Age-Aware Edge Caching and Multicast Scheduling Using Deep Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
The temporal nature of data in Internet of Things (IoT) networks necessitates periodic updates of cached content at edge devices, while multicasting dynamic content can enhance network efficiency. This paper addresses the challenge of joint cache updating and multicast scheduling in a cache-enabled, queue-equipped small base station (SBS) with limited cache capacity, which accesses a macro base station (MBS) to download (update) uncached (cached) content and serves requests through multicasting. We formulate a two-stage optimization problem to minimize the average age of information (AAoI) per request, subject to constrained average queueing delay and access rate. The first stage employs the Lyapunov drift-plus-penalty method at the SBS to schedule multicasting and downloading (updating) uncached (cached) content. The second stage, implemented at the MBS, leverages deep reinforcement learning (DRL) to determine the content replacement policy. Simulation results show that the DRL-based cache replacement policy yields up to 50%, 59%, and 60% improvements in AAoI compared to the maximum age, least-recently-used, and least-frequently-used baseline policies, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it