Robustness of Massive MIMO to Location and Phase Errors
Bibliographic record
Abstract
The impact of random errors in element locations and beamforming phases on the performance of massive multipleinput multiple-output (MIMO) systems and their ability to cancel inter-user interference (IUI) are studied. For an arbitrary array geometry, user orthogonality, also known as “favorable propagation” (FP), is shown to hold asymptotically for the perturbed array as long as it holds for the unperturbed one, for independent (possibly non-Gaussian) errors. This means that small errors do not have catastrophic impact on the FP, even for a large number of antennas, and IUI can be reduced to any desired level. The negative impact of random errors is to slow down the convergence to the asymptotic value so that more antennas are needed under random errors to achieve the same low IUI as without errors. Practical design guidelines are given as to what implementation accuracy is needed to make the impact of random errors negligible and a closed-form estimate of IUI under random errors is presented. The analytical results are validated via numerical simulations and are in agreement with measurement-based studies.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".