Concurrent Detection of 2-D Angle-of-Arrival and Polarization Enabled by Virtual Transceiver Matrix Architecture
Bibliographic record
Abstract
This article introduces and demonstrates an integrated concurrent sensing and communication system characterized by multifunctionality and reconfigurability, underpinned by dynamic cell allocation within a topologically distributed matrix. Unlike traditional angle-of-arrival (AoA) or polarization detection systems, this approach offers a vastly expanded range of matrix cell combinations, facilitating the integration of diverse and simultaneous operations in a single transceiver architecture. Our proposed programmable front-end is set to synthesize transmitter (Tx)/receiver (Rx) channels by adapting to the instantaneous characteristics of incoming signals from various sources. We establish and explore a mathematical model of unit cells and conduct modulation tests using 64-/128-quadrature amplitude modulation (QAM), achieving data rates of up to 250 MS/s, with a maximum error vector magnitude (EVM) of 4.56%. The AoA measurements demonstrate an error of approximately 0.9° in a range from −12° to 12°. In addition, the polarization rotation measurements show an error of maximum 2.8° for an entire spanning range of 0°–90°. The results underscore the efficacy of proposed virtual transceiver matrix (VTM) for its possible deployment across a spectrum of applications including base stations and user terminals for next-generation fifth/sixth-generation (5G/6G) communications, sensing systems, and beyond.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".