Single-carrier frequency domain equalization for broadband cooperative communications
Bibliographic record
Abstract
In this paper, we investigate single carrier frequency-domain equalization (SC-FDE) for distributed space-time block codes (D-STBC) in a relay-assisted transmission scenario over frequency-selective fading channels. We assume the special case of a single-relay where the source-to-relay (S rarr R), relay-to-destination (R rarr D), and source-to-destination (S rarr D) links experience possibly different channel delay spreads. Assuming perfect power control between R rarr D and S rarr D links and high signal-to-noise ratio for all underlying links, our performance analysis demonstrates that SC-FDE for D-STBC is able to achieve a maximum diversity order of min(L1, L3)+L2+2 where L1, L2and L3are the channel memory lengths for S rarr R, S rarr D, and R rarr D links, respectively. This illustrates that the smaller of the multipath diversity orders experienced in S rarr R and R rarr D links becomes the performance bottleneck for the relaying path. For the special case of a non-fading relaying path where line-of-sight propagation is possible in either one of these underlying links, we demonstrate that the maximum diversity orders of L1+L2+2 and L3+L2+2 are achievable assuming nonfading R rarr D and S rarr R links, respectively. An extensive Monte Carlo simulation study is presented to corroborate the analytical results and to provide detailed performance comparisons among the competing schemes
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".