CRB-Rate Tradeoff in RSMA-Enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".