Transformer That Reads Missing Wi-Fi Signals Cuts Indoor Positioning Errors

GPS is a marvel outdoors, but the moment you step into a hospital, a factory floor, a shopping mall, or an airport terminal, satellite signals scatter and fade against concrete and steel. Indoors, engineers have long relied on Wi-Fi signals as a substitute positioning system, measuring how strongly a smartphone hears each nearby access point and matching that pattern, or fingerprint, against a pre-built radio map. The trouble is that these fingerprints are messy. Walls absorb and reflect signals, people moving through a corridor change the readings, different phone models report different strengths for the same spot, and many access points go undetected altogether. A new study published in Frontiers in Signal Processing argues that the missing signals themselves are information, and that a transformer built to read them can locate a device more accurately than a long list of established alternatives.

The system, called IndoorCART, short for Indoor Context-Aware RSSI Transformer, was developed by Balaram Singh, Rajdeep Chatterjee, Mahendra Kumar Gourisaria, Manoj Sahni, and Ernesto León-Castro, with the first three authors based at KIIT Deemed to be University in India. It is an encoder-only transformer, the same family of architecture that underpins modern language models, but retooled for a very different kind of data. Instead of words, the model receives tokens representing the received signal strength indicator, or RSSI, from each Wi-Fi access point in a building. Each token is paired with a learnable identity embedding so the network knows which access point it is hearing, without imposing any artificial order on the measurements. Because Wi-Fi fingerprints have no natural sequence, the authors argue that self-attention, which lets every token interact with every other token in parallel, is a better fit than recurrent or convolutional designs that assume locality or time.

The most distinctive piece of the architecture is what the researchers call Adaptive Attention. In a typical fingerprint, only a fraction of the access points are actually detected; on the widely used UJIIndoorLoc benchmark, more than 62 percent of the possible measurements are missing. Conventional pipelines either fill these gaps with placeholder constants, which can masquerade as genuine weak signals, or discard the missing entries entirely, throwing away the spatial hints that the pattern of absence carries. IndoorCART does neither. Every undetected access point is assigned a learnable absent-signal embedding, a shared null state that tells the encoder the signal was not observed rather than weakly received. A custom attention mask then down-weights interactions involving absent tokens, and a density-adaptive dropout scheme increases regularization when few signals are visible, protecting the model from overfitting to sparse observations. The authors are careful to frame this as a domain-specific adaptation of standard scaled dot-product attention rather than a wholly new attention mechanism, and they note that a uniform null embedding is a conservative choice because the benchmark datasets do not record the physical locations of the access points.

After the encoder stacks have contextualized the tokens, IndoorCART aggregates them with a hybrid strategy that combines a learnable CLS token, which summarizes global fingerprint context, with global average pooling across all access-point tokens, which preserves distributed spatial information. A regression head then maps this representation directly to continuous coordinates. That last choice matters more than it might seem. Many recent transformer-based localization systems frame the problem as classification over discrete reference points, predicting which survey location a fingerprint belongs to and reporting errors relative to label centroids. Direct coordinate regression is a harder task, because the model must predict positions anywhere in the physical space and is scored against true coordinates in meters, but it is also the formulation that matters for real deployments.

The evaluation is unusually thorough for this field. The team compared IndoorCART against nine reproducible regression baselines, including linear regression, k-nearest neighbors, support vector regression, random forests, XGBoost, multilayer perceptrons, one-dimensional convolutional networks, LSTMs, and GRUs, all trained under identical preprocessing, splits, and metrics. They also reimplemented six recent transformer-based localization systems, including AATS, the L-SwiGLU Transformer, HyTra, VTIL, VITAL, and a Siamese-encoder transformer, under the same controlled protocol. Across three public benchmarks, IndoorCART achieved mean absolute errors of 4.79 meters on UJIIndoorLoc, 0.18 meters on TUJI1, and 1.33 meters on SODIndoorLoc, with coefficients of determination of 0.99, 0.98, and 0.99 respectively. On SODIndoorLoc, a sprawling multi-building dataset collected at Shandong Jianzhu University in China, the improvement over the strongest reproduced transformer baseline, HyTra, reached nearly 30 percent.

