Radio Map Research Articles

Analysis of the possibilities of determining location in a Wi-Fi network using neural network algorithms

Indoor positioning on a Wi-Fi network belongs to a class of tasks in which the dependence of output characteristics on input variables is influenced by many parameters and external factors. When solving such problems, it is necessary to take into account that in determining the location, it is of significant interest not only to determine the static coordinates of an object, but also to predict the vector of its movements. In the case where the location of an object is determined only by the level of signal power received from several access points on a Wi-Fi network, the use of signal attenuation models that take into account the conditions of propagation of radio waves indoors is difficult due to the need for reliable information about the material of ceilings, floors and ceilings, the presence of fixed and mobile shading objects, etc. Since the electromagnetic environment inside the room varies depending on many factors, the above-mentioned models have to be adjusted to these changes. Since finding patterns in a large amount of data requires non-standard algorithms, artificial neural networks can be used to solve the positioning problem. It is important to choose a neural network architecture that can take into account changes in the signal strength received by a mobile device from Wi-Fi access points. Before training a neural network, statistical data is preprocessed. For example, abnormal cases are excluded from the machine learning dataset when the device detects a signal from less than three access points at one measuring point. As a result of the analysis of statistical data, it was found that the same distance between the measuring points leads to the fact that the neural network incorrectly determines the location of the object. The paper shows that in order to increase the accuracy of positioning the location in conditions of complex radio placement, when compiling radio maps, it is necessary to determine the optimal varying distances between measuring points. The conducted experimental studies, taking into account the proposed approach to optimizing the distances between measuring points, prove that the accuracy of location determination in the vast majority of measuring points reaches 100%.

Channel-feedback-free transmission for downlink FD-RAN: A radio map based complex-valued precoding network approach

Channel-feedback-free transmission for downlink FD-RAN: A radio map based complex-valued precoding network approach

Deep-Learning-Based Radio Map Reconstruction for V2X Communications

Deep-Learning-Based Radio Map Reconstruction for V2X Communications

A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks

Providing global coverage for ubiquitous users is a key requirement of the fifth generation (5G) and beyond wireless technologies. This can be achieved by integrating airborne networks, such as unmanned aerial vehicles (UAVs) and satellite networks, with terrestrial networks. However, the deployment of airborne networks in a three-dimensional (3D) or volumetric space requires a new understanding of the propagation channel and its losses in both the areal and altitude dimensions. Despite significant research on radio environment map (REM) construction, much of it has been limited to two-dimensional (2D) contexts. This neglects the altitude-related characteristics of electromagnetic wave propagation and confines REMs to 2D formats, which limits the comprehensive and continuous visualization of propagation environment variation in spatial dimensions. This paper proposes a volumetric REM (VREM) construction approach to compute 3D propagation losses. The proposed approach addresses the limitations of existing approaches by learning the spatial correlation of wireless propagation channel characteristics and visualizing REM in areal and height/altitude dimensions using deep learning models. Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. In both cases, knowledge of the propagation environment and transmitter locations in 3D space is used to capture the spatial and altitude dependency of the propagation channel’s characteristics. We developed the Addis dataset, a large REM dataset comprising 54,000 samples collected from the urban part of Addis Ababa, Ethiopia, to train the proposed models. Each sample of data comprises a 512-meter by 512-meter areal resolution with different 3D obstacles (buildings and terrain), 15 simulated propagation loss maps at every 3-meter altitude resolution, and 80 different 3D transmitter locations. The results of the training and testing of the proposed models reveal that the constructed VREMs are statistically comparable. In particular, the Vol2Vol approach has a minimum L1 loss of 0.01, which further decreases to 0.0084 as the line-of-sight (LoS) probability increases to 0.95.

Region clustering based fingerprint model for flexible Wi-Fi fingerprinting

Wi-Fi fingerprint, a mainstream technology of Indoor Location Base Service (ILBS), estimates location through a radiomap that places received signal strength (RSS) as AP and location. However, the semi-regularity of the radiomap due to the diversity of the service area limits the fingerprint locally. To solve this problem, in this paper, we propose a Region Clustering based Fingerprint Model that combines a Region Clustering algorithm and a Region Fingerprint network that enables an integrated Wi-Fi fingerprint. The proposed model consists of RCA, which extracts regions from the global radiomap and selects regions, and RFN, which estimates locations regardless of the size of regions. The proposed RCA divides the service area into optimal regions in the offline phase using clustering and standardizes the radio map by matching each area to the same size. Also, it selects a standardized radio map in the online phase and delivers it to the positioning network. In addition, the proposed RFN estimates the location through reference pointwise Bi-LSTM. Through this, the proposed RCF model is capable of flexible fingerprinting in any size of the service area and can provide stable positioning in terms of performance.

