Channel Prediction-Based Adaptive Power Control for Dynamic Wireless Communications
Bibliographic record
Abstract
In order to improve the transmit power efficiency at the Mobile Station (MS), the transmit power is usually adjusted based on feedback information from the Base Station (BS) or Channel State Information (CSI) estimated at the MS. In fast-varying channels, because of the propagation and estimation delays, and the short channel coherence time, both the feedback information and the estimated CSI may become outdated at the transmit instant, leading to a reduction in power control performance. In this paper, a new channel prediction-based adaptive power control technique is proposed for uplink transmission in Time Division Duplex (TDD) Orthogonal Frequency Division Multiplexing (OFDM) systems. Based on the predicted Channel Impulse Responses (CIRs) provided by a cluster-based time- domain channel predictor, the transmit power is allocated to each OFDM subcarrier and then, a global gain is applied to compensate for the propagation path loss. In doing this, the power control process does not rely on the feedback information from the BS nor the estimated CSI at the MS and thus, the system responsiveness, adaptivity, and power savings are improved.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".