Millimeter-wave 39 GHz 4x4 Phased Antenna Array with Embedded Near-field Probing Antenna for Performance Enhancement
Bibliographic record
Abstract
Beamforming arrays are a vital technology for millimeter-wave (mm-wave) wireless communication systems. A large amount of research and an increasing number of phased array prototypes have been developed which provide high data rates and low latency. However, as sub-6 GHz bands have limited bandwidth and crowded spectrum occupation, mm-wave phased arrays are more desirable for future generation systems. Yet, there are challenges with current phased array technologies that need to be addressed by researchers. Linear power amplifier response, radio frequency front-end calibration, and affordable prototyping costs are the main challenges that must be taken into consideration when designing arrays for mm-wave systems. \n \nThis thesis presents approaches to these challenges and trade-offs and proposes an optimized design solution. A novel wideband near-field (NF) probing antenna is proposed that can be embedded in mm-wave phased arrays to provide the feedback signal path necessary for carrying out array calibration and digital pre-distortion training. Specifically, the topology and layout of the proposed NF probing antenna have been carefully devised to achieve a coupling to its surrounding antenna elements with flat magnitude and constant group delay. A proof-of-concept 4x4 linear-polarized active phased array prototype with embedded NF probing antennas is designed to operate over 37-41 GHz. Measurements confirm the negligible impact of the NF probing antennas on the antenna array's radiation pattern, and the array prototype was successfully used to train a digital pre-distortion function which enabled the effective isotropic radiated power to be increased from 32 dBm to 34.3 dBm, while maintaining an error vector magnitude below 3.5%, when the array was steered from -50 degrees to +50 degrees.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".