Cooperative diversity over log-normal fading channels: performance analysis and optimization
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Bibliographic record
Abstract
Although there has been a growing interest on cooperative diversity, the current literature is mainly limited to the results obtained for Rayleigh, Rician, or Nakagami fading channels. In this paper, we investigate the performance of cooperative diversity schemes over log-normal fading channels which provide an accurate channel model for indoor wireless environments. We focus on single-relay cooperative networks with amplify-and-forward relaying and consider three TDMA-based cooperation protocols: which correspond to distributed implementations of MIMO (multi-input multi-output), SIMO (single-input multi-output), and MISO (multi-input single-output) schemes. For each protocol under consideration, we derive upper bounds on pairwise error probability over log-normal channels and quantify the diversify advantages. Based on the minimization of a union bound on the bit error rate performance, we further formulate optimal power allocation schemes which demonstrate significant performance gains over their counterparts with equal power allocation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it