4 citations · 7 across the 6 of their papers we have counts for
6 papers
Implementation of an Adaptive Transformer Accelerator for Accurate Outdoor Localization with Massive MIMO
Ilayda Yaman, Sijia Cheng, Ove Edfors +1
We present a sparsity-aware FPGA implementation of an adaptive Transformer-based localization accelerator for 5G massive MIMO targeting sub-10\,ms real-time positioning. The archit…
Multi-User XR Offloading via Massive MIMO: A System-Level Analysis using a Real-Life Dataset
Love Bárány, Ilayda Yaman, Ove Edfors +2
SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and st…
Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization
Ilayda Yaman, Guoda Tian, Dino Pjanic +4
Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that efficiently adapt to varying signal conditions and environmental chang…
LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization
Ilayda Yaman, Guoda Tian, Erik Tegler +7
We present a unique comparative analysis, and evaluation of vision, radio, and audio based localization algorithms. We create the first baseline for the aforementioned sensors usin…
High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System
Guoda Tian, Ilayda Yaman, Michiel Sandra +3
High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine…
The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization
Ilayda Yaman, Guoda Tian, Martin Larsson +11
We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes colo…