Scheduling with base station diversity and fairness analysis for the downlink of CDMA cellular networks
Bibliographic record
Abstract
Abstract Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. In this paper, we propose two packet scheduling schemes exploiting base station selection diversity in the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous channel state information (CSI) to select the best user from the best serving base station at each time slot. This technique increases the system throughput by increasing multiuser diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. In addition, we investigate fairness issue of wireless scheduling schemes. Due to different characteristics of wireless scheduling schemes, the existing fairness indexes may result in misleading comparison among different schemes. We propose a new fairness index to compare the overall satisfaction of the network users for different scheduling schemes. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".