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pith:G556ERTH

pith:2026:G556ERTHHZXTGBBRVC3JNUUH36

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Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO

(2) Department of IEOR, (3) Faculty of Computer, Alexander Shmatok (1), Alexey Frolov (1), Dmitry Artemasov (1), Engineering Sciences, India, Indian Institute of Technology Bombay, Innopolis, Innopolis University, IoT, Kirill Andreev (1), Manjesh K. Hanawal (2), Moscow, Nikola Zlatanov (3) ((1) Center for Next Generation Wireless, Russia, Russia), Skolkovo Institute of Science, Technology

A single recurrent transformer block trained once delivers 5 dB and 7.5 dB NMSE gains over prior methods for narrowband and wideband hybrid near-far field THz UM-MIMO channel estimation.

arxiv:2605.12578 v1 · 2026-05-12 · eess.SP · cs.IT · cs.LG · math.IT

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Claims

C1strongest claim

We demonstrate that a single transformer block equipped with state memory can be trained once and then iteratively applied for hybrid-field channel estimation, achieving performance gains of approximately 5 dB and 7.5 dB in NMSE over state-of-the-art solutions in narrowband and wideband scenarios.

C2weakest assumption

The simulated wireless channels used for training and evaluation accurately capture the physical near- and far-field propagation effects, scatterer distributions, and wideband behavior in real THz UM-MIMO deployments.

C3one line summary

A single recurrent transformer block trained once delivers 5 dB and 7.5 dB NMSE gains over prior methods for narrowband and wideband hybrid near-far field THz UM-MIMO channel estimation.

References

59 extracted · 59 resolved · 0 Pith anchors

[1] Recurrent transformer neural network-based hybrid far- and near-field THz channel estimation, 2024

[2] (2024) Framework and overall objectives of the future development of IMT for 2030 and beyond 2024

[3] Terahertz communications and sensing for 6g and beyond: A comprehensive review, 2024

[4] Channel modeling and capacity analysis for electromagnetic wireless nanonetworks in the terahertz band, 2011

[5] Hybrid beamforming for mm-wave massive mimo systems with partially connected rf architecture, 1947

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Receipt and verification

First computed	2026-05-18T03:10:01.569645Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

377be246673e6f330431a8b696d287dfafcc9d513fe9733a1a0de633d26b840b

Aliases

arxiv: 2605.12578 · arxiv_version: 2605.12578v1 · doi: 10.48550/arxiv.2605.12578 · pith_short_12: G556ERTHHZXT · pith_short_16: G556ERTHHZXTGBBR · pith_short_8: G556ERTH

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/G556ERTHHZXTGBBRVC3JNUUH36 \
  | 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: 377be246673e6f330431a8b696d287dfafcc9d513fe9733a1a0de633d26b840b

Canonical record JSON

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    "abstract_canon_sha256": "1396ed8cc9a9831ebefd2a515c2b4e8fb872ef7576284c4db1f4c5e577237c2e",
    "cross_cats_sorted": [
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      "math.IT"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-12T14:44:38Z",
    "title_canon_sha256": "06390053f54723061d7008898e8c17544f35a8289fd8c62191b401347781912c"
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