Related papers: Fast Radio Map Construction and Position Estimatio…

Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety,…

In this paper, we introduce an uplink optical wireless positioning system for indoor applications. This technique uses fingerprints based on the indoor optical wireless channel impulse response for localization. Exploiting the line of sight…

Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely used indoor localization approach due to its compatibility…

With the increasing demand for edge device powered location-based services in indoor environments, Wi-Fi received signal strength (RSS) fingerprinting has become popular, given the unavailability of GPS indoors. However, achieving robust…

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with…

This paper presents a novel indoor positioning approach that leverages antenna radiation pattern characteristics through Received Signal Strength Indication (RSSI) measurements in a single-antenna system. By rotating the antenna or…

Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map,…

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints,…

In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a…

Indoor navigation remains a complex challenge due to the absence of reliable GPS signals and the architectural intricacies of large enclosed environments. This study presents an indoor localization and navigation approach that integrates…

Indoor wireless simultaneous localization and mapping (SLAM) is considered as a promising technique to provide positioning services in future 6G systems. However, the accuracy of traditional wireless SLAM system heavily relies on the…

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and…

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced…

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions,…

RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate people without requiring them to carry or wear any electronic device. Current models…

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by…

An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and…

This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination…

Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards spend up to several milliseconds exploring different sector…

The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms…