Pith Number

pith:JZXSMH5S

pith:2026:JZXSMH5SMQSHSN3WQIHAGLKFO5

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Leveraging Deep Reinforcement Learning for Clustered Cell-Free Networking Over User Mobility

Antonio P\'erez Yuste, Bo Qian, Junyuan Wang, Ouyang Zhou, Yusheng Ji

Deep reinforcement learning partitions cell-free networks into clusters using only one channel estimate per access point.

arxiv:2605.17266 v1 · 2026-05-17 · eess.SP

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4 Citations open

5 Replications open

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Claims

C1strongest claim

The proposed DDPG-C²F framework can be adapted in various application scenarios with different objectives and constraints, outperforms existing baselines in all scenarios, reduces the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

C2weakest assumption

That a single channel estimate per access point provides sufficient state information for the neural network to produce effective clustering decisions that generalize across real-world mobility patterns and varying network sizes.

C3one line summary

A DRL-based framework for clustered cell-free networking reduces channel estimation overhead to a single measurement per AP and adapts to user mobility, outperforming prior clustering methods in simulations across multiple objectives.

References

41 extracted · 41 resolved · 0 Pith anchors

[1] A deep reinforcement learning framework for clustered cell-free networking over user mobility, 2025

[2] 5G-advanced toward 6G: Past, present, and future, 2023

[3] Asymptotic rate analysis of downlink multi-user systems with co-located and distributed antennas, 2015

[4] Network MIMO with linear zero- forcing beamforming: Large system analysis, impact of channel esti- mation, and reduced-complexity scheduling, 2012

[5] User-centric C- RAN architecture for ultra-dense 6G networks: Challenges and method- ologies, 2018

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:48.751290Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4e6f261fb26424793776820e032d4577614c369306dafde89bf4e83ec51cdb0a

Aliases

arxiv: 2605.17266 · arxiv_version: 2605.17266v1 · doi: 10.48550/arxiv.2605.17266 · pith_short_12: JZXSMH5SMQSH · pith_short_16: JZXSMH5SMQSHSN3W · pith_short_8: JZXSMH5S

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JZXSMH5SMQSHSN3WQIHAGLKFO5 \
  | 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: 4e6f261fb26424793776820e032d4577614c369306dafde89bf4e83ec51cdb0a

Canonical record JSON

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    "abstract_canon_sha256": "856da6cd4cd545b6eaf50daad39ed4b3403f620d562c8fa49ba8d50a3494fa6c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-17T05:21:15Z",
    "title_canon_sha256": "34a7fecd2247fd60c8301c37ca6d026de8eba447a1291503557b053ca08564ea"
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