Multifunction Transceiver for Data Communication, Radar Sensing and Power Transfer
Bibliographic record
Abstract
The future of wireless systems is anticipated to revolutionize human connectivity through a diverse range of applications. The integration of multiple wireless functionalities into a unified system presents a critical challenge due to conflicting requirements in transceiver architecture and signal processing. Recent investigations are directing attention towards the development of systems that serve dual functions, like simultaneous wireless information and power transfer and radar-communication, aimed at boosting operational efficiency and ensuring seamless communication among different wireless capabilities. This review paper aims to discuss the architectural aspects of the integration of radar sensing, data communication, and power transfer. Firstly, the integration of radar sensing and data communication is studied for both cooperating and non-cooperating radar systems with conventional and interferometric architectures. Secondly, the power harvesting approach and internal energy recycling are discussed for the fusion of data communication and energy harvesting. Thirdly, radar sensing and power transfer integration is considered with special focus on harmonic backscattering and self-powered radars. Lastly, a roadmap for next-generation multifunction systems is outlined by considering several scenarios of multiplexing and architectures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".