Scheduling and Resource Allocation in LTE Uplink with a Delay Requirement
Bibliographic record
Abstract
In this paper, we investigate the problem of scheduling and resource allocation for LTE Single Cell Uplink systems which use Single Carrier Frequency Division Multiple Access (SC-FDMA). SC-FDMA (L-FDMA scheme) has particular scheduling requirements such as limited power consumption and contiguous resource block constraints. This last requirement has been seldom taken into account in the previous scheduling and resource allocation algorithms. In addition, there is also an end-to-end delay requirement concern, which has not yet been investigated in conjunction with block contiguity constraints, in the LTE Uplink scheduling and resource allocation literature.The end-to-end delay is a significant quality of service (QoS) matter; taking into account that it has a notable impact on the number of users that can be effectively served.We provide two new scheduling algorithms that, in addition to the channel contiguity constraint, take also into account the end-to-end delay constraint. Simulations are conducted on traffic instances with up to 144 users and results are compared with two very recently proposed algorithms. Results show that we increase by approximately a factor of 3, the user capacity of the system relative to systems that does not include the end-to-end delay constraint, without sacrificing the user fairness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".