pith:LIDFNLUY
A Persistence-Aware Framework for Age Violation Control in Wireless Status Update Systems
A consecutive age violation rate vector unifies persistence objectives in wireless status updates and is optimized by quantile regression deep Q-learning.
arxiv:2605.13002 v1 · 2026-05-13 · cs.NI
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\pithnumber{LIDFNLUYEZNN53RMJUTSWZYFFE}
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Record completeness
Claims
Simulation results show that QR-D3QN consistently outperforms expectation-based baselines across a wide range of weighting schemes and system settings, with particularly significant gains under tail-sensitive persistence objectives.
That the simulated stochastic packet arrivals, channel unreliability, and cost constraints sufficiently represent the temporal correlation structure of real wireless deployments so that the learned policies transfer.
Proposes C-AVR framework for persistence-aware AoI reliability and optimizes it via QR-D3QN, outperforming expectation-based RL baselines in simulations especially under tail-sensitive weighting.
References
Receipt and verification
| First computed | 2026-05-18T03:09:00.389128Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5a0656ae98265adeee2c4d272b67052914002cf75261215031eab8384e9b10dc
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LIDFNLUYEZNN53RMJUTSWZYFFE \
| 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: 5a0656ae98265adeee2c4d272b67052914002cf75261215031eab8384e9b10dc
Canonical record JSON
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