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Received Signal Strength (RSS) is considered as a promising measurement for indoor positioning. Lots of RSS-based localization methods have been proposed by its convenience and low cost. This paper focuses on two challenging issues in…
In this paper, we aim to determine the location information of a node deployed in Wireless Sensor Networks (WSN). We estimate the position of an unknown source node using localization based on linear approach on a single simulation…
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate…
Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and…
Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the…
This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The…
This paper reports the findings of an experimental study on the problem of line-of-sight (LOS)/non-line-of-sight (NLOS) classification in an indoor environment. Specifically, we deploy a pair of NI 2901 USRP software-defined radios (SDR) in…
This paper presents a novel indoor positioning method designed for residential apartments. The proposed method makes use of cellular signals emitting from a serving eNodeB which eliminates the need for specialized positioning…
This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the…
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…
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine learning generally requires a large number of ground-truth…
Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which…
The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy degradation and are almost unavailable in indoor environments. Indoor positioning systems (IPS) based on WiFi signals have been gaining popularity. However, owing…
Neural network (NN) emulators of the global 21 cm signal need emulation error much less than the observational noise in order to be used to perform unbiased Bayesian parameter inference. To this end, we introduce $\texttt{21cmLSTM}$ -- a…
Radio frequency (RF)-based indoor localization offers significant promise for applications such as indoor navigation, augmented reality, and pervasive computing. While deep learning has greatly enhanced localization accuracy and robustness,…
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions…
The accuracy of smartphone-based positioning methods using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several statistical features from a long time series…
Locating mobile devices precisely in indoor scenarios is a challenging task because of the signal diffraction and reflection in complicated environments. One vital cause deteriorating the localization performance is the inevitable power…
Indoor localization systems are most commonly based on Received Signal Strength Indicator (RSSI) measurements of either WiFi or Bluetooth-Low-Energy (BLE) beacons. In such systems, the two most common techniques are trilateration and…
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…