Downlink Power Distribution in a Wireless CDMA Network with Cooperative Relaying
Bibliographic record
Abstract
This paper studies power distribution in the downlink of a wireless CDMA network, where mobile stations (MSs) cooperatively relay traffic for their peer stations, and the destination station (DS) combines signals received from both the base station (BS) and the relay station (RS). Both the DS and the RSs transmit at the same frequency band. We consider both decode-and-forward (DF) and amplify-and- forward (AF). An optimization problem is first formulated for distributing the transmission power of the BS and the RSs. The objective is to minimize the transmission power of the BS and the total transmission power of the RSs, subject to the average transmission rate and signal-to-interference-plus-noise ratio (SINR) requirement of the user traffic. The optimum power distribution requires link gains among different MSs, which are usually not available at the BS. We then propose a practical power distribution scheme based on link gains of the RS and DS to the BS. Our results show that i) the proposed link-gain based power distribution scheme achieves close-to-optimum performance, ii) by appropriately selecting the RS and forwarding techniques, cooperative relaying in the downlink of a CDMA network can reduce the communication outage probability and significantly save the BS transmission power in the downlink transmissions, and iii) cooperatively relaying requires very low transmission power from the RSs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".