Energy-efficient pilot and data power allocation in massive MIMO communication systems based on MMSE channel estimation
Bibliographic record
Abstract
This paper addresses the pilot and data power allocation issue in time division duplexing (TDD) massive multi-user multiple-input multiple-output (MU-MIMO) systems. By using minimum mean square error (MMSE) channel estimation along with a maximum-ratio combining (MRC) detector for the uplink transmission and a maximum-ratio transmission (MRT) precoder for the downlink transmission, a novel pilot and data power allocation scheme is proposed to minimize the total uplink and downlink transmit power under per-user signal to interference-plus-noise ratio (SINR) and power consumption constraints. The main contribution of this paper lies in formulating the original energy efficient power allocation problem and converting such a complicated optimization problem to a geometric programming one. Computer simulation shows that the proposed scheme can save up to 78% of the total power as compared to the equal power allocation among all the mobile users.
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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".