Radio Environment Map Research Articles

Enhancing Indoor Localization Accuracy through Multiple Access Point Deployment

This study addresses the limitations of wireless local area networks in indoor localization by utilizing Extra-Trees Regression (ETR) to estimate locations based on received signal strength indicator (RSSI) values from a radio environment map (REM). We investigate how integrating numerous access points can enhance indoor localization accuracy. By constructing an extensive REM using RSSI data from various access points collected by a mobile robot in the intended interior setting, we evaluate several machine learning regression techniques. Our research pays special attention to an optimized ETR model, validated through 10-fold cross-validation and hyperparameter tuning. We quantitatively evaluate the efficiency of our suggested multi-access-point approach using root mean square error (RMSE) for REM evaluation and location error metrics for accurate localization. The results show that incorporating multiple access points significantly improves indoor localization accuracy, providing a substantial improvement over single-access-point systems when assessing interior radio frequency environments.

Cognitive radio network architecture for GEO and LEO satellites shared downlink spectrum

The fixed spectrum assignment policy in the space sector and large constellations of Low Earth Orbit (LEO) satellites left little or no spectrum available for future LEO satellite communications services. Cognitive Radio (CR) technology enables spectrum sharing between primary and secondary users without limiting the transmission power, and thus is of great interest to commercial and defense entities. A number of Radio Environment Map (REM) techniques have been making Cognitive Radio Networks (CRNs) practical by constructing a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. In this paper, we investigate spectrum sharing for a network comprised of a Geostationary Orbit (GEO) and a LEO satellite with a multibeam antenna array. A CRN architecture of GEO and LEO satellites shared downlink spectrum is proposed and details are provided covering its architecture, REM structure and channel utilization data aggregation, as well as a frequency slot assignment mechanism.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression.

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

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.

Radio Environment Map Based Inter-Cell Interference Coordination for Massive-MIMO Systems

Massive-MIMO (M-MIMO) allows to schedule users with high gain narrow beams and to reduce intra-cell inter-beam interference. Close to cell edge, users may experience high interference from beams of neighboring cells which degrades their performance. This paper introduces low complexity inter-cell interference coordination between neighboring cells for M-MIMO systems implementing Grid of Beams (GoB) using Radio Environment Maps (REMs). The REMs are designed using the Kriging with the Covariance Tapering spatial interpolation technique and are created for the serving and the interfering beams. The interference coordination is introduced as constraints to the schedulers that avoid simultaneous scheduling of users served by highly interfering beams from the neighboring cells. The coordination decision is based on information retrieved from REMs and its quality depends on the REMs' precision. Around 72 percent performance gain in terms of mean user throughput is achieved by the REM based coordination with respect to a baseline solution without coordination, and around 40 percent gain with respect to a state of the art solution that implements coordination using information at beam level resolution.

Multisubgrid Cylindrical Parabolic Equation Model for Radio Environment Map Generation in a Large-Scale Region

Multisubgrid Cylindrical Parabolic Equation Model for Radio Environment Map Generation in a Large-Scale Region

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Limited Sampling Spatial Interpolation Evaluation for 3D Radio Environment Mapping.

The increasing densification and diversification of modern and upcoming wireless networks have become an important motivation for the development of agile spectrum sharing. Radio environment maps (REMs) are a basic tool for spectrum utilisation characterisation and adaptive resource allocation, but they need to be estimated through accurate interpolation methods. This work evaluated the performance of two established algorithms for spatial three-dimensional (3D) data collected in two real-world scenarios: indoors, through a mechanical measuring system, and outdoors, through an unmanned aerial vehicle (UAV) for measurement collection. The investigation was undertaken for the complete dataset on two-dimensional (2D) planes of different altitudes and for a subset of limited samples (representing the regions of interest or RoIs), which were combined together to describe the spatial 3D environment. A minimum error of -9.5 dB was achieved for a sampling ratio of 21%. The methods' performance and the input data were analysed through the resulting Kriging error standard deviation (STD) and the STD of the distances between the measurement and the estimated points. Based on the results, several challenges for the interpolation performance and the analysis of the spatial RoIs are described. They facilitate the future development of 3D spectrum occupancy characterisation in indoor and UAV-based scenarios.

