Grassmannian beamforming for Coordinated Multipoint transmission in multicell systems
Bibliographic record
Abstract
Wireless communications suffer from the problem of interference, where undesired signals reach the destination and cause its performance to degrade. Several mechanisms have been proposed over the last years to mitigate the interference problem, where Coordinated Multipoint (CoMP) outstands as one of the best approaches to tackle the interference in cellular systems. The main disadvantage of CoMP is the need of high signalling over the system in order to exchange information among the different parties involved in the communication process (eNBs and UEs), that questions its practical benefit for the operators. This paper presents a novel beamforming mechanism that needs for very low signalling among the transmitting base stations, it is a low complexity transmission strategy, the amount of interference to cell edge users is controlled and it offers an outstanding performance. Computer simulations show the data rate enhancement of the proposed CoMP mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".