build agile services and transform organization towards more agile operations. They may or mayn’t bring cloud like capabilities such as app marketplace, billing or metering, DC/DR, Observability, Serverless, MLOps etc inherently with them. While some platform providers offer some of them, it comes as add-on. Moreover, platforms offered could be virtualized or containerised or fully managed/serverless types but, primarily they are platforms and not cloud.

When we refer to term private cloud, it brings all the above cloud offerings (public cloud offerings) into play, along with bare-bone platform stuff which otherwise a platform would offer.

You may ask, what’s difference though?

It depends on context in which you are asking this question, if you need a platform or a cloud, be it public or private. Or in other words, what’s use case for transformation? It would be the decider.

Let’s say you want to transform your network core to make it more agile and with 5G, you decided that 5G Core (entire suite of Core modules) would run on new platform or cloud, either way. If you just want to run 5G core modules on newer capable platforms, replacing older non-agile platforms, choose a platform. But if you need observability, or DC/DR or even CICD or integration with app stores or billing/metering, better choose a cloud (public cloud like offering) to support your use case. It is impractical to take a platform and expect cloud offerings and vice versa (that’s overkill gentleman).

A platform which offers scalability, elasticity and resiliency is good enough for many telco transformations use cases but not all telco transformational use cases will be well served by mere platforms or their add-on services (a complete PaaS). In many instances, you would need app stores or billing/metering or even DC/DRs or Observability or even serverless for your use case, then choosing cloud (or similar offerings) would make better sense.

Now what telcos should choose really depends on many factors including use case, budgets, timelines, roadmap or even their own vision of transformation would play a significant role in choosing between platform or cloud. Telcos need their platforms to support many use cases, and they would eventually evolve to go beyond mere connectivity providers, catering to enterprises with value added services. In those cases, private cloud would make more sense. But keep in mind that, not all telcos would have same vision of doing everything or going beyond connectivity services. With advent of 5G (& beyond 5G), there will be opportunities for them to offer many more services, but not all telcos will offer those. Imagine, every transformed telco is offering all types of services, including OTT, App, Music etc, how market will look?

In summary, every transformed telco would have different services offerings, and cloud or platforms would play a critical role in their services offerings. This is mainly because, they started with different transformational goals. If you look around telco landscape across globe, you could easily see pioneers and followers separately. But this distinction is quite vague and doesn’t stand scrutiny so let’s not say that pioneers will offer everything while followers will limit themselves to just connectivity offerings.

Now question comes, should all platforms evolve to clouds or public clouds to support transformation? Or would there be truly a private cloud offering everything of that of public cloud offerings?

I don’t see the point in this question though. I have yet to see anyone building platforms or clouds merely for telcos. They won’t. There’s always a larger play for both platform and cloud players, beyond telcos. So, what you build depends on customer needs and market demands.

The question is interesting enough to answer, because I could see platform providers are trying to build private clouds while public cloud providers are trying to build private clouds, and both are racing towards that. If you look around, enterprise or on-prem is bigger opportunity than public clouds, and those who are in business of building private on-prem platforms or private clouds, are building increasingly public cloud like offerings to enable a true private cloud for their telcos or enterprises.

Eventually, without must digressing from topic, you would see private clouds and platforms both getting transformed to more like public cloud, but it won’t be at par. The fundamental reason is their architectures are different altogether and by mere adding more capabilities won’t transform a platform into cloud or vice versa.

Telcos, if you have a vision to be truly a market leader, completely agile, go beyond connectivity and offer thousands of value-add services, either B2B or B2C, then manage them well, choosing cloud offerings makes more sense from start. But if you are exploring opportunities, transforming your core, and not looking at value added services play in longer run, choosing platforms or sticking to private platforms would make more sense from start. Eventually every telco would have hybrid multi-cloud-based architecture, but the way it evolves to that stage would decide the transformation results. Platforms aren’t Cloud and Cloud isn’t just platform but more than platforms. Let’s ensure we understand the fundamental difference and make right choices.

