Diversity combining in bi‐directional relay networks with energy harvesting nodes
Bibliographic record
Abstract
In this study, the authors consider two multiple‐antenna transceivers exchanging information through a relay‐assisted network using a single‐carrier communication scheme. The authors assume that the propagation delay in different relaying path is negligible and the relay nodes are synchronous. As a result, the end‐to‐end multipath channel is not frequency‐selective (time‐dispersive) and hence, the successive arriving signals at the transceivers do not interfere with each other. Otherwise, inter‐symbol‐interference (ISI) will be inevitable and cyclic insertion and removal matrices will be required to combat ISI. In such a two‐way network, the relay nodes harvest energy from the surrounding environment and utilise this energy to forward their received messages using a harvest‐then‐forward protocol. For different receiver diversity combining techniques, the authors design an optimal relay beamforming to maximise the quality of the received signals at the transceivers subject to the energy casualty constraint at the relay nodes (the energy consumed for transmission of each block cannot exceed the accumulative harvested energy). For each diversity combining technique, a closed‐form solution is obtained for the optimal signal‐to‐noise ratio (SNR) that shows how adjusting the data transmission rate of the transceivers and the amount of energy harvested at the relays affects the received SNR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".