Sum-Rate Maximization for Active Channels
Bibliographic record
Abstract
In this letter, we study the problem of joint power allocation and channel design for an active link which conveys information from a source to a destination through multiple orthogonal subchannels. In such a link, the power can be injected into the channel not only at the source but also at each subchannel. For such a parallel channel, we study the problem of sum-rate maximization under the assumption that the source power as well as the total power of the active channel are limited. Although this problem is not convex, we present an efficient solution to this sum-rate maximization. An interesting aspect of this solution is that it requires only a subset of the subchannels to be active and the remaining subchannels should be turned off. This is in contrast with passive parallel channels with equal subchannel signal-to-noise-ratios (SNRs), where water-filling solution to the sum-rate maximization under a source total power constraint leads to an equal power allocation among all subchannels. Furthermore, we prove that the number of active subchannels depends on the product of the source and channel powers. We also prove that if the total power available to the source and to the channel is limited, then in order to maximize the sum-rate via optimal power allocation to the source and to the active channel, half of the total available power should be allocated to the source and the remaining half should be allocated to the active channel.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".