{"paper":{"title":"A Persistence-Aware Framework for Age Violation Control in Wireless Status Update Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A consecutive age violation rate vector unifies persistence objectives in wireless status updates and is optimized by quantile regression deep Q-learning.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Chen Chen, Haoyuan Pan, Kun Chen, Shiyong Zhou, Tse-Tin Chan","submitted_at":"2026-05-13T04:57:12Z","abstract_excerpt":"Timely and reliable status updates are essential for emerging QoS-sensitive wireless applications. Common age of information (AoI)-based metrics, such as average AoI and age violation rate (AVR), characterize time-averaged freshness or violation frequency but do not explicitly capture the temporal persistence of consecutive age violations, which can be critical in safety-sensitive wireless applications. We develop a persistence-aware reliability framework based on the consecutive age violation rate (C-AVR) vector, whose components quantify AoI threshold violations over consecutive time windows"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A consecutive age violation rate vector unifies persistence objectives in wireless status updates and is optimized by quantile regression deep Q-learning.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"41e2aca3112825c349578610160c2bcab52e54082c53caeafc0331e8e994942b"},"source":{"id":"2605.13002","kind":"arxiv","version":1},"verdict":{"id":"daf5bf8a-c796-4c85-b574-4be9f4b8bba0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:39:50.205507Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"A consecutive age violation rate vector unifies persistence objectives in wireless status updates and is optimized by quantile regression deep Q-learning."},"references":{"count":37,"sample":[{"doi":"","year":2012,"title":"Real-time status: How often should one update?","work_id":"ea9261ec-5979-4ffc-a2da-dfcb5d3babc3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"On the role of age of information in the internet of things,","work_id":"b5cc5276-6d9a-4876-96e7-b1ecb3d170d9","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Y . Sun, I. Kadota, R. Talak, E. Modiano, and R. Srikant,Age of Information: A New Metric for Information Freshness. Morgan & Claypool, 2019","work_id":"2237d243-612a-4c34-b6f4-dea11a471820","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Minimizing the aoi in resource-constrained multi-source relaying systems: Dynamic and learning-based scheduling,","work_id":"546c735c-1905-4db4-9795-5100d072edaf","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Aequitas: A 5G scheduler for minimizing outdated information in IoT networks,","work_id":"7f744b2e-f713-4ebb-ab87-4d6405d1d178","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":37,"snapshot_sha256":"29513a5393c8271c1dd6331ce9c591c9d0a75e882b5fdf2c53d3c09e9a13e6eb","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}