# rApp manifest — AI-for-RAN KPI forecasting rApp
#
# Declares this rApp's identity, the R1 data subscriptions it consumes from
# the Non-RT RIC, the A1 policy types it produces, and the ML artifacts it
# trains.  Shaped after O-RAN Alliance WG2 Non-RT-RIC-ARCH conventions for
# rApp manifests — the structure is real, the wire-protocol integration is
# not exercised (see docs/AI_RAN_INTEGRATION.md for the explicit boundary).

rApp:
  name: kpi-forecasting-rapp
  vendor: obiedeh
  version: 0.3.0
  description: |
    AI-for-RAN KPI forecasting rApp. Consumes per-cell KPM measurements
    over R1, forecasts cell-level KPIs hours ahead via a three-model
    head-to-head comparison (Ridge / GradientBoosting / MLP), and emits
    A1 policy candidates for traffic steering ahead of predicted
    congestion / backhaul-saturation / outage events.

  service_metadata:
    deployment_target: non-rt-ric
    timescale: minutes-to-hours
    expected_invocation: scheduled (e.g. every 15 minutes) + on-demand

  interfaces:
    # R1 data subscriptions — the rApp's input plane. Maps to KPM Service
    # Model measurements (3GPP TS 28.552 / O-RAN E2SM-KPM v02.00.05).
    data_subscriptions:
      - service: R1-Data
        consumer_role: ml-consumer
        kpm_service_model: ORAN.E2SM.KPM.v2
        schema_ref: schemas/kpm_input_v1.json
        reporting_period_ms: 60000
        granularity: per-cell
        measurements:
          - PRB.Util.DL.PerCell
          - PRB.Util.UL.PerCell
          - Thrp.DL.PerCell.Mbps
          - Thrp.UL.PerCell.Mbps
          - RRC.ConnReq.Att.Sum
          - DRB.RrcConnEstabAtt.Tot
          - Latency.RTT.PerUE.ms
          - SINR.DL.PerCell.dB
        # The repo also forecasts edge-AI KPIs (GPU / memory utilization)
        # on synthetic data — these are not standard KPM measurements but
        # show the same forecasting pipeline pattern.
        optional_extension_measurements:
          - EdgeAI.GpuUtil.PerCell.Pct
          - EdgeAI.MemoryUtil.PerCell.Pct

    # A1 policy outputs — the rApp's output plane.
    policy_outputs:
      - service: A1-Policy
        provider_role: policy-producer
        schema_ref: schemas/a1_policy_v1.json
        policy_types:
          - type_id: traffic-steering-v1
            description: |
              When forecast PRB DL utilization on a cell exceeds the
              configured threshold within the validity window, recommend
              offloading workload from that cell to neighboring cells
              with available headroom.  The Near-RT RIC enforces the
              policy via E2 RIC Control messages — this rApp produces
              the candidate, it does not enforce.

  ml_artifacts:
    - artifact_id: ridge-linear-baseline
      framework: scikit-learn (custom RidgeForecastRegressor on NumPy)
      type: time-series-regression
      latest_metrics: reports/forecast_examples/latest/metrics.json
      training_recipe: configs/sample_config.yaml
    - artifact_id: gradient-boosting-baseline
      framework: scikit-learn
      type: time-series-regression
      latest_metrics: reports/model_comparison/gradient_boosting/metrics.json
    - artifact_id: mlp-baseline
      framework: scikit-learn
      type: time-series-regression
      latest_metrics: reports/model_comparison/mlp/metrics.json

  # Honest boundary — what's pattern, what's deployment.
  boundary:
    pattern_only: true
    exercised:
      - KPM input parsing per schemas/kpm_input_v1.json
      - three-model forecasting pipeline with measured side-by-side metrics
      - A1 policy candidate generation per schemas/a1_policy_v1.json
      - end-to-end R1 → forecast → A1 simulation (scripts/simulate_r1_dataflow.py)
      - deterministic reproduction via `make verify`
    not_exercised:
      - live R1 data plane (wire protocol — KPM Service Model over E2/O1)
      - live A1 policy plane (wire protocol — A1-P/A1-EI)
      - O-RAN SMO integration
      - rApp lifecycle (Helm packaging, R1 service registration, dms_cli)
      - integration with FlexRIC, OSC RIC, Nokia MantaRay, or any other
        production Non-RT RIC
    rationale: |
      This rApp is the engineering pattern, not a deployable artifact.
      Use it as the reference for "what a real Non-RT RIC KPI-forecasting
      rApp looks like, end-to-end, with measured ML evidence." Wire-protocol
      integration is documented in docs/AI_RAN_INTEGRATION.md.