Optimal Resource Sharing and Network Beamforming in Multi-Carrier Bidirectional Relay Networks
Bibliographic record
Abstract
Considering a bidirectional collaborative scheme, we study the problem of resource sharing between two transceiver pairs in a multi-carrier scenario. One pair, referred to as the primary pair, is considered to be the owner of the spectral resources, meaning that the rates of its users must be guaranteed to be greater than a predefined threshold. It is assumed that the other pair, called the secondary pair, owns the relay infrastructure. Considering no direct link between the transceivers in each pair, the primary network allows the secondary pair to use the spectral resources in order to establish a bidirectional communication between its transceivers. In exchange for this cooperation, the primary pair utilizes the relay infrastructure, thereby enabling a two-way communication between its transceivers. Assuming amplify-and-forward relaying scheme in each subchannel, the relays collectively build two network beamformers each of which enables communication between one pair of transceivers. Aiming to optimally calculate the parameters of the two networks, we study two different approaches. The first approach relies on maximizing the secondary network average sum-rate subject to two spectral power masks for the two networks, while providing a minimum sum-rate to the primary pair in a multi-relay scenario. In the second approach, we consider a constraint on the total power consumed in each network over all subchannels, while maximizing the sum-rate of the secondary transceivers. In this approach, we provide two iterative convex search solutions, one for a single-relay case and one for a multi-relay scenario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".