Joint Imaging and Downlink Communication With Shared Resources
Bibliographic record
Abstract
This paper investigates the fundamental performance trade-offs inherent in joint imaging and communication (JIC) systems that simultaneously utilize shared network resources. Specifically, considering a downlink scenario where a base station illuminates a scene for imaging while concurrently transmitting data to a communication user, four key contributions are presented. First, by isolating the scene scatterers' reflectivity, we formulate the imaging received signal in a structured way that is consistent with the communication signal model. Second, we develop a joint optimization framework to reduce the mean squared error of imaging and communication, thereby maximizing the total system performance. Third, we propose a scene fragmentation approach to reduce the complexity caused by an increasing number of scatterers in the operation environment, which, although improving the scatterers' reflectivity estimation within the scene, can strain the system resources. Finally, we set a theoretical upper limit on the number of scatterers per sub-scene to ensure that the available resources are used optimally with the fragmentation and that the system performance of the JIC is maximized. The benefits of using joint transmit signals for imaging and communication functions within a unified framework are demonstrated using rigorous analysis and simulations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".