Cooperative Diversity with Multiple-Antenna Nodes in Fading Relay Channels
Bibliographic record
Abstract
In this paper, we investigate the performance of a single-relay cooperative scenario where the source, relay and destination terminals are equipped with multiple transmit/receive antennas. We assume that conventional space-time block codes are employed in the underlying source-to-destination (SrarrD), source-to-relay (S rarr R) and relay-to-destination (R rarr D) links, and consider both decode-and-forward (DaF) and amplify - and-forward (AaF) relaying techniques. For the latter one, we consider two variants based on the availability of channel state information (CSI); namely, blind AaF and CSI-assisted AaF. Through the derivation of pairwise error probability, we quantify analytically the impact of multiple antenna deployment for each relaying technique under various scenarios which involve relay location and power control assumptions imposed on cooperating nodes. Our transmission model assumes that the source and destination terminals are equipped with MStransmit and N receive antennas, respectively, and the relay terminal is equipped with MRreceive and MTtransmit antennas. For a scenario where R rarr D and S rarr D links are balanced and S rarr R link experiences sufficiently large SNR, our performance analysis demonstrates that the maximum achievable diversity order is MTmin(MS, N)+MSN for blind AaF scheme and N(MT+MS) for both CSI-assisted AaF and DaF schemes. For another scenario where R rarr D link has a sufficiently large SNR and S rarr R and S rarr D links are balanced, CSI-assisted AaF, blind AaF and DaF schemes achieve diversity orders of MS(N + MR), MS(N + MT), and MSN, respectively. Other scenarios involving the availability of non-fading R rarr D link and poor inter-user channel quality are further investigated. An extensive Monte Carlo simulation study is also presented to corroborate the analytical results and to provide detailed performance comparisons among the three relaying techniques under consideration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".