Beamforming for inter‐relay interference reduction in MIMO‐aided two‐path successive relaying
Bibliographic record
Abstract
Abstract To effectively reduce the inter‐relay interference (IRI) in two‐path successive relaying, two beamforming schemes are proposed in this paper, utilizing multiple‐antenna relay nodes. Specifically, the two cooperation nodes perform receive combining of the source signal and transmit beamforming of the relayed signal alternately in the successive relaying process. As a result, the IRI between them can be effectively suppressed, thanks to the additional degree of freedom provided by the multiple‐input multiple‐output inter‐relay channel. In the first beamforming scheme, the source‐to‐destination signal‐to‐interference‐plus‐noise ratios (SINR) of separate paths are maximized with approximation, leading to a minimum variance distortionless response beamformer under the high SINR condition. To further improve the system performance, noting that the received SINRs of the two paths have impact on each other due to the mutual coupling of the beamformers, the sum of mean squared errors from these two transmission paths is minimized in the second scheme. Based on this performance criterion, a suboptimal beamformer design is developed numerically through cyclic minimization of the sum of mean squared error cost function. Simulation results demonstrate the superiority of both proposed beamforming schemes in terms of symbol error rate and the achievable system rate, in particular, at high IRI levels. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".