User Effects on MIMO Performance: From an Antenna to a Link Perspective
Bibliographic record
Abstract
We investigate in this paper the effects of the user's presence on the performance of a multiple-input multiple-output (MIMO) system in data and in voice usage scenarios. The investigation studies the user effects on the antenna performance and how these are incorporated into the MIMO channel and the link characteristics. The antennas and the user are deterministic. These are then integrated into the statistical 3GPP spatial channel model (SCM) for a typical macrocell propagation environment setting. The channel performance is analyzed based on the average channel capacity, the average power transfer, the correlation, and the cumulative distribution function of the channel capacity as well as the link throughout and the error performance. The mentioned channel and link properties are tied to the MIMO antenna properties that are represented in the mutual coupling between the antennas, the power loss, the total radiated power, the mean effective gain (MEG), as well as the efficiency with emphasis on how the user affects each. It was found that the presence of the user contributed to a loss of up to 50% in the average channel power transfer. The data position was found to be the lowest in terms of channel capacity performance. The voice position performance showed a large dependence on the user orientation with respect to the line of sight path while the data position showed less dependence on the user's orientation. We also discuss through the examined antenna and channel properties the importance of the channel multipath on the MIMO performance. In some scenarios, it was found that a well-conditioned channel can compensate for losses due to the presence of the user, improving the overall system performance. The presented investigation at the link level also discusses the user effects in different MIMO transmission schemes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".