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pith:U7HZHAJ5

pith:2026:U7HZHAJ5DM5PZU4OQP4TF6VEW4

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Deep Mixture of Experts Network for Resource Optimization in Aerial-Terrestrial CF-mMIMO Systems under URLLC

Chong Huang, Donggen Li, Dusit Niyato, Jingfu Li, Pei Xiao, Wenjiang Feng, Zhu Han

A deep mixture of experts network with channel prediction optimizes power allocation in aerial-terrestrial cell-free massive MIMO systems to support URLLC.

arxiv:2605.15135 v1 · 2026-05-14 · eess.SP · cs.IT · math.IT

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Claims

C1strongest claim

The proposed framework better captures heterogeneous UE requirements and improves communication performance under URLLC constraints. Numerical results demonstrate the effectiveness of the proposed method.

C2weakest assumption

That the CP-Net can accurately predict CSI for high-mobility UEs in real environments and that the MoE-Net's learned gating produces allocations that generalize beyond the simulated training scenarios without violating URLLC constraints.

C3one line summary

A Transformer-based CP-Net predicts aged CSI and a MoE-Net with weighted gating optimizes power allocation in aerial-terrestrial CF-mMIMO systems to meet URLLC requirements.

References

42 extracted · 42 resolved · 0 Pith anchors

[1] Perform ance analysis of cell-free massive MIMO-URLLC systems over corr elated Ri- cian fading channels with phase shifts, 2024

[2] Wireless acce ss in ultra- reliable low-latency communication (URLLC), 2019

[3] Rate-s plitting multiple access in cell-free massive MIMO-URLLC systems: A chievable rate analysis and optimization, 2024

[4] The potential of low-alt itude airspace: The future of urban air transportation, 2024

[5] Cell-free mMIMO systems in short packet transmission regime: Pilot and powe r allocation, 2024

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Receipt and verification

First computed	2026-05-17T21:40:25.610197Z
Last reissued	2026-05-17T21:57:18.926861Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

a7cf93813d1b3afcd38e83f932faa4b73d1cb5149d4881f93cdc3bbdde0f5e3a

Aliases

arxiv: 2605.15135 · arxiv_version: 2605.15135v1 · pith_short_12: U7HZHAJ5DM5P · pith_short_16: U7HZHAJ5DM5PZU4O · pith_short_8: U7HZHAJ5

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/U7HZHAJ5DM5PZU4OQP4TF6VEW4 \
  | 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: a7cf93813d1b3afcd38e83f932faa4b73d1cb5149d4881f93cdc3bbdde0f5e3a

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-14T17:42:17Z",
    "title_canon_sha256": "1d92d773de566f5c56c5bc57d0f98db6063e397c7d0f1dd4fb630f8bea50a122"
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