ANN‐based design of a versatile millimetre‐wave slotted patch multi‐antenna configuration for 5G scenarios
Bibliographic record
Abstract
This study addresses the modelling of a dual band (28 and 38 GHz), circularly polarised slotted‐patch‐antenna for highly demanded millimetre wave multi‐input multi‐output (MIMO)‐systems in fifth generation (5G) networks. A computer‐aided‐design model is derived by means of an artificial neural network (ANN) which allows obtaining the physical dimensions of a single‐fed antenna, satisfying both near‐ and far‐field goals, without resorting to time‐consuming electromagnetic simulation. This mathematical model can be implemented in any CAD‐tool, as demonstrated within the framework of advanced design system. This allows, for the first time, to carry out optimisations of strategic importance for future 5G non‐linear‐radiating‐systems, especially operating at millimetre wave, directly addressing their far‐field behaviour. The model performance is validated by some examples and measurement results. A further important advantage of this approach is that the trained ANN‐model can be further adopted to fast, but accurately, investigate the complex relationships between antenna layout and its near‐field and far‐field performance, such as the resonance conditions and the polarisation behaviour. Indeed arbitrary orthogonal‐polarisations (LHCP/RHCP) have been achieved by the aid of the ANN‐model of the same topology. This result can be adopted to implement a combination of two independent radiation patterns for the antenna pair: this feature is attractive for MIMO applications. This is confirmed by measurements showing antenna‐coupling reduction with the MIMO‐array exploiting polarisation diversity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".