Optimum Power Allocation for Fading Relay Channels
Bibliographic record
Abstract
Optimum power allocation is a key technique to realize the full potentials of relay-assisted transmission promised by the recent information-theoretic results. In this paper, we present a comprehensive framework for power allocation problem in a single-relay scenario taking into account the effect of relay location. In particular, we aim to answer the two fundamental questions: Q1) How should the overall transmit power be shared between broadcasting and relaying phases?; Q2) In the relaying phase, how much power should be allocated to relay-to-destination and source-to-destination links? The power allocation problem is formulated to minimize a union bound on the bit error rate (BER) performance assuming amplify-and-forward (AaF) relaying. We consider three TDMA-based cooperation protocols which correspond to distributed implementations of MIMO (multi-input-multi-output), SIMO (single-input-multi-output), and MISO (multi-input-single-output) schemes. Optimized protocols demonstrate significant performance gains over their original versions which assume equal sharing of overall transmit power between the source and relay terminals as well as between broadcasting and relaying phases. It is observed that optimized virtual (distributed) antenna configurations are able to demonstrate a BER performance as close as 0.4 dB within their counterpart co-located antenna configurations.
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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 | 0.002 |
| 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".