On the Ergodic Capacity of Wireless Relaying Systems over Rayleigh Fading Channels
Bibliographic record
Abstract
The ergodic capacities in Rayleigh fading of various wireless relaying systems with an arbitrary number of half duplex relays are analyzed, assuming channel state information is only known at the receivers. Closed-form analytical expressions for calculation of the ergodic capacities of these systems are derived. It is shown that systems with nonregenerative fixed gain relays achieve higher ergodic capacities than the corresponding systems with nonregenerative variable gain relays. A modified fixed gain relay, which incorporates the power constraint at the relays, is proposed. It is shown that systems with modified fixed gain relays slightly outperform the corresponding systems with nonregenerative variable gain relays and fixed gain relays at small signal-to-noise ratios, but attain almost the same ergodic capacities as systems with nonregenerative variable gain relays as the signal-to-noise ratio increases. In addition, the ergodic capacity of a hybrid system with both regenerative and nonregenerative relays is studied. Systems with regenerative relays employing decode-and-forward relaying offer higher ergodic capacities than the corresponding systems with any classes of nonregenerative relays or hybrid relays.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".