Performance analysis of amplify-and-forward relaying with optimum combining in the presence of co-channel interference
Bibliographic record
Abstract
The diversity gains of cooperative relay networks are degraded in the presence of co-channel interference (CCI), which is the principal limiting factor in a properly planned cellular network. Optimum combining (OC) can be used to mitigate the adverse effects of CCI, which enables achieving diversity gains when CCI is present. The performance of OC in a channel state information (CSI) assisted amplify-and-forward (AF) relay network is analyzed when the destination node is affected by CCI, with the aid of a tight approximation for the signal-to-interference-plus-noise-ratio (SINR) at the destination node. Closed-form expressions are derived for the outage probability and the moment generating function for the approximated SINR. It is proved that OC results in a diversity gain of M, where M is the number of relay nodes. OC shows significant performance improvements over maximal-ratio combining (MRC), which reaches error floors at low-to-medium power levels.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".