Energy Efficient Pilot and Data Power Allocation in Multi-Cell Multi-User Massive MIMO Communication Systems
Bibliographic record
Abstract
In this paper, we propose a joint pilot and data power allocation scheme aiming to improve the energy efficiency of time division duplexing (TDD) massive multi-user multiple-input multiple-output (MU-MIMO) communication systems for both uplink and downlink transmission. The proposed scheme uses a maximum-ratio combining (MRC) detector in the uplink together with a maximum-ratio transmission (MRT) precoder in the downlink. By using minimum mean square error (MMSE) channel estimation, the total uplink and downlink transmit power is minimized under per-user signal to interference-plus-noise ratio (SINR) requirement and per-user power consumption constraints. Lower bounds of the average SINR are derived and used in the power allocation algorithm in order to simplify the optimization problem. The tightness of the derived SINR lower bounds and the advantage of the proposed power saving scheme as compared to equal power allocation among all users are validated by computer simulation.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".