Cooperative Sensing and User-Echo Associations for Integrated Sensing and Communication Networks
Bibliographic record
Abstract
Wireless networks are evolving from a communication-only network to one with integrated sensing and communication (ISAC) capabilities. In such cases, the cooperation of multiple base stations (BSs) can be exploited to achieve precise sensing for multiple user equipments (UEs). However, the identities of the UEs are not contained in the echoes, making it difficult for the sensing receiver to associate UEs with their echoes when monostatic and bistatic sensing modes coexist. This leads to a loss of cooperative gain and larger echo interference between BSs, thereby degrading communication and sensing performances. To overcome this challenge and achieve multi-directional sensing of UEs, this paper develops a novel approach for parameter estimation and user-echo association using ISAC signals under doubly dispersive channels. In particular, we establish a model for multiple BSs cooperative sensing of extended UEs utilizing the orthogonal time frequency space (OTFS) signal. Meanwhile, the bistatic angles are introduced as indicators to characterize the correlation between the physical scattering structures of the UE from different directions, simplifying the complex association process. Additionally, we design a parallel off-grid sparse Bayesian learning (SBL) algorithm to estimate unknown parameters iteratively. Simulation results demonstrate that the proposed scheme achieves better NMSE and BER performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".