Multi-domain CSI-based Indoor Localization with Deep Attention Networks for MIMO JCAS system
Bibliographic record
Abstract
Indoor localization is essential for future 6G systems that combine communication and sensing. Accurate positioning is vital for applications like augmented reality and autonomous robotics. In this context, we propose a multi-domain channel state information (CSI)-based localization approach using deep attention networks (DAN) in a massive multiple input multiple output-based (maMIMO) system. We introduce the extraction of features from CSI information in multiple domains, including time, frequency, and Doppler, and design uni-domain and multidomain feature sets. We implement the proposed DAN approach leveraging attention mechanisms to integrate and effectively process the multi-domain CSI data. We evaluate the performance of our model using a publicly available maMIMO dataset and compare it with baseline convolutional neural network (CNN) models. Our results indicate that the DAN-based approach enhances localization performance more than uni-domain, multidomain CNN models and also existing multi-domain-based CNN benchmarks. These findings highlight the benefits of using multidomain features, especially from the Doppler domain along with attention mechanisms for reliable indoor localization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".