A 3D-to-2D Transform Algorithm for Incorporating 3D Antenna Radiation Patterns in SCM
Bibliographic record
Abstract
We present a method to incorporate the effect of the three-dimensional (3D) antenna radiation patterns into a two-dimensional (2D) multiple-inputmultiple-output (MIMO) channel model. The proposed method is a low-complexity technique that increases the accuracy of existing 2D spatial channel model (SCM) proposed by 3GPP for performance analysis of long-term evolution (LTE) MIMO. Using a realistic 3D antenna field pattern and the 2D 3GPP SCM, the 3D-to-2D transform algorithm (3D-2D-TF) gives a 5% outage capacity within 0.5 b/s/Hz when compared to a higher complexity averaging approach using 18-cut planes (18-CP) of the 3D radiation pattern. This is achieved with 6% of the run-time complexity. By not including the elevation information, the original SCM 2D model gives an outage capacity prediction error of up to 2.4 b/s/Hz as compared to the 18-CP averaging approach. The 3D-2D-TF is therefore a promising low-complexity candidate that increases the accuracy of 2D channel models for MIMO 4G performance evaluations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".