Integrated Sensing and Communication Beamforming Design With Target Model Aware Antenna Selection
Bibliographic record
Abstract
For high-resolution sensing in integrated sensing and communication (ISAC) systems, the deployment of extra-large antenna arrays (XLAAs) is essential. This, however, renders the traditional point target (PT) model inaccurate. Instead, targets must be considered as having a spatial extent over range and angle, necessitating their modeling as extended targets (ET) for accurate sensing, especially within the near-field propagation region. This shift to ET modeling often entails a significant increase in energy consumption, and reduced sum-rate and increased latency for the communication users compared to the simpler PT model. To address this critical trade-off, this paper proposes an antenna selection strategy for XLAA-based ISAC. By selectively activating antenna elements, the proposed ISAC design aims to maintain effective far-field PT operating conditions, thereby enhancing energy efficiency and communication sum-rate. The optimization ensures the communication quality-of-service by enforcing signal-to-interference-plus-noise power ratio constraints for the communication users, while inherently managing the sensing performance evaluated via the Cramer-Rao bound. This strategy provides a controllable operating point, balancing the ET model’s high sensing accuracy, which comes with higher signal processing time and lower communication sum-rate, against the PT model’s lower sensing accuracy but lower processing time and higher sum-rate. Numerical results validate the proposed approach, demonstrating substantial improvements in energy efficiency and sum-rate over pure ET modeling, achieved at a quantifiable cost in sensing accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".