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pith:SQTQCOEG

pith:2026:SQTQCOEGJRHAEJYCTTAEASKISI
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Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO

Maryam Sabbaghian, Mehrdad Momen-Tayefeh, Mehrshad Momen-Tayefeh

A multi-block attention network recovers channel estimates from 87 percent fewer pilots in IRS-assisted mmWave MIMO systems.

arxiv:2605.15032 v1 · 2026-05-14 · eess.SP · cs.LG

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Claims

C1strongest claim

The MBA method reduces pilot overhead by up to 87% compared to the LS estimator and achieves approximately 51% lower NMSE at 10 dB SNR than leading methods.

C2weakest assumption

That the simulated propagation environments and noise models accurately represent real-world mmWave channels, and that the neural network trained on synthetic data generalizes to practical deployments.

C3one line summary

A multi-block attention neural network reduces pilot overhead by 87% and NMSE by 51% at 10 dB SNR for cascaded channel estimation in IRS-assisted mmWave MIMO-OFDM systems.

References

44 extracted · 44 resolved · 2 Pith anchors

[1] Intellige nt reflecting surface-aided wireless communications: A tutorial, 2021
[2] Toward smart wireless communications via intellig ent reflecting surfaces: A contemporary survey, 2020
[3] Reconfigur able in- telligent surfaces vs. relaying: Differences, similariti es, and performance comparison, 2020
[4] Intelligent reflecting surface enhan ced wireless network via joint active and passive beamforming, 2019
[5] Reconfigurable intelligent surfaces for wireless communica- tions: Principles, challenges, and opportunities, 2020

Formal links

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Receipt and verification
First computed 2026-05-17T23:38:54.574179Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

94270138864c4e0227029cc04049489214f5d506359e72b76604a00afdfa1f71

Aliases

arxiv: 2605.15032 · arxiv_version: 2605.15032v1 · doi: 10.48550/arxiv.2605.15032 · pith_short_12: SQTQCOEGJRHA · pith_short_16: SQTQCOEGJRHAEJYC · pith_short_8: SQTQCOEG
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SQTQCOEGJRHAEJYCTTAEASKISI \
  | 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: 94270138864c4e0227029cc04049489214f5d506359e72b76604a00afdfa1f71
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-14T16:27:38Z",
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