Uplink Scheduler for SC-FDMA-Based Heterogeneous Traffic Networks With QoS Assurance and Guaranteed Resource Utilization
Bibliographic record
Abstract
Assuring quality-of-service (QoS) and guaranteeing effective utilization of resources in a network with heterogeneous traffic is difficult due to the conflicting requirements associated with the different types of traffic. Nevertheless, emerging high-bandwidth third/fourth-generation (3G/4G) technologies, such as Long-Term Evolution (LTE) and LTE-Advanced, are expected to meet the demands of new applications by providing high data rates while simultaneously satisfying each of the individual QoS criteria. The relevant standards recommend single-carrier frequency-division multiple access (SC-FDMA) as the preferred uplink transmission scheme. In this paper, we have formulated the uplink scheduling problem for SC-FDMA-based heterogeneous traffic systems considering both standard specific constraints and individual user QoS requirements. To capture the QoS requirements of different types of traffic, we adopt a utility function that is already used for the downlink operation of code-division multiple-access (CDMA)-based systems. We also consider an opportunity cost function that is based on granular resource utilization to increase the usefulness of our results in practical situations. We have used the dual decomposition method to solve the resulting problem. Given the high computational complexity of the optimal solution, we have also proposed a suboptimal algorithm that is less complex but which offers acceptable performance. In our evaluation of the performance of our proposed uplink scheduling scheme, we assume that the offered traffic is a combination of best effort traffic, traffic with specified delay bounds, and traffic with specified throughput requirements. An extensive set of simulation results demonstrate the efficacy and effectiveness of our scheme compared with existing solutions employed by LTE, which is a representative SC-FDMA-based system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".