Outage analysis for two-user parallel Gaussian interference channels
Bibliographic record
Abstract
We address outage analysis for a two-user parallel Gaussian interference channel consisting of two sub-channels. Each sub-channel is modelled by a two-user Gaussian interference channel with quasi-static and flat fading. Both users employ single-layer Gaussian codebooks and maintain a statistical correlation ρ between the signals transmitted over the underlying sub-channels. If the receivers treat interference as noise (TIN) or cancel interference (CI), the value of ρ minimizing the outage probability approaches 1 as the signal-to-noise ratio (SNR) approaches infinity, while ρ = 0 is optimum under joint decoding (JD) regardless of the value of SNR. Motivated by these observations, we let ρ = 1 under TIN and CI and ρ = 0 under JD and compute the outage probability in finite SNR assuming the direct and crossover channel coefficients are independent zero-mean complex Gaussian random variables with possibly different variances. In the asymptote of large SNR and assuming the transmission rate per user is r log snr, we show that the outage probability scales as snr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-(1-r)</sup> under both TIN and CI, while it vanishes at least as fast as snr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-min{2-r;4(1-r)}</sup> log snr under JD.
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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 | 0.000 |
| 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".