Diversity-multiplexing tradeoff in multiple-relay network-part II: Multiple-antenna networks
Bibliographic record
Abstract
This paper studies the setup of a multiple-relay network in which K half-duplex multiple-antenna relays assist in the transmission between a/several multiple-antenna source(s) and a multiple-antenna destination. Each two nodes are assumed to be either connected through a quasi-static Rayleigh fading channel, or disconnected. This paper is comprised of two parts. In this part of the paper, we study multiple-antenna multiple-relay network. We prove that the Random Sequential (RS) scheme proposed in achieves the maximum diversity gain in a general multiple-antenna multiple-relay network. Moreover, we show that utilizing independent random unitary matrix multiplication at the relay nodes enables the RS scheme to achieve better diversity-multiplexing tradeoff (DMT) results comparing with the traditional amplify-and-forward relaying. Indeed, using the RS scheme, we derive a new achievable DMT for the MIMO parallel relay network. Interestingly, it turns out that the DMT of the RS scheme is optimum for the MIMO half-duplex parallel 2-relay (K = 2) setup. Finally, we show that utilizing random unitary matrix multiplication also improves the DMT of the Non-Orthogonal amplify-and-forward relaying scheme of in the MIMO single relay channel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".