Sum-rate maximization for asynchronous two-way relay networks
Bibliographic record
Abstract
This paper is on sum-rate maximization, under a total transmit power budget, for an asynchronous single-carrier bidirectional (two-way) relay network. Considering a two-way relay network where two single-antenna transceivers exchange information through multiple single-antenna amplify-and-forward (AF) relays, we assume that different transceiver-relay links cause significantly different propagation delays in the signal they convey. This asynchronous bidirectional network is not amenable to a frequency flat end-to-end channel model, rather a multi- path end-to-end channel model with multiple taps appears to be more realistic. Such multi-path channel causes inter-symbol- interference (ISI) at the two transceivers. Such an ISI results in inter-block interference (IBI) in a block transmission/reception scheme which in turn can result in the loss of sum-rate, if it is not properly taken into account in the process of power allocation and network beamforming. Assuming the transceivers' transmit powers as well as the relay beamforming weights as the design parameters, we rigorously prove that the sum-rate maximization problem of such a network 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, are turned on and the rest of the relays are switched off. We present semi-closed-form solutions for the design parameters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".