Adaptive scheduling for MIMO wireless networks: cross-layer approach and application to HSDPA
Bibliographic record
Abstract
In this paper, we consider the scheduling problem in multiple-input multiple-output (MIMO) wireless networks. The main important characteristic of an optimal scheduler is to maximize throughput while servicing users in a fair manner. Herein, we formulate MIMO scheduling as a generalized assignment problem (GAP) and propose a general solution for the GAP, namely, a cross-layer MIMO scheduler (CMS), which uses a novel adaptive proportional fairness (APF) mapping approach in conjunction with a new fast transmit antenna selection (FTAS) technique, to determine the set of users to transmit to and the antenna over which the data associated to each user should be transmitted. The proposed scheduler is applied for packet transmission in high-speed downlink packet access (HSDPA), taking advantage of the use of adaptive modulation and coding while coping with the constraints on the maximum number of simultaneous codes a user equipment can support, the limited uplink signalling, and the absence of fast power control. Numerical results show that the proposed CMS provides up to 70% increase in total throughput compared to other scheduling schemes
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".