Scheduling and resource allocation for multiclass services in LTE uplink systems
Why this work is in the frame
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Bibliographic record
Abstract
We propose two scheduling and resource allocation schemes that deal with Quality of Service (QoS) requirements in Uplink Long Term Evolution (LTE) systems. QoS for a multiclass system has been seldom taken into account in previous resource allocation algorithms for LTE uplink. In one of the new algorithms, we investigate the possibility of assigning more than one resource block and its consequences on satisfying stringent QoS requirements in the context of heavy traffic, either in terms of end-to-end delays or of minimum rates. System capacity and the number of effectively served requests are used as performance metrics. Numerical results show that it is possible to manage a multiclass scheme while satisfying the QoS constraints of all requests. Allowing the assignment of more than one resource block per request did not appear to be a meaningful advantage. Indeed, it is only useful when there is a heavy traffic, and some of the requests have stringent QoS requirements. But then, satisfying those requests can only be done at the expense of reducing the overall system capacity and of limiting the number of users who can be served.
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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 | 0.000 | 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 it