Spectral Efficiency Maximization of Multiuser Massive MIMO with a Finite Dimensional Channel
Bibliographic record
Abstract
In this paper, we develop a closed-form lower bound for the achievable uplink rate of users in a single-cell massive multiple-input multiple-output (MIMO) system using zero-forcing (ZF) receiver. The base station (BS) is equipped with a large number of uniformly and linearly spaced antennas and serves single-antenna users. It estimates channel state information (CSI) using uplink pilot symbols. The channel model is also supposed to have a finite dimension. We then define the spectral efficiency as the sum of derived uplink rates. Through power allocation between data and pilot symbols, we further optimize this approximate expression of spectral efficiency. Simulation results are presented to evaluate the approximation of spectral efficiency and to show the advantage of our power allocation scheme in comparison to equal power allocation. The results also demonstrate that in low signal-to-noise ratios, more power should be allocated to the pilot symbols.
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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".