Cooperative ISAC for Localization and Velocity Estimation Using OFDM Waveforms in Cell-Free MIMO Systems
Bibliographic record
Abstract
In this paper, we present a cooperative integrated sensing and communication (ISAC) framework in cell-free multiple-input multiple-output (MIMO) systems, where multiple access points (APs), under the control of a central processing unit (CPU), collaboratively perform target sensing by using the reflected echo signals. Most of the existing works first estimate the sensing parameters (e.g., range, angle, relative velocity) observed by each AP and then use these estimated parameters for sensing tasks such as localization and velocity estimation. However, this approach may suffer from performance degradation due to errors in the estimated parameters. We propose a deep neural network (DNN)-based scheme to jointly process the echo signals received across the distributed APs and directly estimate the location and velocity of the targets. The proposed scheme bypasses the sensing parameter estimation stage and enhances the sensing performance. Simulation results show that our proposed scheme significantly reduces the localization and velocity estimation error when compared with a state-of-the-art approach.
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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 | 0.002 |
| 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".