Performance analysis of decode-and-forward relaying with optimum combining in the presence of co-channel interference
Bibliographic record
Abstract
Cooperative relaying achieves a wide range of advantages including diversity gain, coverage extension and mitigation of shadowing. Nevertheless, the performance advantages of cooperative relaying diminish considerably if co-channel interference is present. Optimum combining (OC) can be used to mitigate the adverse effects of co-channel interference in wireless communications. The performance of optimum combining in a decode-and-forward relay network with N equal-power interferers is analyzed. The probability density function of the output signal-to-interference-plus-noise ratio is obtained and a closed-form expression for the exact outage probability is derived for N ≥M+1 where M is the number of relay nodes. An approximation for the symbol error rate (SER) is presented. The performance results and closed-form expressions for outage probability and for SER suggest that the asymptotic diversity gain of OC is equal to M, which is a significant improvement over maximal-ratio combining, whose asymptotic diversity gain is equal to zero when operating in co-channel interference.
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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 | 0.009 |
| 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 | 0.001 | 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".