REM-Based Indoor Localization with an Extra-Trees Regressor

As a widely established and accessible infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization for both mobile and stationary users. However, WLANs present several challenges that must be fulfilled to achieve localization based on Wi-Fi signals and to obtain proper coverage prediction maps. This paper presents a study based on the application of extra-trees regression (ETR) for indoor localization using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected signal strength indicator (RSSI) values collected by a mobile robot. Our methodology consists of utilizing the RSSI collected values to construct the REM, which is then leveraged to create a dataset for indoor localization. This process involves tracking a user’s movements within a specific area of interest while considering a single access point. The proposed scheme explores various machine learning (ML) regression algorithms, with hyperparameter tuning carried out to optimize their performance through 10-fold cross-validation. To assess the REM, we employed metrics, such as the root mean square error, absolute error, and R-squared error. Additionally, we evaluated the indoor localization accuracy using location error metrics. Among the ML techniques assessed, our proposed ETR-based approach demonstrates the highest performance based on these error metrics. The combination of generating coverage maps and utilizing regression techniques for localization presents a potent approach for analyzing the radio frequency environment in indoor spaces.

Detection of Transmission State of Multiple Wireless Sources: A Statistical Mechanics Approach

Consider a random network of static primary wireless sources and a co-located network of secondary wireless devices. The channel coefficients between the two networks are assumed to be known to the secondary users (SUs), e.g., using radio environment maps (REM). However, the operational state of the sources is unknown due to intermittency. In this paper, we study the performance of primary source detection by SUs using a message-passing algorithm. Additionally, we employ methods from statistical mechanics, in particular, the Replica approach, to obtain analytic results for the performance of such networks in the large system-size limit. We test the results through a large-scale simulation analysis, obtaining good agreement. The proposed method provides a simple way to evaluate the performance of the system and assess how it depends on the macroscopic parameters that characterize it, such as the average density of SUs and sources and the signal-to-noise ratio. The main contribution of this paper is the application of an algorithm that quantitatively predicts the parameter value region for which accurate and reliable detection of the operational state of the primary sources can be achieved in a fast and decentralized manner.

Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks.

The management of cellular networks, particularly within the environment rapidly advancing to 6G, presents considerable challenges due to the highly dynamic radio environment. Traditional tools such as Radio Environment Maps (REMs) have proven inadequate for real-time network changes, underlining the need for more sophisticated solutions. In response to these challenges, this work introduces a novel approach that harnesses the unprecedented power of state-of-the-art image classifiers for network management. This method involves the generation of Network Synthetic Images (NSIs), which are enriched heat maps that precisely reflect varying cellular network operating states. Created from user location traces linked with Key Performance Indicators (KPIs), NSIs are strategically designed to meet the intricate demands of 6G networks. This research delves deep into a comprehensive analysis of the diverse factors that could potentially impact the successful application of this methodology in the realm of 6G. The results from this investigation, coupled with a comparative assessment against traditional REM usage, emphasize the superior performance of this innovative method. Additionally, a case study involving an automatic network diagnosis scenario validates the effectiveness of this approach. The findings reveal that a generic Convolutional Neural Network (CNN), one of the most powerful tools in the arsenal of modern image classifiers, delivers enhanced performance, even with a reduced demand for positioning accuracy. This contributes significantly to the real-time, robust management of cellular networks as we transition into the era of 6G.

Radio environment maps for indoor visible light communications aided by machine learning

VLC has been proposed as reliable high-capacity wireless optical communication system for indoor access networks. This technology comprises a high bandwidth unlicensed solution and has been suggested for ultra-reliable environments like hospitals. In this study, Machine Learning is used to investigate indoor wireless optical channels where ultra-reliable communications and signal coverage are required. Specifically, we assess the precision of different Decision Trees ML algorithms based on their mean absolute error metric in comparison to the anticipated simulated outcomes for optical channel estimation in various rooms that could be used in medical care. We demonstrate that Decision Trees models are capable of making fast and accurate predictions. It is shown that the Stack model is the most appropriate Decision Trees-based method, from the ones we studied, for predicting indoor VLC RSS values and constructing the corresponding REMs. These models are 300–65,000 times faster than traditional calculation methods, depending on the number of reflections bounces considered, and deliver accurate predictions, with less than 0.015 mean absolute percentage error, of the Radio Environment Map of a communication system that supports VLC. This is the first implementation of constructing optical REMs for Indoor VLC systems to the best knowledge of the authors.

