Full-Order Tensor-Based Parameters Estimation and Channel Reconstruction for Heterogeneous Bi-Static mmWave ISAC
Bibliographic record
Abstract
Integrated sensing and communication (ISAC), as a rapidly advancing technique, is proposed to address both the communication and sensing problem in a unified framework. In this paper, a cost-saving heterogeneous bi-static architecture is designed for the millimeter wave (mmWave) ISAC system, where both communications terminals (CTs) and passive targets (PTs) are jointly considered. Based on the proposed architecture, an effective estimation scheme is proposed utilizing the canonical polyadic (CP) decomposition. Specifically, the estimation of cascaded and uplink channels are formulated as high-order tensor recovery problems, where the channels are modeled as full-order (FO) CP-form tensors. Then, a novel reconstruction scheme is provided to reduce the downlink pilot overhead. A more applicable generic uniqueness condition is proposed based on the permutation lemma and redundant minors. Additionally, the Cramer-Rao lower bound (CRLB) based on the FO model is derived. Numerical results verify that the proposed estimation and reconstruction schemes outperform both the classic monostatic counterpart in terms of architecture and current tensor-based joint AoD and Doppler shift estimation (JADE) in terms of algorithm. The reconstruction scheme with reduced in downlink pilot overhead approaches the estimation scheme in terms of estimation performance.
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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 | 0.000 | 0.001 |
| 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".