It brings forth another important question, how do enterprises ensure security of P5G as well as their existing enterprise network?

Trend-micro has conducted a detailed survey on ‘Expectations of P5G Network Security’ recently and published the findings. Not surprising at all, some of the challenges we already foresaw during our earlier discussions.

Although it’s known that cellular networks (4G/5G) are more secure (compared to Wi-Fi), many enterprises do have concerns regarding data transferred on 5G air interface (devices to cell tower), and attacks on devices connected to network. There are additional concerns about whether 5G network equipment can be compromised, esp. if you are deploying it in hybrid or public cloud setup. These concerns are quite valid ones.

With respect to data transfer on air interface, 5G offers very robust cryptographic encryption process, with the introduction of NEA (New Encryption Algorithm) and NIA (New Integration Algorithm). The details of which are beyond scope of this blog post but interested readers can refer to ITU Workshop for more details. Moreover, with SUPI being encrypted with public key in home network itself, subscriber identity is protected completely.

To address the above security concerns, many enterprises, are either partnering with specialized security partners with 5G domain expertise or relying on existing IT security partners to address those cocerns. In any situation, task at end requires specialized understanding of entire 5G security landscape and there’s no easy route to find possible answers.

Interestingly, the findings from TrendMicro survey shows that many enterprises are intend to connect their existing enterprise network with P5G network in some way. In fact, close of 70% enterprises are going to integrate networks, which brings forth an interesting question, on how to do enterprises ensure seamless security of traffic, integration of devices connectity and policies. Surely, with P5G, enterprises need to take a holistic view of their entire enterprise network security, including P5G networks.

Topic of 5G security does require discussion around open standards. With O-RAN on rise, and many enterprises relying on building cloud-native networks with open source modules, ensuring compliance with open standard is must for enterprises. Issue of vulnerabilities, esp. with adoption of open standards is another major concern.

While there’s no easy route to 5G security, many enhancements with 5G Security from standard perspective are going to help but they aren’t enough. Along with P5G, deployment of MEC (multi-access edge compute) based architecture, integrated with P5G is also on rise. MEC, although a boon to solve business problems in real time, brings many other complexities on-campus. esp. MEC is deployed in data plane path with UPF on N6 interface, many of these MEC apps could be part of Enterprises network deployed on Cloud or in Hybrid model. Securing traffic on N6 interface, as well as ensuring right policies applies to traffic flowing in/out of UPF (service provider’s network) is going to be important. Moreover, how do enterprises integrate their existing network and apps with these policies is interesting to see.

What’s way forward for enterprises adopting P5G and MEC?

TrendMicro recommends enterprises should perform pilot of security operations, integrating their existing enterprise network with P5G before going live with P5G applications. Moreover, in our view, many enterprises, adopting P5G/MEC should gear up to understand 5G security aspects, and build domain expertise early by upskilling their existing IT team or partnering with right partners or hiring such experts from outside. Implementing such pilots won’t be easy, and without skilled resources on-board, it will be an uphill task.

As telcos are upgrading their networks from 5G NSA (non-standalone) to SA (standalone), it’s imperative that they will also be looking to build more revenue driven services, in-line with 5G SA capabilities. According to some estimates, 5G B2B services could potentially bring around 40% of total telco revenue in coming years. Nevertheless, telcos as well other ecosystem players, including public cloud providers, are looking to grab the pie of that revenue.

Private 5G or P5G networks, deployed in either Standalone Non-public (SA-NPN) or Public integrated Non-public networks (PI-NPN). The choice depends on numerous factors, including business models, network operations and management, costs, and licenses (regulatory) among others. Many enterprises, large/medium size, have already partnered with P5G ecosystem partners, telcos, and edge providers to support in rolling out P5G networks across their enterprise locations. Some of them have deployed SA-NPN, others are evaluating PI-NPN, where telcos play pivotal role.

