Virtual Receiver Matrix and Combinatory Analog Operations for Future Multifunction Reconfigurable Sensing and Communication Wireless Systems
Bibliographic record
Abstract
This article proposes and presents a topological receiver architecture, named virtual receiver matrix (VRM), suitable for future smart multifunction wireless systems. This concept is devised and benefits from using combinatory analog operations with multiple distributed units in a receiver matrix or array. This mechanism of receiving data through spatially “floating” distributed virtual receiver channels offers an unprecedented solution of providing unparalleled degrees of freedom to implement multiple functions such as data reception, angle-of-arrival (AoA) detection, radar, and imaging operations among many others in a single receiver architecture. Interestingly, the total number of possible virtual receivers from different combinations of unit cells in a matrix is also significantly increased compared with a conventional “fixed” receiver array. Each virtual receiver, made of a combinatory set, depends on the characteristics of incoming signals and their illumination angle. A mathematical model is established and investigated for the design of unit cells. Although a prototype of choice is studied and designed for the fifth-generation (5G) wireless systems, it is anticipated that the VRM concept is applicable to the sixth-generation (6G) and future wireless systems for enhancing their functionality, capacity, agility, and speed. In this article, a multiport interferometric technique is used for each unit cell of VRM as a proof-of-concept; however, any other receiver type that uses the phase difference of incoming signals can be deployed as the unit cell for the realization of the VRM concept. First, the experimental results of the fabricated proof-of-concept prototype for various modulation schemes including QPSK, 16-quadrature amplitude modulation (QAM), and 32-QAM are shown with data rates up to 1 MS/s, having a maximum error vector magnitude (EVM) of 9%. Finally, a systematic scheme for 2-D AoA detection using a special combination of unit cells is proposed and demonstrated through the proposed VRM.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".