# DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2101.07848 · HTML: https://mlsanity.com/paper/2101.07848/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 19 Jan 2021.

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

- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Dual Motion GAN for Future-Flow Embedded Video Prediction](https://mlsanity.com/paper/1708.00284/) (2017)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning](https://mlsanity.com/paper/1708.06235/) (2017)
- [Deep Frame Prediction for Video Coding](https://mlsanity.com/paper/1901.00062/) (2018)
- [DNN-based Localization from Channel Estimates: Feature Design and Experimental Results](https://mlsanity.com/paper/2004.00363/) (2020)
- [MaMIMO CSI-based positioning using CNNs: Peeking inside the black box](https://mlsanity.com/paper/2003.04581/) (2020)

## Built on by (most cited first, 4 of 4)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Vision Transformer Based User Equipment Positioning](https://mlsanity.com/paper/2511.08549/) (2025)
- [OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments](https://mlsanity.com/paper/2606.11490/) (2026)
- [CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models](https://mlsanity.com/paper/2607.14975/) (2026)

## Abstract (arXiv)

This paper presents a data-driven localization framework with high precision in time-varying complex multipath environments, such as dense urban areas and indoors, where GPS and model-based localization techniques come short. We consider the angle-delay profile (ADP), a linear transformation of channel state information (CSI), in massive MIMO systems and show that ADPs preserve users' motion when stacked temporally. We discuss that given a static environment, future frames of ADP time-series are predictable employing a video frame prediction algorithm. We express that a deep convolutional neural network (DCNN) can be employed to learn the background static scattering environment. To detect foreground changes in the environment, corresponding to path blockage or addition, we introduce an algorithm taking advantage of the trained DCNN. Furthermore, we present DyLoc, a data-driven framework to recover distorted ADPs due to foreground changes and to obtain precise location estimations. We evaluate the performance of DyLoc in several dynamic scenarios employing DeepMIMO dataset to generate geo-tagged CSI datasets for indoor and outdoor environments. We show that previous DCNN-based techniques fail to perform with desirable accuracy in dynamic environments, while DyLoc pursues localization precisely. Moreover, simulations show that as the environment gets richer in terms of the number of multipath, DyLoc gets more robust to foreground changes.