Pith Number

pith:XVXGKB43

pith:2026:XVXGKB43E2D7HOJVHLCQUNBI5P

not attested not anchored not stored refs resolved

SiFo: Wireless Foundation Model for Low-Overhead Site-Specific CSI Feedback

Cheng-Jie Zhao, Yuanwei Liu, Zhaolin Wang, Zongyao Zhao

SiFo pretrains a CSI feedback model across sites and adapts it to new deployments by matching users to calibration samples via RSRP measurements without parameter updates.

arxiv:2605.16141 v1 · 2026-05-15 · eess.SP

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\pithnumber{XVXGKB43E2D7HOJVHLCQUNBI5P}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

SiFo achieves higher CSI-capture efficiency than separately trained site-specific learning baselines under the same target-site labeled budget, approaches the high-overhead 3GPP NR Type-II feedback reference using only RSRP measurements collected during online SSB probing, and converts the high CSI-capture efficiency and low overhead into effective spectral efficiency improvement under limited target-site data.

C2weakest assumption

The assumption that RSRP fingerprints collected during standard SSB probing are sufficiently discriminative to match a served user to nearby calibration samples whose stored full-CSI subspace labels provide accurate site-specific guidance (abstract, paragraph describing online operation and calibration memory).

C3one line summary

SiFo pretrains a CSI feedback model on source sites and uses RSRP-based user matching to calibration memory for site-specific subspace guidance at target sites without parameter updates.

References

33 extracted · 33 resolved · 1 Pith anchors

[1] On the Opportunities and Risks of Foundation Models 2021 · arXiv:2108.07258

[2] WirelessLLM: Empowering large language models towards wireless intelligence, 2024

[3] WiFo: Wireless foundation model for channel prediction, 2025

[4] Large wireless model (LWM): A foundation model for wireless channels 2024

[5] WirelessGPT: A generative pre-trained multi-task learning framework for wireless communication and sensing, 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:54.766684Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bd6e65079b2687f3b9353ac50a3428ebcf085a1b6b12a1b9bd54637749ec4395

Aliases

arxiv: 2605.16141 · arxiv_version: 2605.16141v1 · doi: 10.48550/arxiv.2605.16141 · pith_short_12: XVXGKB43E2D7 · pith_short_16: XVXGKB43E2D7HOJV · pith_short_8: XVXGKB43

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XVXGKB43E2D7HOJVHLCQUNBI5P \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: bd6e65079b2687f3b9353ac50a3428ebcf085a1b6b12a1b9bd54637749ec4395

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "453528447cc932fb554c395c19a0b669c272b07b93b48f282a438ff38f56082e",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-15T16:23:10Z",
    "title_canon_sha256": "aefec8aca31e308de4aa842ae3e0d24730b6d7c4d8b8e28549ba0116c5ac5b4a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16141",
    "kind": "arxiv",
    "version": 1
  }
}