Multihop Diversity in Wireless Relaying Channels
Bibliographic record
Abstract
This paper presents theoretical characterizations and analysis for the physical layer of multihop wireless communications channels. Four channel models are considered and developed: the decoded relaying multihop channel; the amplified relaying multihop channel; the decoded relaying multihop diversity channel; and the amplified relaying multihop diversity channel. Two classifications are discussed: decoded relaying versus amplified relaying, and multihop channels versus multihop diversity channels. The channel models are compared, through analysis and simulations, with the "singlehop" (direct transmission) reference channel on the basis of signal-to-noise ratio, probability of outage, probability of error, and optimal power allocation. Each of the four channel models is shown to outperform the singlehop reference channel under the condition that the set of intermediate relaying terminals is selected intelligently. Multihop diversity channels are shown to outperform multihop channels. Amplified relaying is shown to outperform decoded relaying despite noise propagation. This is attributed to the fact that amplified relaying does not suffer from the error propagation which limits the performance of decoded relaying channels to that of their weakest link.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".