The Impact of Imperfect Channel Estimations on the Performance of Optimum Combining in Decode-and-Forward Relaying in the Presence of Co-Channel Interference
Bibliographic record
Abstract
Optimum combining (OC) in cooperative relaying enables achieving a diversity gain of M in the presence of co-channel interference (CCI), where M is the number of relay nodes. The additional performance overhead of OC is the need for estimation of interferer channels. The impact of imperfect channel estimation on the performance of OC with decode-and-forward relaying is analyzed. When the source-destination and relay-destination channel estimations are imperfect, the diversity gains of OC deteriorate and the performance further degrades with increase of the error variance. When the destination node accurately estimates the variances of the interferer channel state information (CSI), instead of instantaneous CSI, no performance loss is observed. Thus, the overhead associated with the interferer channel estimation in OC can be significantly reduced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".