Multiuser Association and Localization Over Doubly Dispersive Multipath Channels for Integrated Sensing and Communications
Bibliographic record
Abstract
Supporting multiuser communication and localization is a typical scenario in Integrated sensing and communications (ISAC). However, the problem of multi-echo induced by multipath and multiuser makes it hard to determine the relationship between user equipments (UEs) and these echoes. Thus, applying traditional estimation algorithms at the radar receiver inevitably leads to weak communication and localization performances due to the mismatch between echoes and UEs. In this paper, aiming to achieve multiuser association and localization under doubly dispersive multipath channels, we construct an ISAC unified waveform based on the orthogonal delay-Doppler division multiplexing (ODDM) principle and develop an off-grid cluster sparse Bayesian learning estimation (OG-CSBL) algorithm. Particularly, we focus on the mono-static setup, where the base station (BS) expects to communicate with multiuser while sensing their locations. We utilize the high-resolution range profile (HRRP) to characterize the physical features of UEs and establish associations with their echoes by exploiting the inherent cluster structure. To estimate parameters, we design a hybrid Dirichlet process (DP)-Gaussian hierarchical prior distribution and propose a variational Bayesian inference (VBI)-EM strategy. Additionally, we develop a backtrack echo identification scheme to facilitate precise UE localization. Simulation results demonstrate that the proposed scheme achieves superior NMSE performance, offers meter-level localization accuracy, and obtains better BER performance in the complex multiuser coexistence scenario.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".