Cooperative Power Allocation Schemes and BER Performance in Decode-and-Forward OFCDM Based Relay Networks
Bibliographic record
Abstract
In this paper, different power allocation schemes between the source and the cooperating relay nodes are analyzed and numerically evaluated for a two-hop decode-and-forward OFCDM based relay network. A cooperative power allocation ratio ¿ (=source power/total power) is defined and BER performance is evaluated for different values of ¿ in the relay network. It is shown that there exists an optimal power allocation ratio for different operating environment such as source-to-relay channel gains and time-frequency spreading factors. It is reported that (a) When all three channels (source-to-relay, source-to-destination and relay-to-destination) have equal gains, power ratio is found to be ¿ ¿ 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The BER performance degrades at a faster rate when ¿ increases above the optimal value than the decrement at higher Eb/N¿. (b) For a network with stronger source-to-relay link, the optimal ¿ remains invariant at higher Eb/N¿for equal channel gain case; however, the optimal power ratio moves toward lower value of ¿ at lower Eb/N¿. (c) The optimal ¿ remains almost the same with different time-frequency spreading factors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".