User effect analysis on polarisation in multi-input multi-output systems
Bibliographic record
Abstract
In this study the authors present the results of investigating the human head effects on the performance of a two-antenna system as applied to the polarisation of these antennas. For this, we define a 2×2 multi-input multi-output (MIMO) model constructed of two dipole transmitting antennas and two dipole receiving antennas. The user head effects are studied at three different frequencies: 700, 950 and 1880 MHz. The investigation is performed via full-wave electromagnetic simulations and lab experiments. Two antenna polarisation scenarios are investigated: vertically co-polarised (V-polarised) dipoles and cross-polarised (X-polarised) dipoles. The channel matrix that includes the antenna system on both sides of the communication link and the user's head is constructed and characterised. The channel power factor, the sub-channel weights and the channel capacity are all measured and analysed. The investigation showed that the presence of the human head decreases the channel power at all the examined frequencies and this effect becomes more sever as the frequency becomes higher. Also, the user head helped to improve the communication channel state in the V-polarised scenario, whereas it degraded the channel state in the X-polarised scenario. Therefore in the presence of the user, V-polarised antenna configurations would be the better choice if an increase in the channel capacity is the multi-antenna design objective. While X-polarised antenna configurations should be considered if an improvement in the signal quality and hence in the communication link margin is the MIMO design objective.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".