User-satisfaction-based weighted SLNR beamforming in TD-LTE-A system
Bibliographic record
Abstract
In TD-LTE-A system, the objective of conventional non-codebook beamforming algorithms is to maximize sum capacity to accommodate more users. However, fairness among users is not considered by these algorithms, and the performance of users with poor channel quality will always be bad. To relax the fairness requirement in resource allocation, this paper first introduces two parameters of user satisfaction into TD-LTE-A system for Guaranteed Bit Rate (GBR) and Non-GBR traffics, respectively. Then a user-satisfaction-based beamforming algorithm is proposed. This algorithm employs user satisfaction parameter to adjust the weights for weighted Signal-to-Leakage-plus-Noise Ratio (SLNR) algorithm. Finally, the weights of different traffics are also considered in the proposed beamforming algorithm so that average user satisfaction across different types of traffics can be modified according to the requirement of telecommunication operators. Simulation results show that the proposed algorithm can improve user satisfaction and user fairness, and average user satisfaction of GBR and Non-GBR traffics can be adjusted by changing of GBR priority parameter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".