Sum-Rate Maximization for Active Channels With Unequal Subchannel Noise Powers
Bibliographic record
Abstract
In this paper, an active channel, between a source and a destination, refers to a parallel channel where the source transmits power over different subchannels as well as the powers of the subchannels can be adjusted. We herein study the sum-rate maximization for an active channel subject to two constraints, one on the source total transmit power and one on the total channel power. Although this maximization is not convex, we use Karush-Kuhn-Tucker (KKT) conditions to develop a computationally efficient algorithm for optimal source and channel power allocation. To do so, we first show how KKT conditions can be used to determine the number of subchannels that can be active in order for the source power constraint to be feasible. Indeed, we show that not all subchannels but only a subset of them may receive transmit power from the source. Then, for any feasible number of active subchannels, we obtain the optimal source power allocation. In fact, we prove that for any feasible number of active subchannels, there is only one or two solutions for the optimal source power allocation. As such, the optimal solution can be obtained by comparing a finite number of points in the feasible set and by introducing the point, which yields the best sum-rate performance, as the optimal solution. Our analysis and simulation results show that active channels can offer a significantly higher sum-rate compared to their passive counterparts, which rely on water-filling scheme for source power allocation across subchannels.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".