Efficient scheduling for the downlink of CDMA cellular networks using base station selection diversity
Bibliographic record
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. Exploiting base station selection diversity, in this paper, we propose two scheduling schemes for the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous Channel State Information to transmit to the best user from the best serving base station in each time slot. This technique increases the system throughput by increasing multi-user 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. We also investigate the issue of fairness analysis of wireless scheduling schemes. Due to the unique characteristics of wireless scheduling schemes, the existing fairness indexes fail to provide a proper comparison among different scheduling schemes. We propose a new fairness index to compare the overall satisfaction of the network users among different wireless scheduling schemes. This approach complies with the definition of max-min fairness which is a widely accepted notion of fairness for data communication networks.
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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 | 0.004 |
| 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 | 0.001 | 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".