The Effect of Channel Uncertainty on Max-Min Goodput
Bibliographic record
Abstract
In this paper, we consider the effect of channel uncertainty on the rates reliably delivered to the users; i.e., the goodput. After the base station (BS) designs a set of beamformers for a specific objective, the BS must select the operating or transmission data rate for each user. However, under channel uncertainty, higher transmission rates cause higher outage probability, and the delivered rate drops. Since lower rates are not desirable, one must balance between the transmission rate and outage. In this paper, we first explain how approximating the PDF of a quadratic form with a positive definite matrix can be used to obtain the outage probability for any set of beamfomers and transmission rate. Then we focus on the specific case of maximizing the minimum delivered rate, where we modify a robust beamforming approach to maximize the resulting goodput. We then derive iterative closed-form expressions for this case. The simulation results illustrate the efficacy of our analysis and the significant gains that can be obtained by optimizing the goodput metric.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".