Optimized Power Allocation for Multiple-Antenna Multiple-Relay Cooperative Communication System
Bibliographic record
Abstract
One of the main advantages of relayed communication is that it reduces the necessary transmit power and distributes transmitted power throughout the hops. This implies lower interference to the rest of the network and consequently improves performance of the system. We investigate optimized power allocation over two-hop multiple antenna fixed relays for a given power budget. Outage probability which is the probability that the link quality falls bellow the certain threshold in a Rayleigh fading channel is used as the performance criterion. It is determined that increasing the number of relays and antennas at each relay increases capacity. The outage probability of threshold maximal ratio combining (T-MRC) and threshold selection combining (T-SC) of the multiple antenna multiple fixed relays is derived. Threshold decode and forward relaying (T-DF), which is more reliable than conventional decode and forward relaying (DF) is considered. Results show that optimizing the allocation of power enhances the system performance, especially in highly unbalanced links. The system with optimized power allocation can outperform the two-hop multiple relay system using uniform power allocation and distributed beamforming at the expense of increased computational complexity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".