Capacity analysis for a multiuser cross-layer downlink model in the presence of fading and interference
Bibliographic record
Abstract
A system model that employs beamforming at the physical (PHY) layer and different scheduling algorithms at the medium access control (MAC) layer is used in a downlink environment where two users are served simultaneously. The effect of the scheduling algorithms on the system performance is assessed in terms of total system capacity. The scheduling algorithms considered, choose users either randomly, or on the basis of their instantaneous signal-to-noise ratio (SNR) and/or their angular location around the base station. A semi-analytical framework for the capacity analysis is also presented. The results obtained from the numerical model are shown to be consistent with those based on the semi-analytical framework. It is shown that explicitly taking into consideration at the MAC layer the angular location of mobile users around the base station along with instantaneous SNR for the selection of users that are served simultaneously leads to an improved system capacity. This improvement is the result of reduced interference that simultaneously served users cause on each other. The results show that joint addressing of the PHY and MAC layer issues in an integrated cross-layer framework is important for achieving maximal system performance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".