Cooperative subcarrier and power allocation for a two-hop decode-and-forward OFCMD based relay network
Bibliographic record
Abstract
In this article, subcarrier and power allocation schemes are proposed and analyzed for different scenarios for a two-hop decode-and-forward OFCDM based relay network. In subcarrier allocation, the effect of considering the channel state information (CSI) of source-base station and source-relay link are evaluated in a cooperative diversity system. Results show that allocation of subcarriers based on source-relay node CSI provides better BER performance at higher Eb/No, and at lower Eb/No, both the source-relay and source-base station links need to be considered. From our numerical simulation, we also noticed that the cross-over Eb/No, point (around which frequency spreading gives better performance than time spreading) moves towards the lower Eb/No, when the subcarrier allocation is done giving more weight to source-base station link rather than the source-relay link which provides additional flexibility in operating environment for OFCDM systems. In power allocation, a cooperative power allocation ratio lambda (=source node power/total power) is defined and BER performance is evaluated for different values of lambda in the relay network. It is found 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 lambda ap 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The performance degrades at much faster rate when lambda increases above the optimal value at higher Eb/No. On the other hand, the performance remains almost the same when the decrement in lambda is less than the optimal value. (b) For a network with stronger source-to-relay link, the optimal lambda remains almost the same as the case with equal channel gains at higher Eb/No; however, the optimal power ratio moves toward lower value of lambda of 0.65 at lowerb/No. (c) The optimal lambda 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".