CRB-Rate Tradeoff in RSMA-Enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Near-field integrated sensing and communication (NF-ISAC) combines wireless communication with simultaneous angle and distance sensing. However, this dual functionality creates a complex interference environment. Existing NF-ISAC designs offer limited flexibility in managing such interference, highlighting the need for more advanced strategies. To address this, we propose a rate-splitting multiple access (RSMA) scheme for NF-ISAC, leveraging both fully and partially connected hybrid analog-digital (HAD) beamforming architectures. We derive the Cramér-Rao bound (CRB) for joint distance and angle sensing and characterize the tradeoff between the max-min communication rate and multi-target CRB. To explore the CRB-rate Pareto boundary, we formulate a sensing-centric optimization problem under communication rate constraints. For the fully connected HAD architecture, a penalty dual decomposition (PDD)-based double-loop algorithm is developed, while a two-stage approach is used to reduce complexity. This framework is also extended to the partially connected case. Simulations show that the proposed schemes achieve performance comparable to a fully digital beamformer with fewer RF chains, effectively balancing hardware efficiency and system performance. Furthermore, the schemes significantly outperform space division multiple access and far-field ISAC, delivering lower sensing error and an expanded CRB-rate region.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it