Transformer-Based Multi-Modal Indoor Localization in RIS-Assisted Wireless Networks
Bibliographic record
Abstract
This paper introduces a Transformer-based framework for high-precision indoor localization in 6G-enabled Internet of Things (IoT) environments, enhanced by reconfigurable intelligent surfaces (RISs). The proposed system integrates multiple signal modalities, including channel state information (CSI), received signal strength (RSS), geometric data, and adjustable RIS phase shifts, into a unified deep learning model. A multi-head self-attention Transformer is employed to capture the spatial-temporal dependencies inherent in indoor signal propagation, enabling reliable estimation of user coordinates even under challenging multipath and non-line-of-sight (NLoS) conditions. The RIS is further optimized through controlled phase shift strategies to enhance both localization accuracy and signal quality. We conduct comprehensive simulations that model real-world environmental conditions, including Rayleigh fading and varying signal-to-noise ratio (SNR) levels. Results indicate that the proposed framework significantly outperforms traditional localization benchmarks, achieving sub-meter accuracy while reducing training complexity and enhancing scalability. Moreover, the model is robust against channel estimation errors and supports real-time inference, making it ideal for smart building applications. This framework lays the groundwork for future wireless systems that demand intelligent and geometry-aware 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".