Jointly optimal distributed beamforming and power control in asynchronous two-way relay networks
Bibliographic record
Abstract
In this paper, we consider an asynchronous two-way relay network, where multiple single-antenna relay nodes enable bi-directional communication between two single-antenna transceivers using amplify- and-forward (AF) signaling in a multiple access broadcast channel (MABC) protocol. We assume that each relaying path, which originates from one transceiver, goes through one of the relays, and ends at the other transceiver, causes a delay which can be significantly different from the delays caused by other relaying paths. Such a two-way relay channel can produce inter-symbol-interference at the two transceivers. Assuming a block transmission scheme, we use cyclic prefix insertion to eliminate inter-block-interference. Aiming to optimally obtain the relay beamforming weights and the transceivers' transmit powers, we minimize the total consumed power in the network, subject to two constraints on the transceivers' data rates. We rigorously prove that at the optimum, the end-to-end channel impulse response (CIR) must have only one non-zero tap, and hence, only those relays which contribute to that non-zero tap are switched on. We propose a simple search algorithm to optimally determine which tap of the end-to-end CIR is non-zero. Finally, we present a semi-closed-form solution for the optimal the relays' beamforming weights and the transceivers' transmit powers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".