On Sum-Rate Maximization Approach to Network Beamforming and Power Allocation for Asynchronous Single-Carrier Two-Way Relay Networks
Bibliographic record
Abstract
We study the sum-rate maximization problem, under a total power budget, for asynchronous single-carrier bi-directional relay networks, consisting of two transceivers and multiple amplify-and-forward relays. When different transceiver-relay links cause significantly different propagation delays in the signal they convey, the end-to-end channel is not amenable to a frequency-flat modeling; rather, a multi-path channel model is appropriate. Such a multi-path channel model results in inter-symbol-interference at the transceivers. Aiming to maximize the sum-rate of this channel over the relay weights and transceivers' powers, we rigorously prove that such a sum-rate maximization problem leads to a relay selection scheme, where only those relays, which contribute to one of the taps of the end-to-end channel impulse response (CIR), are turned on. Indeed, we prove that the optimal end-to-end CIR has only one non-zero tap, rendering the end-to-end channel frequency-flat. Our proof shows that the mean-squared-error (MSE) optimal joint post-channel equalization, network beamforming, and power allocation scheme is sum-rate-optimal. The equivalence of MSE-optimal and sum-rate-optimal solutions is interesting, as MSE minimization promotes end-to-end reliability, while sum-rate maximization advocates for multiplexing gain. These approaches often pull the design of communication systems in different directions. For the aforementioned scenario, these approaches are identical as we prove.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".