# Permutation-Invariant Transformer Neural Architectures for Set-Based Indoor Localization Using Learned RSSI Embeddings

arXiv: https://arxiv.org/abs/2506.00656 · HTML: https://mlsanity.com/paper/2506.00656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs](https://mlsanity.com/paper/1811.01900/) (2018)
- [SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data](https://mlsanity.com/paper/2103.15619/) (2021)
- [FSPool: Learning Set Representations with Featurewise Sort Pooling](https://mlsanity.com/paper/1906.02795/) (2019)
- [Recurrent Neural Networks For Accurate RSSI Indoor Localization](https://mlsanity.com/paper/1903.11703/) (2019)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [SeTformer is What You Need for Vision and Language](https://mlsanity.com/paper/2401.03540/) (2024)
- [Semi-Sequential Probabilistic Model For Indoor Localization Enhancement](https://mlsanity.com/paper/2001.02400/) (2020)

## Abstract (arXiv)

We propose a permutation-invariant neural architecture for indoor localization using RSSI scans from Wi-Fi access points. Each scan is modeled as an unordered set of (BSSID, RSSI) pairs, where BSSIDs are mapped to learned embeddings and concatenated with signal strength. These are processed by a Set Transformer, enabling the model to handle variable-length, sparse inputs while learning attention-based representations over access point relationships. We evaluate the model on a dataset collected across a campus environment consisting of six buildings. Results show that the model accurately recovers fine-grained spatial structure and maintains performance across physically distinct domains. In our experiments, a simple LSTM consistently outperformed all other models, achieving the lowest mean localization error across three tasks (E1 - E3), with average errors as low as 2.23 m. The Set Transformer performed competitively, ranking second in every experiment and outperforming the MLP, RNN, and basic attention models, particularly in scenarios involving multiple buildings (E2) and multiple floors (E3). Performance degraded most in E2, where signal conditions varied substantially across buildings, highlighting the importance of architectural robustness to domain diversity. This work demonstrates that set-based neural models are a natural fit for signal-based localization, offering a principled approach to handling sparse, unordered inputs in real-world positioning tasks.