The ablation studies show where the gains come from. Swapping the transformer encoder for an MLP, a CNN, or a BiLSTM increased error by roughly 12 to 30 percent across the benchmarks, confirming that global self-attention captures cross-access-point structure that local or sequential models miss. Removing the adaptive components entirely, meaning the absent-signal embeddings, the custom masking, and the density-adaptive dropout, raised the mean error from 4.79 to 5.67 meters on UJIIndoorLoc and from 1.33 to 1.89 meters on SODIndoorLoc. A worst-case analysis using the 95th-percentile error showed that adaptive masking also reduces severe localization failures, not just average error, which matters for safety-critical applications such as emergency response inside buildings.

Notably, the authors resist the temptation to oversell. On UJIIndoorLoc, the margin over a simple k-nearest-neighbor regressor was only 0.11 meters, a 2.24 percent relative improvement, and several baselines matched the same rounded coefficient of determination. The team attributes this to the dataset’s dense sampling, with more than fifteen fingerprints per location, and its strong local spatial autocorrelation, which lets similarity-based methods perform near saturation. They even estimate a theoretical error floor of about 4.45 meters for the dataset given its low signal-to-noise ratio, suggesting IndoorCART is already close to what the data can support. Similarly, the striking 0.18-meter result on TUJI1 comes with an explicit caveat: because the study used the dataset’s fixed split without enforcing device-disjoint or location-disjoint testing, the figure should be read as interpolation performance under that split, not as proof of generalization to unseen devices or reference points.

The paper also tackles an uncomfortable gap between its own numbers and those reported elsewhere in the literature. Several published transformer systems claim sub-meter errors on UJIIndoorLoc, roughly an order of magnitude below IndoorCART’s 4.79 meters. The authors identify six factors behind the discrepancy, including classification-based formulations that cannot localize between reference points, smaller proprietary testbeds, cross-validation protocols that can yield optimistic estimates, and differing error metrics. When they reproduced HyTra, L-SwiGLU, and VITAL under their own standardized protocol, none matched IndoorCART, and the much-cited 0.355-meter result for L-SwiGLU could not be reproduced on the full UJIIndoorLoc test set. The comparison, they stress, positions IndoorCART as the strongest model within this controlled study rather than as a universal state-of-the-art claim.

Deployment practicality receives its own analysis. The base model carries 8.2 million parameters, occupies 32.8 megabytes, and infers a position in 0.234 milliseconds, well within real-time requirements and, remarkably, using only 0.08 gigabytes of RAM at inference because it does not store the training data the way k-nearest-neighbor and random-forest methods must. Pruning and INT8 quantization shrink the model to 5.7 megabytes and cut inference time to 0.052 milliseconds with only a 2.7 percent accuracy penalty, opening the door to edge and mobile deployment. The authors recommend the full model for sparse or structurally complex environments, simpler methods for dense, well-structured radio maps, and hybrid local-global schemes as a promising middle ground.

The broader significance is twofold. For engineers, IndoorCART offers a template for treating missingness as a first-class signal in tabular sensing problems, a lesson that likely extends beyond Wi-Fi to Bluetooth, ultra-wideband, and emerging 6G integrated sensing systems. For the research community, the paper is a case study in honest evaluation: it distinguishes controlled reimplementations from literature-reported numbers, quantifies uncertainty with bootstrap confidence intervals and paired statistical tests, and states plainly where its advantages shrink. As demand grows for centimeter-to-meter accuracy in the GPS-denied spaces where people actually live and work, frameworks that combine architectural adaptation with this level of methodological candor may prove as influential as the accuracy numbers themselves.

Subject of Research: A transformer-based framework for Wi-Fi fingerprinting indoor localization that models missing RSSI signals

Article Title: IndoorCART: a spatially-aware transformer framework for Wi-Fi fingerprinting indoor localization

Article References: Singh, B., Chatterjee, R., Gourisaria, M. K., Sahni, M., & León-Castro, E. (2026). IndoorCART: a spatially-aware transformer framework for Wi-Fi fingerprinting indoor localization. Frontiers in Signal Processing, 6, Article 1884243. https://doi.org/10.3389/frsip.2026.1884243

Image Credits: AI Generated

DOI: 10.3389/frsip.2026.1884243

Keywords: indoor localization, Wi-Fi fingerprinting, transformer, RSSI, self-attention, coordinate regression, UJIIndoorLoc, missing data, Adaptive Attention, deep learning, positioning, signal processing