Analysis of a Dual‐Rate Transmission Scheme for Gaussian Broadcast Channels
Bibliographic record
Abstract
Abstract In a broadcast channel, a single transmitter communicates to receivers with different channel capacities, a typical example of which is the over‐the‐air television broadcasting. A natural question that arises is evaluation of the optimal information transfer rate in such an environment. For the two‐receiver case, we examine various transmission scenarios in this environment and focus on the theoretically optimal superposition scheme, where the information intended for the better channel is superimposed on the portion of information common to both receivers. As a result, a basic grade of service is maintained throughout the entire coverage area in addition to a higher quality service offered to the receivers with better reception conditions. Using end‐to‐end distortion and coverage channel signal‐to‐noise ratio threshold measures, three specific embedded signaling modes are introduced. We demonstrate that a generalized embedding scheme provides a practical trade‐off between distortion and coverage radius. The scheme is also formulated in terms of an optimization problem, seeking the best embedding factor for a given application. To verify the theoretical results, we idealize a dual rate transmission system over a Gaussian broadcast channel, in which receivers are distributed according to a circularly symmetric exponential pdf. The numerical results indicate that this model offers a higher per capita data rate comparing to the conventional single rate transmission systems. Application of this multirate broadcast holds promise for multiresolution transmission of digital audio and high definition television.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".