Automatic Clustering for Improved Radio Environment Maps in Distributed Applications

Wireless communication greatly contributes to the evolution of new technologies, such as the Internet of Things (IoT) and edge computing. The new generation networks, including 5G and 6G, provide several connectivity advantages for multiple applications, such as smart health systems and smart cities. Adopting wireless communication technologies in these applications is still challenging due to factors such as mobility and heterogeneity. Predicting accurate radio environment maps (REMs) is essential to facilitate connectivity and improve resource utilization. The construction of accurate REMs through the prediction of reference signal received power (RSRP) can be useful in densely distributed applications, such as smart cities. However, predicting an accurate RSRP in the applications can be complex due to intervention and mobility aspects. Given the fact that the propagation environments can be different in a specific area of interest, the estimation of a common path loss exponent for the entire area produces errors in the constructed REM. Hence, it is necessary to use automatic clustering to distinguish between different environments by grouping locations that exhibit similar propagation characteristics. This leads to better prediction of the propagation characteristics of other locations within the same cluster. Therefore, in this work, we propose using the Kriging technique, in conjunction with the automatic clustering approach, in order to improve the accuracy of RSRP prediction. In fact, we adopt K-means clustering (KMC) to enhance the path loss exponent estimation. We use a dataset to test the proposed model using a set of comparative studies. The results showed that the proposed approach provides significant RSRP prediction capabilities for constructing REM, with a gain of about 3.3 dB in terms of root mean square error compared to the case without clustering.

A REM Update Methodology Based on Clustering and Random Forest

In this paper, we propose a radio environment map (REM) update methodology based on clustering and machine learning for indoor coverage. We use real measurements collected by the TurtleBot3 mobile robot using the received signal strength indicator (RSSI) as a measure of link quality between transmitter and receiver. We propose a practical framework for timely updates to the REM for dynamic wireless communication environments where we need to deal with variations in physical element distributions, environmental factors, movements of people and devices, and so on. In the proposed approach, we first rely on a historical dataset from the area of interest, which is used to determine the number of clusters via the K-means algorithm. Next, we divide the samples from the historical dataset into clusters, and we train one random forest (RF) model with the corresponding historical data from each cluster. Then, when new data measurements are collected, these new samples are assigned to one cluster for a timely update of the RF model. Simulation results validate the superior performance of the proposed scheme, compared with several well-known ML algorithms and a baseline scheme without clustering.

Gaussian mixture model based radio environment mapping for an underlay Device‐to‐Device network

AbstractEfficient management of the scant radio resources in densely deployed Device‐to‐Device (D2D) and IoT networks in 5G+ and 6G wireless era would require continuous and reliable information feedback regarding the actual on‐ground resource utilization. Toward that goal, radio environment maps (REM) are becoming increasingly important as they provide critical information relevant for network management. REM essentially entails the estimation of the radio environment at unsampled locations using the values measured by a few dedicated REM sensors by interpolation. Hence, the accuracy of REM is dependent upon two major factors (1) the sensor deployment and (2) the interpolation scheme. So far, the ordinary Kriging (OK) with randomly deployed sensors is the most prevalent method of REM creation. However, the underlying assumption in OK is that the radio environment conforms to a Gaussian spatial random field (GSRF). In reality, the spatial random field generated by a D2D network displays non‐Gaussian characteristics. Hence, a variational Bayesian based Gaussian mixture model enabled Kriging (VK) method has been proposed in this work which achieves better interpolation accuracy than OK with a lower run‐time computational complexity. A comparison of various sensor deployment schemes on the basis of REM accuracy in fading channel conditions has also been conducted. Additionally, a spatial‐correlation based method has been proposed to estimate the minimum number of required REM sensors to ensure reliable REM performance. The simulation results indicate a superiority of hexagonal deployment of sensors over other schemes and establish VK as a more accurate interpolation algorithm than OK.

Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction

In mobile communications network planning (and designing any radio system), coverage prediction helps network operators optimize cellular networks to improve customer experience. Accordingly, several path-loss models have been proposed that depend on many conditions, such as suitable selection of the terrain for each model, the height of the receiver and transmitter above ground and the distance between them, and the presence of obstacles. This may increase the prediction error between actual and estimated values, which change according to the propagation model selected. To overcome these problems, we propose a novel approach to mobile coverage prediction based on an extremely randomized trees regressor (ERTR) algorithm. In addition, we construct a radio environment map (REM) over a Google Earth digital map to improve visualization of the results and to easily detect coverage holes and traffic hotspots. For this purpose, we utilize a dataset with real measurements collected from Victoria Island and Ikoyi in Lagos, Nigeria. For performance evaluation, we use k-fold cross-validation based on four error metrics: relative error, root mean squared error, mean absolute error, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score. The proposed ERTR scheme achieves the best performance in terms of accuracy and computational load in predicting the reference signal received power and the received signal strength indicator value. We prove this with extensive simulation analysis and by comparing the error metrics of the proposed ERTR approach with an existing method widely used to perform coverage prediction, called ordinary kriging. We also compared seven machine learning regression algorithms, namely, random forest, a bagging regressor, support vector regression, k-nearest neighbors, a deep neural network, Gaussian process regression, and the decision tree.

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

UAV-Based Volumetric Measurements toward Radio Environment Map Construction and Analysis.

Unmanned aerial vehicle (UAV)-empowered communications have gained significant attention in recent years due to the promise of agile coverage provision for a large number of various mobile nodes on the ground and in three-dimensional (3D) space. Consequently, there is a need for efficient spectrum utilization in these dense aerial networks, which is characterized through radio environment maps (REMs), the construction of which is an important research area. Nevertheless, due to the difficult collection of radio frequency (RF) data, there are limited works that are based on real-world measurement campaigns. This paper presents a novel experimental setup that includes a constellation of three UAVs, the communication signals of which are measured by a software-defined radio (SDR) mounted on a separate UAV. It follows a trajectory that defines the REM's two-dimensional (2D) area on a plane, executed at four altitudes, to extend the REM to 3D. The measurements are then processed and their features (received mean power level, average difference of the mean power, percentage of meaningful correlations) are analyzed in the temporal, spatial, and frequency domains to determine the utilization of a 20 MHz band in the 2.4 GHz spectrum, as well as their variation with altitude. This analysis provides a base for research in reducing the amount of measurements (by identifying the regions of low and of high interest) and spectrum occupancy prediction for UAV-based communication coexistence.

USER ACTIVITY MODELING BASED ON MARKOV CHAIN&#x0D; FOR RADIO ENVIRONMENT MAP IN COGNITIVE RADIO NETWORKS

A technique for generating schedules of movement and data transmission sessions for subscriber devices&#x0D; of a simulation model for constructing a radio environment map for a cognitive cellular communication system is presented.&#x0D; The technique defines the subscriber’s behaviour as pseudo-random on small time scales, but periodically&#x0D; repeating over large intervals. The impact on the behaviour of several time parameters is taken into account: day types,&#x0D; such as working, weekend, holiday; weekday number; the week number of the month. Scenarios are generated using&#x0D; binary sequences obtained using a Markov chain and an impulse noise generator to change the subscribers state.&#x0D; The software model implementation is made in MatLab package, the results of studies are presented that confirm&#x0D; the correctness of the model and the possibility of using the proposed approach to form a radio environment map.

Complexity reduction of ordinary kriging algorithm for 3D REM design

This work has investigated the feasibility of using cluster ordinary kriging (OK) interpolation to speed up indoor three-dimensional (3D) radio environment map (REM) generation time than the conventional OK-based REM generation time in two-dimensional (2D) and 3D ways mainly. The REM has been generated on the real-time received signal strength (RSS) dataset of an ultra-high frequency active television (TV) channel to explore TV grey space (TVGS) volume for cognitive access inside a six-storied office building. It can support future home networks for high-speed data-based applications. The optimal number of clusters has been found at K=4 on both 3D and 2D ways through distortion % deviation analysis. The maximum distortion % deviation = 88.74%, has been found at K=4. The exponential semivariogram model has been selected as the best-fit model after a thorough cross-validated comparative analysis of the clustered and non-clustered OK algorithms separately on residual squared error (RSE), root mean square error (RMSE) and the ratio of expected variance (EV) and kriging variance (KV) results. The RSE and RMSE are minimum, and the EV: KV ratio is in between 0.95 to 1.05 for the exponential model. Significant observation of the analysis of the results shows that cluster OK algorithms outperform conventional OK algorithms of corresponding domains on time complexity. A comparative study on RMSE, correlation coefficient (CC), and relative recovery error (RRE) show that cluster OK improves the accuracy. Cluster 2D and 3D OK-based detected TVGS volume is 13450 m3, 5.634% of the whole building volume.