Many of these deployments have integrated MEC framework in their architecture, which could include AI/ML applications, Edge/MEC Platforms, 5G Core modules, either on-prem or hybrid way, targeting specific business problems, beyond connectivity services. It includes video analytics, AR/VR, gaming, and other types of applications, deployed at Edge. The entire solution includes P5G Radio, MEC and 5G Core components, providing end to end solutions to enterprises.

We came across a few interesting P5G and MEC use cases and listed them below for reference.

As Commonwealth Games 2022 are already underway in UK, BBC, the leading broadcaster is already using Public P5G Network to broadcast games lives. According to the article, BBC is using 5G SA non-public network to broadcast images from Birmingham.

In Hungary, Vodafone and Ericsson are working with Foxconn to drive operational efficiency use cases with AI/ML with 5G Network. The network, built by Ericsson P5G, where Foxconn team uses 5G non-fixed cars within the given area, where newly built PCs are tested before moving to packaging.

Next use case is from Education vertical, where Telefonica has built 5G SA Campus network for University of Munich. The university will use superfast on-campus connectivity to drive research around autonomous transport.

Vodafone, Germany has built an app for DFL Supercup, which will drive the in-stadium sports experience of 1000+ mobile users, powered by 5G and MEC. The app will provide users with interesting insights from live matches in real-time.

In the end, discussion of P5G won’t be complete without mentioning China, who has more than 3000+ 5G Enterprise Connectivity projects underway across. Termed as ‘5G Industrial Internet‘, it focuses on Smart Manufacturing Use Cases including automation, digitization, and intelligence.

It’s evident that the race is already on to grab pie of B2B digital services with launch of P5G, MEC services by telcos and other service providers. India, with recently concluded 5G Spectrum auctions, is also aggressively looking to rollout 5G and P5G services at select cities and many enterprises are keenly watching the space.

Enterprise Networks were always privately protected and managed behind IT firewalls and switches. Only a certain portion of the network made public supporting diverse set of use cases. Now a days, many enterprises adopted hybrid cloud approach to build and manage IT workloads, networks are evolving to the next level.

With the arrival of Cellular connectivity on campus, be it LTE or 5G, the scenario is going to change completely. Depending on the deployment options chosen, stand-alone and public integrated stand-alone, the complexity of managing these networks is going to be enormously complicated.

Now let’s take an example of it to understand it better. Let’s say an enterprise, a medium size, already with setup of devices, app and connectivity in place decides to evaluate P5G/PLTE network and already zeroed down to partners, has spectrum and use case is identified, which justifies the investment RoI. These steps themselves are a long-haul process and making choices is not going to be easy, without active participation of multiple industry stakeholders in the process. For sake of simplicity, let’s assume somehow the enterprise has navigated these steps (with so much of struggle), and ready to deploy a video analytics ‘Face Detection’ application on campus.

We are assuming that they have approached a Telco at this stage to help them out in building this on-campus network and here’s what the setup would look like on campus on a prominent level.

If you look at the above architecture, on prominent level, enterprise is deploying 5G/IoT enabled CCTV Cameras, 5G Radio Site (gNB), 5GC components (this could be optional), MEC Application stack (Video Analytics Face Detection App), all on campus. The architecture is more for representation purposes, and some modules components could be deployed with Telco/Public MEC locations depending on Use case requirements and depends on enterprise choice on setting up entire infra on campus. Eventually, the network will be monitored and operationalized by Telco OSS/NOC centers, and Enterprise IT teams will also be managing them as well.

The question comes, what will be the role of Enterprise IT teams in managing these Cellular Private Networks?

It could vary, depending on network deployment model, but in the end, if IT teams aren’t going to manage these Edge apps and networks, enterprises could face dilemma of operating these complex networks. Moreover, if they choose to be on their own (NPN), without Telcos, enterprise’s P5G partners need to help in managing and operating these networks end to end, including app life cycle management, without creating much fragmentation in existing setup.

How will Enterprises solve these challenges?

They could have multiple options though. First, they can outsource entire private network on BOOT(Build-Own-Operate-Transfer) model to partners (including Telco). Second, they could do it mix of responsibilities, partially owning design or operation aspects. Third, they could do it entirely on their own, with the ownership of Enterprise IT teams.

