A study on channel estimation algorithm with sounding reference signal for TDD downlink scheduling
Bibliographic record
Abstract
Coping with the limited amount of available spectrum, time division duplexing (TDD) system is considered as an attractive duplexing method due to exploiting channel reciprocity as well as flexible resource management. The conventional scheduling scheme is based on the channel quality indicator (CQI) reported from the user equipment (UE) to estimate instantaneous data rates for the scheduling metric calculation. However, CQI is insufficient to reflect the state of the channel variation in terms of frequency and time. Based on the channel reciprocity of TDD systems, we utilize uplink sounding reference signal (SRS) to estimate downlink channel status. However the received SRS power is a result of uplink power control where power control effect should be compensated to estimate channel status in downlink scheduling. In order to solve this problem, we propose the SRS path loss estimation method based on the power headroom report. By using this scheme, the base station (BS) can obtain the compensated signal-to-interference-plus-noise-ratio (SINR) and determine the scheduling metric based on its calculated SINR instead of reported CQI from UE. Simulation results show that the proposed scheduling algorithm outperforms the conventional scheme in total throughput, whereas the fairness index experienced by the proposed algorithm is lesser than of the conventional scheme based on CQI.
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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".