Always in sight: How DPI powers visibility in Cloud RAN

Cloud RAN represents a virtualized and disaggregated architecture spanning on-site deployments, telcos’ edge and core data centers, and hyperscalers. It gives telcos immediate access to elastic computing and networking capabilities, leveraging cloud-native elements such as microservices, containers, and service meshes. Beyond this, Cloud RAN provides a significant leap in network programmability, enabling software-driven approaches to manage, orchestrate, and automate RAN operations. 

Growing traffic analytics requirements in Cloud RAN 

The adoption of cloud-based RAN fundamentally changes traffic analytics requirements across the network. Traditionally, RAN functionalities such as scheduling and power control rely on real-time traffic inputs to make decisions on resource allocation, latency optimization, and signal quality adjustments. Without these insights, quality-of-service (QoS) guarantees can be compromised, putting service-level agreements (SLAs) at risk. 

The migration of RAN workloads to the cloud amplifies these analytics demands: 

• Distributed visibility: Traditional baseband units (BBUs) are dismantled, so analytics once centralized must now be accessible across distributed cloud domains.
• Integration with management platforms: Traffic insights must feed NFV Management and Orchestration (NFV-MANO), policy control, slice orchestration, and OSS platforms to support consistent, policy-aligned decisions.
• Expanded monitoring: Cloud RAN health checks now cover multiple VNF]s/CNFs, orchestration platforms, links, APIs, servers, containers, container orchestrators, hypervisors, and VMs. Gaps in any component can cascade and affect overall network performance.
• Threat detection: Cloud RAN increases attack surfaces, from container vulnerabilities and misconfigurations to lateral threat movement across tenants and sites, both on-premises and at the edge.

Visibility in the cloud with DPI 

Deep packet inspection (DPI) is integral to mobile network architectures. In Cloud RAN, next-gen DPI engines such as R&S^®PACE 2 and R&S^®vPACE by ipoque provide advanced traffic filtering and analytics to enhance RAN visibility. 

Key advantages include: 

• Cloud-optimized design: R&S^®vPACE is VPP-based, supporting CNFs, VNF]s, and 5G UPFs in demanding cloud environments.
• Flexible deployment: The DPI engines can be deployed at cell sites, in edge clouds alongside vDUs, edge gateways, and UPFs, or centrally with vCUs and service gateways.
• Comprehensive insights: Traffic mirrored from vTAPs, SDN switches, and container networks can be filtered and analyzed, providing long-term, detailed analytics. 

DPI in the RAN functional layer

vCUs and vDUs benefit from real-time, fine-grained traffic analytics: 

• On-site: DPI supports MAC scheduling, power control, admission control, and interference management.
• Edge cloud: Application, session, and QoS data enable PRB allocation, HARQ decision logic, load balancing, and access control enforcement.
• Core network: Application classification (video streaming, conferencing, cloud gaming) and traffic metadata (throughput, bitrate, latency) inform QoS flow mapping, buffer optimization, and handover management. 

Application-level awareness is critical for policy control and slice orchestration, ensuring latency-sensitive applications like AR/VR, autonomous vehicles, or industrial automation are correctly routed to MEC or edge cloud nodes. 

DPI in the RAN control and management layer 

DPI insights empower orchestration and management platforms: 

• Resource optimization: NFV-MANO uses traffic volumes and application patterns to scale vCUs and vDUs, maintain SLA compliance, and support closed-loop automation.
• Encrypted traffic intelligence: Enables visibility into encrypted, obfuscated, or anonymized flows for accurate, application-based policy enforcement.
• Operational efficiency: RAN O&M and OSS platforms gain detailed QoE metrics (throughput, latency, retransmission rates) for monitoring, troubleshooting, and predictive maintenance.
• Security enforcement: Early detection of malicious or anomalous traffic supports microsegmentation, container isolation, dynamic firewall rules, and automated mitigation measures.
• Performance assurance: Ensures VNF]s/CNFs and underlying infrastructure are optimally resourced, avoiding bottlenecks and performance degradation. 

Bringing openness and AI to Cloud RAN 

DPI data also fuels the intelligence layer in Open RAN architectures: 

• AI integration: High-quality DPI analytics serve as training data for AI engines, enabling predictive RAN operations and proactive management.
• Network intelligence: Supports Near-RT RIC, Non-RT RIC, and xApps/rApps in multi-vendor deployments, aligning policies and preventing conflicts.
• Optimization use cases: DPI drives energy efficiency, QoS/QoE improvements, and AI-powered automation within the RAN. 

Fueling telcos’ cloud ambition 

Emerging trends such as AI-native telcos, agentic AI for RAN management, and evolving network services are shaping Cloud RAN toward fully data-driven architectures. DPI-powered analytics enable an intelligent, adaptive cloud environment where dynamic scaling ensures the RAN remains highly responsive to traffic and AI demands. This approach underpins the creation of high-performance, future-ready RAN networks and supports telcos’ broader cloud strategies. 

For more insights on optimizing Cloud RAN with DPI, contact ipoque to discuss your network’s visibility and analytics needs.