Sum-Rate Maximization for Two-Way Active Channels
Bibliographic record
Abstract
A two-way active parallel channel refers to a communication link between two transceivers, where the subchannel gains between the two transceivers can be adjusted such that a given performance criterion is optimized. A two-way active channel can have reciprocal subchannels, meaning that the subchannel gains in both communication directions are identical. Otherwise, if the gains of each subchannel in the two communication directions are different, the active channel is referred to as non-reciprocal. In this paper, we consider the problem of sum-rate maximization for reciprocal and non-reciprocal two-way active channels under two constraints on the transceivers' transmit powers and a third constraint on the channel power (i.e., the sum of squared of the subchannel gains). We prove rigorously that for such active channels, in order to maximize the sum-rate, only a subset of the subchannels will have to be active. We also provide the optimal values of the subchannel gains and the optimal values of the transceivers' transmit powers over different subchannels in closed forms. Simulation results show that parallel active channels can outperform their passive counterparts, where the subchannel gains are fixed, and thus, they cannot be adjusted.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".