Non-Coherent and Mismatched-Coherent Receivers for Distributed STBCs with Amplify-and-Forward Relaying
Bibliographic record
Abstract
Cooperative diversity is a transmission technique where multiple nodes in a network cooperate to form a virtual antenna array realizing the benefits of spatial diversity in a distributed fashion. The coherent scenario considered in most existing work on cooperative diversity assumes the availability of perfect channel state information at the relay and destination terminals and is highly unrealistic in practical applications. In this paper, we investigate non-coherent and mismatched-coherent receivers for a cooperative diversity scheme assuming both quasi-static and time-varying fading channels for the underlying cooperative links. Specifically, we consider a distributed space- time block coded (STBC) system in a single-relay scenario operating in the amplify-and-forward relaying mode. Exploiting the orthogonal structure of distributed STBC, we first derive a non-coherent decoding rule which can be implemented in practice by a Viterbi-type algorithm. Although this decoding rule has been derived assuming quasi-static channels,- its inherent channel tracking capability allows its deployment over time- varying channels with a promising performance as a sub-optimal solution. As a possible alternative to non-coherent detection, we investigate the performance of mismatched-coherent receiver (i.e., coherent detection with imperfect channel estimation) within the considered relay-assisted transmission scenario. We further compare the performance of non-coherent and mismatched- coherent receivers to reveal their robustness under various mobility scenarios.
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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 | 0.004 |
| 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 | 0.001 |
| 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".