In our design case, the enterprise has chosen to partner with Telco to build this on-prem network, which simplifies certain aspects. Assuming Telco has application ecosystem (app marketplace), end to end app orchestration layer (network slice) built (with Cloud providers), and with integration with OSS/BSS and 5G/4G Core, deployment will happen on Edge platforms, managed by Telco NOC teams. From application perspective (Face Detection), enterprise IT teams may monitor dashboards created specific to Use Case (alerts/events) and may not have full control of app life cycle itself, despite the app being hosted on-campus. This could be a challenge for enterprises initially, but eventually, they will have to gear up to not only manage one application, but an entire suite of Edge applications deployed on-campus with Cellular connectivity.

Enterprise IT team will have multi-fold challenges to tackle here. If you remember our initial setup, without Cellular Networks, it’s quite straightforward for IT teams to manage them, partially because, enterprise app/devices are fully integrated as part of

With Edge/MEC apps, such as Face Detection, although deployed on-campus, is more of carrier grade app, orchestrated, managed, and tightly coupled with cellular ecosystem components. Moreover, the IoT devices, such as CCTVs, will discover these apps directly (DNS resolution), and need to be managed separately as well, bypassing enterprise auth mechanisms (SIM Authentication). Even these CCTVs will get IP addresses from packet core elements, making it difficult for IT teams to control all aspects of private network and MEC App deployments.

Enterprises, in the end, will have to figure out, how their existing investments in IT teams, should support managing, operating both regular Enterprise networks/devices as well P5G networks and Edge Apps together in long run. Moreover, over time, enterprises would want to scale, deploy many such Edge Apps, solving multiple use cases with 5G Networks, which make proposition quite challenging to comprehend. I can imagine, in future, an average enterprise deploying at least 5-10 different edge apps, solving different business problems with help of AI/ML, 5G and Cloud Native technologies.

This brings to an interesting question, on the future of enterprise networks altogether.

In future, we could clearly see enterprises managing not only typical enterprise grade apps (e.g., CRM, HR, Finance, Inventory etc.) but will have many more apps (Edge Apps), solving different business problems with AI/ML including Smart Surveillance, Worker Safety, Parking Management, Smart Buildings etc. Moreover, the enterprise network architecture will have to adapt to integrate with cellular private networks, including Radio, MEC, Core, and others. Depending on deployment models, there could be a larger role of Public MEC, OSS, BSS, and other cellular modules as well in enterprise networks. In addition, as enterprises have already adopted cloud to deploy some or all their workloads, it’s going to add complexity to existing mix of apps, platforms, and solutions. The future is a mix of hybrid workloads, deployed across multi-cloud, on-prem, coupled with edge apps, and cellular networks.

Are enterprises geared for the future of enterprise networks?

Maybe not. It’s a long haul but the journey has already begun. At this stage, I could see enterprises skeptical of adding complexity to their already complex setup, but eventually, if P5G ecosystem (including Telcos) manages to solve the challenges, adoption of the private network, and edge apps going to be easier, enabling enterprises to solve complex business problems with new possibilities.

Moreover, the challenge is everyone is thinking siloed here. Cellular operators or private network operators, thinking from Cellular perspective to design these networks, making it harder for Enterprises and IT teams to adopt these networks. Moreover, IT teams, traditionally skilled at managing Wi-Fi/Ethernet connectivity, network devices and apps, are going to find it difficult to manage Cellular on-prem networks, Edge apps and devices. They need to gear for the next wave right away. Enterprises who are adopting P5G/PLTE networks need to think holistically and create a sustainable handshake to make integration a success.

In many instances, private networks are an integration challenge altogether. How well it gels with existing enterprise ecosystem, and economics of partnerships, selection of use cases will decide of future of enterprise network. The role of enterprise IT teams is more critical than ever, helping enterprises navigate complex path forward.

(All views are personal and don’t represent any org, company, or vendor solutions.)