Outage probability of MISO broadcast systems with noisy channel side information
Bibliographic record
Abstract
Transmitter precoding strategies are necessary to achieve the capacity promised in broadcast multiple input single output (MISO) systems. However, these schemes generally require perfect channel information at the transmitter. In this paper, we investigate the impact of Gaussian noise in the channel state information (CSI) of a linear zero forcing transmitter, operating in a fading MISO broadcast channel. We consider a rectangular channel with p users and n transmit antennas such that p ≤ n. System performance is analyzed in terms of the outage probability. Using results from [1], we give simple two and three dimensional integral formulae of the exact outage probability for an arbitrary number of users and transmit antennas. These integrals can be numerically evaluated, for an arbitrary number of users, with a fixed amount of computational effort. We give some numerical results that afford insight to the performance of broadcast channels under channel uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".