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.
Code (0)
등록된 구현이 없습니다.
Tasks
Indoor LocalizationVocal Bursts Intensity PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization
The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the…
Image-Based LocalizationIndoor LocalizationLuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization
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 using the recently published Lund University…
Indoor LocalizationSensor FusionOutlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning
In Internet of things (IoT) millions of devices are intelligently connected for providing smart services. Especially in indoor localization environment, that is one of the most concerning topic of smart cities, internet…
BIG-bench Machine LearningEnsemble LearningIndoor LocalizationOutlier DetectionDyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks
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 s…
Time SeriesTime Series AnalysisTowards Fine-Grained Indoor Localization based on Massive MIMO-OFDM System: Experiment and Analysis
Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (M…
Indoor Localization