Bibliographic record
Abstract
Abstract Multiple‐input multiple‐output (MIMO) technology has been a key driver in the development of fourth‐generation wireless systems and will continue to be utilized in the upcoming fifth‐generation one. MIMO was an excellent technology to make use of multipath effects and enhance the system capacity dramatically. A new technology that is based on MIMO that utilizes large amounts of antennas at the base‐station side to simultaneously send dedicated beams and create dedicated channels for various users has been identified and will be deployed with fifth‐generation systems to enhance the channel capacity even more. This technology is called massive‐MIMO (MaMI). The majority of works that addressed MaMI focused on one dimensional (azimuthal) beam steering. Adding the second dimension (elevation) to the original MaMI systems has been denoted as three‐dimensional MIMO (3D‐MIMO) since the beams will cover the 3D space in front of the MaMI array, thus enhancing coverage, reducing interference and enhancing capacity. This article focuses on 3D‐MIMO antenna array systems, their performance metrics and some examples of actual implementations at microwave and mm‐wave bands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".