Flow control in the presence of interference cancellation in wireless CDMA networks
Bibliographic record
Abstract
We consider the control of uplink packet flow subject to in-cell and out-of-cell interference limitations, in the presence of imperfect Interference Cancellation (IC). The aim is to combine a location-based packet flow control algorithm with multi-user detection for IC. The algorithm assigns packets to be transmitted to separate queues, one for each spatial zone within which packets generate roughly the same in-cell interference and impose equal interference on a neighboring base station. The objective is to maximize data throughput while ensuring fairness among users and limiting queuing and transmission delays. Throughput and fairness are two conflicting objectives that need to be optimized. We show that IC combined with location based scheduling achieves a better tradeoff between throughput and fairness even under stringent resource limitations. Compared to throughput maximization, simulations suggest that maximum fairness can be achieved with a loss in throughput of only 13%, whereas the loss is 65% when IC is not combined with scheduling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".