Optimal power allocation and relay selection for multiple code division multiple access peer‐to‐peer communication
Bibliographic record
Abstract
The authors consider a half‐duplex asynchronous code division multiple access cooperative network with N source–destination (SD) pairs and a number of relay nodes where the nodes of all pairs have to exchange data in two hops via assistance of one of the available relays. In this study, they minimise the total transmit power and derive the closed‐form solution for choosing the best relay, the best relay gain and the transmit powers of all sources where some predefined signal‐to‐interference plus noise‐ratios (SINRs) are guaranteed. Interestingly, the feasibility condition of the problem depends only on the required SINRs, the number of SD pairs and the maximum cross‐correlation of users’ codes. They suggest two control procedures for admitting or dropping of users to the network to satisfy the feasibility condition. For a reciprocal environment, the best relay and its gain are proved to remain unchanged for reversing the communication directions. In addition, the authors’ power control algorithm can be directly applied to the case of two‐hop two‐way relaying. Computer simulations are used to demonstrate the system performance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".