Orthogonal Coded Active Illumination for Millimeter Wave, Massive-MIMO Computational Imaging With Metasurface Antennas
Bibliographic record
Abstract
Emerging metasurface antenna technology enables flexible and low cost massive multiple-input multiple-output (MIMO) millimeter-wave (mmW) imaging for applications such as personnel screening, weapon detection, reconnaissance, and remote sensing. This work proposes an orthogonal coded active illumination (OCAI) approach which utilizes simultaneous, mutually orthogonal coded transmit signals to illuminate the scene being imaged. It is shown that OCAI is robust to code amplitude and code phase imbalance introduced by imperfect transmitter (TX) and receiver (RX) hardware, while also mitigating common impairments of low cost direct-conversion receivers, such as RX self-jamming and DC offsets. The coding gain offered by this approach improves imager signal to noise ratio performance by up to 15 dB using codes of symbol length 32. We present validation images of resolution targets and a human-scale mannequin, obtained with a custom massive-MIMO mmW imager having 24 simultaneous TXs and 72 simultaneous RXs operating in the K-band (17.5 GHz to 26.5 GHz). The imager leverages both spatial coding via frequency diverse metasurface antennas, and temporal coding via OCAI of the scene.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".