Cross Layer Scheduling Algorithms for Downlink Multi-Antenna CDMA Systems
Bibliographic record
Abstract
In today's wireless communication systems, the design of efficient packet scheduling algorithms at the MAC layer is proven to have significant impact on their overall performances. In the light of this fact, we propose and compare in this paper different scheduling techniques which aim at satisfying the users' requirements in terms of both rates and delays. The considered system is a downlink multi antenna code division multiple access (MIMO-CDMA) system which assumes both traffic arrival and users' mobility. First, the MIMO-CDMA system is modeled as a weighted graph. The weight of each vertex is then updated at each time slot according to a specified scheduling rule. Finally, we solve heuristically a graph coloring problem in order to find a near- optimal scheduling decision. We evaluate through simulations the performance of the proposed algorithms and show that a cross layer design taking the benefits of both MIMO and scheduling may be efficient to address the tradeoff between system capacity and users' quality of service requirements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".