Pith Number

pith:DUUODIEZ

pith:2026:DUUODIEZCIUEGTXOCY3O3FZQAD

not attested not anchored not stored refs resolved

ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture

Nanqing Jiang, Tao Guo, Xiaoyu Zhao, Yinfei Xu, Zhangyao Song

ChannelKAN uses a hybrid CNN-KAN architecture to predict channel state information more accurately than RNN, LSTM, GRU, CNN or Transformer models in high-mobility wireless systems.

arxiv:2605.12553 v1 · 2026-05-11 · eess.SP · cs.AI

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{DUUODIEZCIUEGTXOCY3O3FZQAD}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Experiments on 3GPP-compliant QuaDRiGa datasets demonstrate that ChannelKAN outperforms RNN, LSTM, GRU, CNN, and Transformer baselines in normalized mean square error (NMSE), spectral efficiency (SE), and bit error rate (BER) across various velocities and signal-to-noise ratios.

C2weakest assumption

That performance measured on QuaDRiGa ray-tracing simulations will translate to real-world measured channels without retraining or domain adaptation.

C3one line summary

A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.

References

21 extracted · 21 resolved · 0 Pith anchors

[1] Neural network-based fading channel pre- diction: A comprehensive overview, 2019

[2] End-to-end deep learning for tdd mimo systems in the 6g upper midbands, 2025

[3] Addressing the curse of mobility in massive mimo with prony-based angular-delay domain chan- nel predictions, 2020

[4] Massive mimo channel prediction: Kalman filtering vs. machine learning, 2021

[5] Model enhanced learning based detectors (me-lead) for wideband multi-user 1-bit mmwave com- munications, 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.097730Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f

Aliases

arxiv: 2605.12553 · arxiv_version: 2605.12553v1 · doi: 10.48550/arxiv.2605.12553 · pith_short_12: DUUODIEZCIUE · pith_short_16: DUUODIEZCIUEGTXO · pith_short_8: DUUODIEZ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DUUODIEZCIUEGTXOCY3O3FZQAD \
  | 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: 1d28e1a0991228434eee1636ed973000fd0d5aeeb82fd236a129edd7f753c69f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e6b2d9a67c83a62e245be874c87f9cf5a19a5e8901a43ed9dd2d2e3655af8e68",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-11T07:58:51Z",
    "title_canon_sha256": "91f834b227a7d9d06583b81c501031b01223c077a03749b51ca3b1382e860488"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12553",
    "kind": "arxiv",
    "version": 1
  }
}