Capacity of Bernoulli-Gaussian Interference Channels in Rayleigh Fading with Full CSI
Bibliographic record
Abstract
In this paper, we investigate the channel capacity of a Bernoulli-Gaussian (BG) interference channel in Rayleigh fading when the channel state information (CSI) is known at both the transmitter and receiver via tight lower and upper bounds. Specifically, we first derive an upper bound on the channel capacity assuming a Gaussian-distributed output. Under this assumption, an optimal power adaptation scheme is established and the upper-bound is obtained in closed-form. By assuming a Gaussian-distributed input, we then adopt the derived power adaptation scheme to establish a lower bound on channel capacity. A simple approximation of the instantaneous output entropy using a piecewise-linear curve fitting(PWLCF)-based scheme is then developed, which provides a closed-form estimation of the lower bound with a predetermined error level. Finally, a comparison between the derived upper and lower bounds are made. Both analytical and numerical results show that these two bounds are tight in a wide range of input power levels and they can be used effectively to estimate the channel capacity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".