The effect of noise, fading and interference on the performance of scheduling algorithms in a cross-layer downlink model
Bibliographic record
Abstract
A system model combining space time block coding and beamforming at the PHY layer along with various scheduling algorithms at the MAC layer is used to study the effect of noise, fading and interference on the performance of a system that simultaneously serves two users. System performance expressed in terms of capacity, is assessed for line of sight (LoS) and non-LoS (NLoS) environments, for a range of SNRs, and for different number of antenna elements that are used for combating interference via beamforming. As expected, system capacity is higher for higher SNRs and for direct LoS environments for all scheduling algorithms. The improvement in capacity is also achieved due to an increased number of antenna elements at the PHY layer that leads to narrower beam widths, and aid in minimizing the interference from other simultaneously served users. The scheduling algorithm proposed by D. Arora and P. Agathoklis (2005), that explicitly considers the angular spread between simultaneously served users for combating interference, performs consistently better than the greedy and the round-robin (RR) scheduling algorithms. The results suggest that jointly addressing the PHY and MAC layer issues is important for achieving maximal system performance.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".