Abstract

Valid Measurements Are Not a Valid Input: Qualifying RAN Telemetry Before AI-RAN Beamforming, Scheduling and RIC Decisions in 5G-Advanced and 6G

ABSTRACT

Part 02 of 15 — AI Safety and Operational Consistency Technical Disclosure Series

In one line: Every radio measurement can be individually valid while the combination is wrong for the calculation. Before an AI-driven RAN control calculation is computed or delivered, the observation set is qualified for that specific calculation and classified SUPPORTED, CONTRADICTED or UNRESOLVED, without ever authorizing radio actuation by itself.

AI-native radio access networks, Open RAN, RAN Intelligent Controller (RIC) xApps and rApps, self-organizing network (SON) functions and AI-RAN platforms combine telemetry from many sources to drive beamforming, scheduling, interference coordination, handover, load balancing and energy saving. Individually valid records can still be jointly wrong for a calculation when they come from different cell or beam identities or configuration generations, different clock domains or acquisition intervals, incompatible units or aggregation semantics, or a shared upstream sample counted as independent corroboration.

This technical disclosure presents a computer-implemented, calculation-first method and system for deciding whether a set of RAN observations is jointly applicable to one specific AI-driven control calculation. The method first identifies the control calculation and its mandatory observation roles, then tests candidate observation sets against calculation-specific predicates.

Resource identity: cell and beam identifiers are namespace-qualified and resolved against the mapping history valid for each acquisition interval, so reused or remapped identifiers are not falsely equated. Timing: positive temporal overlap is declared only when validity windows overlap for all admissible clock offsets; merely possible overlap is UNRESOLVED, and uncertainty terms are not double counted. Measurement semantics: conversions are bound to source and destination schema generations, preserving units, physical dimension, aggregation window and the counter-versus-rate distinction. Lineage: transitive provenance is reconstructed to detect shared upstream origin, and independence is enforced where the calculation requires it.

Each candidate set is classified SUPPORTED, CONTRADICTED or UNRESOLVED with predicate-level reason codes. Missing or undecidable evidence is never promoted to a positive result, and bounded-search exhaustion is kept distinct from proven infeasibility.

Example: a beamforming calculation would combine channel state information reported under a beam identifier that was remapped after a configuration change with a performance counter from the previous configuration generation, timestamped in a different clock domain whose overlap is only possible, not certain. The set is classified UNRESOLVED with identity and timing reason codes, and the calculation is not delivered on that input.

Material dependencies are revalidated between qualification and delivery, recomputing only the affected predicates. After controller failover or application relocation, the successor reconstructs source, clock, mapping and dependency evidence rather than inheriting a predecessor's positive flag. The consistency report is non-actuating and carries no bearer authority: any modification of a live radio resource must pass through a separate authority and protected execution boundary, and effect evidence is kept separate from admission.

The disclosure comprises Common Workflow A-CW-01 (three phases, 26 stages: calculation and observation qualification; joint candidate-set verification; computation, revalidation and delivery) and six embodiments: temporal consistency of delayed, reordered and incomplete observations; resource-state correspondence during reassignment and identifier reuse; control-specific preservation of measurement semantics across heterogeneous interfaces; radio-application relocation with observation-continuity reconstruction; joint consistency for beamforming, scheduling and coordinated multi-resource control; and controller-failover continuity with control-result revalidation. It includes a six-page mathematical formulation, five drawings, forty implementation checkpoints, a threat model and interoperability failure-injection tests. A Python reference implementation passed 56 of 56 synthetic tests, with evaluator latency measured in eight languages. These are synthetic evaluator timings, not radio-control latency, and do not establish production performance or live-network safety.

The mechanisms apply to 3GPP Release 18, 19 and 20 5G-Advanced and 6G radio access, including NR physical-layer measurements (TS 38.215), 5G performance measurements (TS 28.552) and self-organizing networks (TS 28.313); O-RAN near-real-time and non-real-time RIC architectures with E2, A1, O1 and R1 interfaces and E2SM-KPM telemetry; fronthaul and network timing based on IEEE 1588 PTP; AI-RAN and edge inference platforms; and non-terrestrial network integration. The architecture is vendor-neutral and independent of any equipment manufacturer, silicon platform, cloud provider, AI model or programming language.

The technical contribution is calculation-specific qualification of radio telemetry for identity, time, semantics and lineage before AI-driven control computation, with explicit uncertainty and strict separation between qualifying an input and being authorized to act on the result.

Related publications and applications: source disclosure "From Intent to Effect" (Zenodo record 23793543); Indian Patent Application No. 202631129178 (patent pending), from which this Group A content is drawn; PCT/IB2026/055615 (published as WO 2026/150382); and follow-on applications PCT/IB2026/055760, 055870, 056058, 056571, 056771, 056809, 056941, 057198, 057540 and 058236. Companion Internet-Draft: draft-das-ai-safety-radio-observation-consistency-00.

