Scheduling Deadline-Constrained Traffic over Hybrid Channels
Bibliographic record
Abstract
In this paper, we address the problem of scheduling real-time traffic over hybrid channels with throughput constraints in a single-hop downlink wireless network. Unlike previous work in this area which primarily focused on fading channels, we consider a hybrid channel model where channels have distinct characteristics. This model allows the scheduler to use both a fast unreliable channel and a slower, yet reliable one. The fast unreliable channel is modeled as an ON-OFF channel, while the slow reliable channel is deterministic. We explore scheduling deadline-constrained traffic under two distinct scenarios: scheduling real-time traffic with and without prior knowledge about the state of the fast unreliable channels. Employing a hybrid channel model adds complexity to the scheduling process, requiring the scheduler to make decisions about both user selection and channel allocation simultaneously. Thus, for each scenario, we propose two scheduling algorithms, User-Selection-First (USF) algorithm and Channel-Selection-First (CSF) algorithm, based on the characteristics of the channels. Our simulation results indicate that the USF algorithm effectively meets the timely throughput requirements in both scenarios, and the CSF algorithm achieves this in the scenario where channel state information is available for unreliable channels. In both scenarios, the CSF and USF algorithms outperform the case where packets are scheduled over unreliable channels only, delivering higher timely throughput.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".