Spectral efficiency maximization of single cell massive multiuser MIMO systems via optimal power control with ZF receiver
Bibliographic record
Abstract
This paper investigates the spectral efficiency of multiuser multiple-input multiple-output systems with a large number of antennas at the base station that serves single-antenna users in one cell. It is assumed that the base station estimates the channel with the help of uplink training and then employs the zero-forcing technique to detect the data signals transmitted by the various users. An optimal power control scheme over pilot and data power based on large-scale fading is proposed to maximize the sum spectral efficiency for a given total energy budget in a coherence interval. Simulation results show that the spectral efficiency of the proposed method is superior to that of other existing methods. It Is also shown that, In order to maximize the sum spectral efficiency, more power should be allocated to the data signal power at high signal-to-nolse ratios and less power at low signal-to-noise ratios.
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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".