Performance Analysis of a New Transmission Scheme for Multi-Relay Channels
Bibliographic record
Abstract
Cooperative diversity provides reliable communications between nodes in a network through relay nodes. In this paper, we introduce a new transmission protocol for relay fading channels. We examine the performance of the proposed protocol using both the amplify-and-forward (AF) and decode-and-forward (DF) modes. Our results prove that using this protocol, one can achieve full spatial diversity at full rate. We also show that our protocol with M relays is equivalent to a delay diversity scheme with M+1 transmit antennas. At the receiver side, a maximum likelihood sequence detector is used to recover the transmitted symbols. Comparing our protocol with existing ones, we noted large performance degradations in all protocols when the relay is operating in the DF mode where detection errors exist. This is different from the AF mode, where diversity is always maintained and only a SNR loss is incurred (relative to the ideal case of error-free relay transmission). This, in turn, suggests that even with the large cost/complexity involved in the DF mode, the ensuing performance may be poor compared to the AF mode. Motivated by this fact, we obtain a bit-error rate upper bound for a multi-relay configuration where all relay nodes operate in the AF mode. At high signal-to-noise ratio (SNRs), this error bound is shown to be tight when compared to simulation 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".