Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization

Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that can efficiently adapt to varying signal conditions and environmental changes. Factors such as multipath interference and obstructions introduce different levels of complexity that affect the accuracy of the localization. Although generalized models offer broad applicability, they often struggle to capture the nuances of specific environments, leading to suboptimal performance in real-world deployments. In contrast, specialized models can be tailored to particular conditions, enabling more precise localization by effectively handling domain-specific variations and noise patterns. However, deploying multiple specialized models requires an efficient mechanism to select the most appropriate one for a given scenario. In this work, we develop an adaptive localization framework that combines shallow attention-based models with a router/switching mechanism based on a single-layer perceptron (SLP). This enables seamless transitions between specialized localization models optimized for different conditions, balancing accuracy, computational efficiency, and robustness to environmental variations. We design three low-complex localization models tailored for distinct scenarios, optimized for reduced computational complexity, test time, and model size. The router dynamically selects the most suitable model based on real-time input characteristics. The proposed framework is validated using real-world vehicle localization data collected from a massive MIMO base station (BS), demonstrating its ability to seamlessly adapt to diverse deployment conditions while maintaining high localization accuracy.

Indoor Localization Using Radio, Vision and Audio Sensors: Real-Life Data Validation and Discussion

This paper investigates indoor localization methods using radio, vision, and audio sensors, respectively, in the same environment. The evaluation is based on state-of-the-art algorithms and uses a real-life dataset. More specifically, we evaluate a machine learning algorithm for radio-based localization with massive MIMO technology, an ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, and an SFS2 algorithm for audio-based localization with microphone arrays. Aspects including localization accuracy, reliability, calibration requirements, and potential system complexity are discussed to analyze the advantages and limitations of using different sensors for indoor localization tasks. The results can serve as a guideline and basis for further development of robust and high-precision multi-sensory localization systems, e.g., through sensor fusion and context and environment-aware adaptation.

High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System

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 learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.

The LuViRA Dataset: Measurement Description

We present a dataset to evaluate localization algorithms, which utilizes vision, audio, and radio sensors: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes RGB images, corresponding depth maps, IMU readings, channel response between a massive MIMO channel sounder and a user equipment, audio recorded by 12 microphones, and 0.5 mm accurate 6DoF pose ground truth. We synchronize these sensors to make sure that all data are recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot and 88 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. The data from different sensors can be used separately or jointly to conduct localization tasks and a motion capture system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on fusing the most commonly used sensors for localization tasks. However, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Fusing sensor data can lead to increased localization accuracy and reliability, as well as decreased latency and power consumption. The created dataset will be made public at a later date.