Channel Training and Coherent Decodings in Amplify-and-Forward Relay Network
Bibliographic record
Abstract
In this paper, channel training and coherent decodings with channel estimation errors are investigated in a relay network with one single-antenna transmitter, two single-antenna relays, and one double-antenna receiver. A two-stage training scheme is proposed to estimate both the relay-receiver and transmitter-relay channels at the receiver. We use distributed space-time coding (DSTC) for data transmission and investigate the effect of channel estimation errors on the diversity. Two coherent decodings are considered: mismatched decoding in which the channel estimations are treated as if perfect, and matched decoding which takes into account the estimation errors. We show that for full diversity, mismatched decoding requires at least 6 symbol intervals for training, while 4 symbol intervals for training are enough for matched decoding. On the other hand, the complexity of matched decoding is much higher. To achieve a balance between performance and complexity, an adaptive decoding scheme is proposed. Simulated network error rates are shown to justify the analytical results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".