On linear detector's spectral capacity for massive MIMO systems
Bibliographic record
Abstract
In this paper, we investigate and study the performance of three linear receivers in Multi-User Multiple-Input-Multiple-Output (MU MIMO) systems with large antenna arrays, namely very large MU MIMO or massive MIMO. In general, when the number of antennas at the Base Station (BS) is much larger than the number of users in the system, linear receivers perform quite well. Therefore, we present the derivation, with perfect Channel State Information (CSI) and imperfect CSI, the lower capacity bounds for Maximum-Ratio Combining (MRC), Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) receivers. Mathematical analysis and simulations suggest that the users' transmission power can be reduced by the number of BS antennas in the case of perfect CSI, and can be reduced by the square root of the number of BS antennas in the case of imperfect CSI, while providing a set, desired Quality-of-Service (QoS). Compared to the single antenna system, the presented results showed that spectral efficiency of the system is enhanced by using fairly large antenna arrays.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".