5G AI RAN use cases smart city infrastructure
Use Cases

ISAC as a priced, prioritized, intent-driven service.

Every ISAC use case needs operator-grade manageability before it can be sold as a service. InfraSense provides the missing management story — sensing as a declared intent with resource constraints, priority, and closed-loop convergence.

Venue Analytics
ISAC
Venue Analytics

Dense-venue localization and coverage intents with arbitration under load

A sensing intent declares a localization accuracy target (e.g. 0.1m) and a resource ceiling for a geographic scope. The Near-RT RIC derives concrete sensing configuration with caps applied; a closed loop retunes toward the accuracy and coverage targets. The field-shaped pilot scenario in the InfraSense roadmap targets exactly this: dense-venue localization and coverage intents with arbitration under load.

0.1m
Critical tracking accuracy (demonstrator example)
90%
Coverage intent target (demonstrator example)
ISACVenue AnalyticsA1 SensingIntent
Public Safety
Public Safety
Public Safety

Sensing intents for public safety — prioritized and resource-constrained

Public safety sensing intents are declared as critical priority — protected in full by the SMO Global Agent when aggregate commitments over-commit the network. Lower-priority consumers are trimmed to their declared floors before any critical sensing intent is reduced.

Critical
Priority class — protected in full by SMO Global Agent
Explicit
Refusal when no compliant placement exists
Public SafetySMO Global AgentPriority Arbitration
V2X Perception
V2X
V2X Perception

Vehicle-to-everything sensing over the 5G/6G RAN

V2X perception requires real-time inference with hard latency bounds — a natural fit for the RT latency class (<10ms), which admits only DU-adjacent / RT-RIC-adjacent infrastructure. The inference layer enforces tier placement and refuses requests that cannot meet the bound rather than silently degrading.

<10ms
RT latency class — DU-adjacent / RT-RIC only
Enforced
Tier placement — no silent misrouting
V2XRT Latency ClassDedicated Inference
Industrial Digital Twins
Industrial IoT
Industrial Digital Twins

Sensing data feeding continuously calibrated digital twins

High-rate channel, reflection, and mobility data produced by ISAC must be processed close to where it is produced. InfraSense places inference at the appropriate tier and feeds the output into the AODT digital twin — keeping the simulation calibrated against live network conditions for what-if planning and xApp validation.

Near-RT
Latency class — 10ms–1s, Near-RT RIC tier
Live
Continuous AODT calibration from real sensing data
Industrial IoTDigital TwinISAC
AI-for-RAN Control
AI-for-RAN
AI-for-RAN Control

Beam management, interference mitigation, and integrated sensing

ML models that make or inform RAN control decisions carry hard deadlines — near-real-time loops of 10ms–1s and real-time loops below 10ms. InfraSense classifies each request and places it on the correct tier, selecting among model variants (fast / balanced / accurate) to meet accuracy within the latency bound.

<10ms
Real-time RAN control loop bound
10ms–1s
Near-real-time RAN control loop bound
AI-for-RANBeam ManagementInterference Mitigation
Multi-Operator / Multi-Vendor
O-RAN
Multi-Operator / Multi-Vendor

Standards-aligned deployment across heterogeneous RAN estates

InfraSense attaches to A1, E2, E3, O1, and O2 as defined by O-RAN — deployment means adding a policy type, an orchestrator, and an SMO service, not replacing the stack. The roadmap includes exercising the inference layer and A1 policy flow against at least one additional non-proprietary or partner RIC implementation.

A1/E2/E3/O1/O2
O-RAN interfaces — no stack replacement
M5–M8
Interoperability milestone — cross-vendor RIC demo
O-RANMulti-VendorInteroperability

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