Author: Sangam Kumar Das, Independent Researcher, Balasore, Odisha, India. Publication date: 30 October 2026. Open to acquihire, strategic collaboration, licensing or joint development with organizations building RAN, Open RAN, AI-RAN or AI-agent infrastructure: [email protected], +91-9861363532.

KEYWORDS

Valid measurements are not a valid input; radio access network observation consistency; RAN telemetry qualification; calculation-first qualification; joint observation applicability; AI-native RAN; AI-RAN; AI-driven RAN control; 5G Advanced; 5.5G; 6G radio access; 3GPP Release 18; 3GPP Release 19; 3GPP Release 20; TS 38.215; TS 28.552; TS 28.313; NR; gNB; distributed unit; central unit; Open RAN; O-RAN; RAN intelligent controller; RIC; near-real-time RIC; non-real-time RIC; xApp; rApp; E2 interface; E2SM-KPM; A1 interface; O1 interface; R1 interface; SMO; SON; self-organizing networks; radio resource management; KPM; PM counters; performance measurements; channel state information; CSI; CSI reporting; beam measurement; beam management; beam identifier remapping; YANG-Push; measurement aggregation window; counter versus rate; unit and dimension preservation; schema adapter; schema generation; source-qualified observation; data lineage; transitive provenance; shared upstream origin; source independence; double counting of evidence; three-valued verdict; SUPPORTED CONTRADICTED UNRESOLVED; Kleene logic; predicate-level reason codes; bounded search exhaustion versus infeasibility; clock uncertainty; clock offset bound; clock domain; time synchronization; IEEE 1588 PTP; fronthaul timing; acquisition interval; event time versus arrival time; out-of-order telemetry; delayed telemetry; cell identity reuse; PCI reuse; configuration generation; source epoch after restart; TOCTOU; material state change; revalidation before delivery; controller failover; successor state reconstruction; xApp relocation; edge inference relocation; non-actuating verification; separation of verification and authorization; protected execution boundary; beamforming; massive MIMO; scheduling; interference coordination; coordinated multipoint; CoMP; handover optimization; mobility robustness; load balancing; energy saving; network slicing; non-terrestrial network; NTN; AI safety; execution finality; standard-essential technology; SEP; FRAND licensing

CPC CODES (author-suggested)

H04W 24/02; H04W 24/04; H04W 24/10; H04W 56/00; H04W 72/12; H04B 7/0617; H04B 7/0626; H04L 41/16; H04L 41/0816; H04L 41/0893; H04L 41/40; H04L 43/08; G06F 16/215; G06F 16/2365; G06N 5/04; G06N 20/00; G06F 11/14

IPC CODES (author-suggested)

H04W 24/02; H04W 24/04; H04W 24/10; H04W 56/00; H04W 72/12; H04B 7/06; H04L 41/16; H04L 41/08; H04L 43/08; G06F 16/215; G06F 16/23; G06N 5/04; G06N 20/00; G06F 11/14

These classification codes are author-suggested search classifications, not official classifications assigned by a patent office.

Filing history. The parent application, PCT/IB2026/055615, was filed on 4 June 2026 and published by WIPO on 16 July 2026 as WO 2026/150382. Its specification runs to 8,598 pages and sets out more than 300 distinct embodiments. It builds on an earlier application covering the Algorithmic Logic Fingerprint (ALF), PCT/IB2026/053385, filed on 7 April 2026 and published on 4 June 2026 as WO 2026/115520. The parent was followed by eight more PCT applications and multiple Indian patent applications, together comprising more than 10,000 pages of non-repetitive disclosure.

Official IETF Datatracker links for all 16 drafts:

  1. https://datatracker.ietf.org/doc/draft-das-multi-agent-authority-control/

  2. https://datatracker.ietf.org/doc/draft-das-agent-tool-integrity/

  3. https://datatracker.ietf.org/doc/draft-das-distributed-effect-recovery/

  4. https://datatracker.ietf.org/doc/draft-das-agent-credential-privacy/

  5. https://datatracker.ietf.org/doc/draft-das-cross-device-authority-revocation/

  6. https://datatracker.ietf.org/doc/draft-das-hardware-execution-finality/

  7. https://datatracker.ietf.org/doc/draft-das-autonomous-infrastructure-finality/

  8. https://datatracker.ietf.org/doc/draft-das-causal-effect-receipts/

  9. https://datatracker.ietf.org/doc/draft-das-interoperable-consequence-authority/

  10. https://datatracker.ietf.org/doc/draft-das-social-media-algorithmic-accountability/

  11. https://datatracker.ietf.org/doc/draft-das-execution-finality-enforcement/

  12. https://datatracker.ietf.org/doc/draft-das-agent-execution-finality/

  13. https://datatracker.ietf.org/doc/draft-das-drip-uas-act-finality/

  14. https://datatracker.ietf.org/doc/draft-das-execution-finality-deployment/

  15. https://datatracker.ietf.org/doc/draft-das-agentic-adaptive-authorization/

  16. https://datatracker.ietf.org/doc/draft-das-execution-finality-ai-interoperability/

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