Sum-Rate Optimal Network Beamforming and Subcarrier Power Allocation for Multi-Carrier Asynchronous Two-Way Relay Networks
Bibliographic record
Abstract
In this paper, we consider a multicarrier asynchronous bidirectional relay network, where different relay-transceiver links can cause different delays in the signals they convey, thereby producing inter-symbol-interference at the two transceivers. Such a two-way relay link can be modeled as a frequency selective channel with multiple taps, and thus, it can be equalized using orthogonal frequency division multiplexing (OFDM) transmission and reception schemes at the two transceivers, while the relays use simple amplify-and-forward relaying protocol. We prove rigorously that maximizing the sum-rate under a total power budget, through jointly optimal network beamforming at the relays and subcarrier power loading at the two transceivers, leads to a relay selection scheme, where only those relays which contribute to one tap of the end-to-end channel impulse response, have to be active and the remainder of the relays have to be switched off. The optimal tap can then be obtained using a simple search method. Moreover, we present semi-closed-form solutions for the optimal value of transceivers' subcarrier powers as well as for the relay beamforming weights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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) | 0.002 | 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".