Effective Data Rate Based Rank Adaptive Receive Antenna Selection
Bibliographic record
Abstract
In LTE-A downlink, beamforming is adopted to improve spectral efficiency and system capacity. Antenna selection in beamforming selects the proper receive signal subspace on different resource blocks (RBs), which can further improve system performance. In this paper, we first define the concept of effective data rate. Based on such concept, we propose a low complexity antenna selection algorithm for beamforming technology. Different from existing algorithms, the proposed algorithm first determines the data layer number of each user by considering both channel quality and user requirements, and based on the data layer number, it selects a corresponding number of receive antennas to form the receive signal subspace. As a result, dynamic switching between single-layer and dual-layer beamforming can be realized so that user requirements can be better satisfied. Then, the proposed algorithm calculates the spatial correlation to assign proper antennas on different RBs to reduce inter-layer interference. Simulation results show that the effective data rate of the proposed algorithm is higher than other algorithms when user number is bigger than 20 so that the requirements of more users can be satisfied, and user fairness can also be improved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".