Detect-and-Forward Multirelay Systems With Decision-Feedback Differential Coherent Receivers
Bibliographic record
Abstract
This paper considers a detect-and-forward multirelay wireless system employing differential MPSK modulation with decision-feedback differential coherent (DFDC) receivers. The DFDC receivers overcome the limitation of conventional differential detectors when used over time-varying fading channels and provide performance gains. Using a selective approach, the relays transmit only when the demodulation is errorless, and hence the destination employs an SNR-dependent threshold-decision rule to determine when the relays are active. The SNR-dependent thresholds ensure good performance over a wide SNR range. We propose the hold-and-estimate and hold-and-combine strategies for proper operation of the DFDC receivers over the relay to destination channels, to address the problem of intermittent transmissions on these links because of the selective protocol. We also demonstrate the necessity of initializing the DFDC receivers with pilot symbols. Finally, a recursive least squares adaptive algorithm is employed with the DFDC receivers to bypass the need of estimating the channel autocorrelation function. Analytical lower bounds to error probability illustrate the potential advantages of using DFDC receivers in such relaying systems. Extensive computer simulation results demonstrate the performance gains achieved by these relaying schemes with respect to comparable systems, especially over fast fading channels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".