Neural architecture search for radio map reconstruction with partially labeled data

In this paper, we tackle the challenging task of reconstructing Received Signal Strength (RSS) maps by harnessing location-dependent radio measurements and augmenting them with supplementary data related to the local environment. This side information includes city plans, terrain elevations, and the locations of gateways. The quantity of available supplementary data varies, necessitating the utilization of Neural Architecture Search (NAS) to tailor the neural network architecture to the specific characteristics of each setting. Our approach takes advantage of NAS’s adaptability, allowing it to automatically explore and pinpoint the optimal neural network architecture for each unique scenario. This adaptability ensures that the model is finely tuned to extract the most relevant features from the input data, thereby maximizing its ability to accurately reconstruct RSS maps. We demonstrate the effectiveness of our approach using three distinct datasets, each corresponding to a major city. Notably, we observe significant enhancements in areas near the gateways, where fluctuations in the mean received signal power are typically more pronounced. This underscores the importance of NAS-driven architectures in capturing subtle spatial variations. We also illustrate how NAS efficiently identifies the architecture of a Neural Network using both labeled and unlabeled data for Radio Map reconstruction. Our findings emphasize the potential of NAS as a potent tool for improving the precision and applicability of RSS map reconstruction techniques in urban environments.

Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones.

Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user's location gradually and precisely. When the user's floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user's position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user's floor change and converts the user's phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained.

The role of environment on the formation of different FR type extragalactic radio sources

We study the environment of nearby extragalactic radio sources of different morphological type from our sample. We chose 30 3C radio galaxies of different FR class for which we have several observational dates on wavelength from radio to X ray. For the study we select the regions with radius of 500 pc around of the parent galaxy of radio sources. We bring the optical maps of these regions that are overlaid on the radio maps and maps in all available wavelength. The preliminary review show that there are some differences in the neighboring regions around radio galaxies of different FR classes.

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

AbstractIndoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix‐based RSSI indoor positioning (M‐RIP) for initial location estimation and feature‐based image indoor positioning (F‐IIP) for position determination via image feature matching. Furthermore, extended area‐based post‐processing (EA‐PP) is employed to augment M‐RIP's precision and minimize image matching computation in F‐IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

Radio Map Reconstruction With Adaptive Spatial Feature Learning

Radio map reconstruction is a fundamental problem of great relevance in numerous real‐world applications, such as network planning and fingerprint localization. Sampling the complete radio map is prohibitively costly in practice and difficult to achieve. Such methods for reconstructing radio maps from a subset of measurements are now gaining additional attention. In this paper, we first explore the spatial features of signals on the radio map and formulate the reconstruction problem as an optimization problem with feature penalties. Then, we propose an iteration algorithm with spatial feature learning to reconstruct signals on the radio map, which improves the reconstruction accuracy by using an adaptive feature dictionary. Numerical examples are given to demonstrate the viability and performance of our method at last.

Optimal Trajectory Design for Unmanned Aerial Vehicle Cargo Pickup and Delivery System based on Radio Map

Optimal Trajectory Design for Unmanned Aerial Vehicle Cargo Pickup and Delivery System based on Radio Map

Energy-Modified Leverage Sampling for Radio Map Construction via Matrix Completion

Energy-Modified Leverage Sampling for Radio Map Construction via Matrix Completion

Enhancing Indoor Positioning Systems Accuracy with Optimal Placement of Wi-Fi Access Points

The indoor positioning system (IPS) has generated a considerable amount of interest in recent times, and the prosperity of the system's development is heavily reliant on its ability to accurately locate objects. The performance measure is significantly impacted by the location of access points (APs). However, the bulk of previous studies have tended to overlook the matter of optimal AP placement and efficient design for IPS due to the dependence on pre-existing installed APs, which were chiefly formulated for coverage objectives. In this investigation, an optimal placement function - which is reliant on mean and variance - has been developed using received signal strength (RSS) measurements data. The performance evaluation in this research is based on experimentation and compared with currently employed placement methods. The results indicate that the most optimal function value for the suggested method is 1.5714, which is substantially smaller than the values for rectangular, triangular, and triangular II, which are 12.468, 5.5364, and 8.5147, respectively. When the recommended placement strategy is employed instead of the existing ones, the weighted K-nearest algorithm (WKNN) for location error, using average RSS as the fingerprint radio map database, yielded a heightened degree of precision.

Compressed Tensor Completion: Approach for UAVAided 3D Radio Map Construction

Compressed Tensor Completion: Approach for UAVAided 3D Radio Map Construction

Constructing Indoor Region-Based Radio Map Without Location Labels

Constructing Indoor Region-Based Radio Map Without Location Labels

Energy Minimization for Federated Learning Based Radio Map Construction

Energy Minimization for Federated Learning Based Radio Map Construction

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning

Automatic Radio Map Adaptation for Robust Indoor Localization With Dynamic Adversarial Learning

Automatic Radio Map Adaptation for Robust Indoor Localization With Dynamic Adversarial Learning

Joint Optimization of Sensor Deployment and Spectrum Data Completion for Radio Map Construction

Joint Optimization of Sensor Deployment and Spectrum Data Completion for Radio Map Construction

Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction

Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction