End-to-End Optimum ML Detection for DF Cooperative Diversity Networks in the Presence of Interference
Bibliographic record
Abstract
Unlike the existing detectors, which are developed for decode-and-forward (DF) networks in the ideal interference-free case, we consider a more practical scenario where arbitrary interference exists. We consider a DF cooperative network consisting of a source, multiple relays, a destination, and multiple interferers affecting both the relays and the destination. Each relay is equipped with multiple antennas and knows its local instantaneous channel state information (CSI). Assuming that the destination knows the instantaneous CSI of the source-relay, relay-destination, and source-destination channels, we develop, for the first time in the literature, the end-to-end optimum maximum-likelihood (ML) detectors in closed-form for DF systems employing either simultaneous or orthogonal transmissions in the presence of interference. Furthermore, theoretical analysis shows that the proposed detectors achieve full diversity gains in the presence of interference with finite interference-to-noise ratios. Numerical results demonstrate that the proposed optimum detectors substantially outperform the conventional schemes which simply ignore 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".