Decorrelate-and-forward relaying scheme for multiuser wireless code division multiple access networks
Bibliographic record
Abstract
Decorrelate-and-forward relaying scheme for wireless synchronous code division multiple access (CDMA) networks where multiple sources communicate with a destination supported by multiple relays in flat Rayleigh fading channels is considered. By exploiting equicorrelated spreading sequences, a simple near–far resistant decorrelator together with a beamforming scheme at each relay and the maximal ratio combiner (MRC) at the destination are developed in order to achieve the full cooperative diversity for every source. Different from the case with a single source, the beamforming design in multiple-source networks needs to take into account the amount of transmit power each relay spends to forward the signals from the sources. As such, the authors also propose a novel power allocation scheme to improve the fairness among the sources in terms of the instantaneous signal-to-noise ratio. Simulation results demonstrate the effectiveness of the proposed solution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".