Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

Radio Environment Map (REM) is used to enhance the communication efficiency in spatial spectrum sharing. REM indicates the received signal strength at each location by measurements data from each terminal or estimated data by the database. Crowdsourcing-based spectrum sensing is a promising approach for generating the REM for a large communication area. However, the open environment also leads the security problems, which can damage the REM accuracy by some malicious activities. The accuracy of terminals' reports became an essential problem need to be considered in REM construction system. In this paper, we introduce a Kriging-based Trust Nodes aided REM construction (KTNA-REM) system to achieve a high accuracy under the threatening environment. By adding the support of a small amount of the trust nodes, the database can evaluate the reputation of the terminals continuously and generate the REM based on the high reliable data even when the amount of the malicious terminals is big. Simulation results confirm the efficiency and accuracy of the proposed method.

In this paper, we discuss a method to construct a radio environment map (REM) in an environment with multiple primary users (PUs). The REM provides statistical information about the PU activity at each location. It enables the secondary user to access the licensed band dynamically. We derive the measurement architecture based on the “crowd-sourcing” scheme to gather large-scale measurement data with inexpensive sensor nodes. The PU detection or identification scheme is key part of the REM construction then there are multiple PUs in the environment. However, it is difficult to identify multiple PUs in an individual user terminal. Therefore, the PU detection or identification problem is solved at the REM servers using the Hidden Markov Model (HMM), which is a time-series-based machine learning technique. The proposed HMM method classifies the measurement data depending on the combined state of each transmitter, which can be either active or idle. The results show that the proposed method exhibits better performance than the existing unsupervised clustering method.

Radio environment maps (REMs) have demonstrated their significance in spectrum sensing, control, and sharing, particularly in relation to unmanned systems, the Internet of Things (IoT), and 5G communication. However, they encounter challenges related to meeting the increasing accuracy requirements. This paper introduces the BHM-RK algorithm, a novel approach for constructing REMs in scenarios with multiple transmitters. The algorithm utilizes spatial clustering for transmitter partitioning, Bayesian posterior inference within partitions, and residual Kriging interpolation to address challenges posed by diverse transmitter influences. Experimental validation demonstrates the superior performance of the BHM-RK algorithm in terms of construction accuracy compared to existing methods, highlighting its effectiveness in enhancing precision in REM construction for scenarios with multiple transmitters.

Accurate radio environment map (REM) construction proves critical for efficient wireless spectrum management. Although conventional 2D REMs have demonstrated effectiveness in wireless network optimization, they inherently overlook vertical signal strength variations, which are vital for UAV operations, particularly in urban landscapes with skyscrapers or diverse terrain features. Current estimation approaches, including ground-based crowdsourcing, random sampling, and predetermined trajectory measurements, show limited capability in generating high-fidelity 3D REMs. This study proposes a joint optimization framework for UAV-enabled adaptive 3D radio mapping, integrating 3D REM construction with adaptive aerial sampling. At the heart of the construction module, a dual-branch encoder-decoder architecture fuses multi-scale features from sparse aerial measurements with building structural data, explicitly modeling obstruction effects through offline pre-training and online refinement to enhance generalization. For adaptive sampling, a diffusion-based trajectory planner dynamically optimizes UAV measurement paths by integrating environmental priors (e.g., building layouts), effectively overcoming the sparse-reward limitations inherent in reinforcement learning methods. Experimental validation demonstrates significant performance improvements across all evaluation metrics. Compared to 2D per-layer estimation methods, our 3D estimator achieves 49% superior structural similarity (SSIM) in construction accuracy, while the feature fusion module yields a 37% reduction in mean squared error (MSE). The diffusion-based planner outperforms reinforcement learning approaches by achieving 45% lower MSE and 18% higher SSIM in resultant map quality after 5,000 step iterations.

The increased development of information industry and relevant radio communication services is making the spectrum management problem more challenging to solve. The Radio Environment Map (REM) can be a powerful tool for solving spectrum scarcity and spectrum access problems, making context-aware resource allocation more efficient. We can optimize the accuracy of the REM using a geostatistical tool named Kriging interpolation in cognitive radio networks. In this paper, the proposed REM construction method combines residual maximum likelihood-based radio propagation parameter estimation with Kriging-based transmission power prediction. Additionally, we compare the performance of the constructed REMs with the metric of root mean square error (RMSE) in three methods: the path loss-based method, the Kriging-based method without the fit of a path loss model beforehand, and the proposed method. With the Monte Carlo simulation, the result indicates that the path loss-based method provides the most unsatisfied performance and the interpolation accuracy can be improved by 2 dB with the proposed method, which proves that our approach can effectively determine sharing conditions of radio spectrum use both in time and space.

Radio Environment Map (REM) is very useful as it provides insight of the spatial characteristics of radio environment. To achieve higher accuracy, a novel REM methodology is proposed in this paper. Noticing the inability of systematic sampling to tackle inaccessible areas, a sampling scheme named simulated annealing assisted electrons repulsion is formulated. In this algorithm, the sampling points and boundary of inaccessible areas are treated as electrons such that the repulsive force between them can ensure the uniform distribution of the sampling points and keep them out of the inaccessible areas at the same time. In addition, simulated annealing is adopted to prevent the sampling location from converging to local optimal. Then, two interpolation methods, namely inverse distance weighting and ordinary kriging, are introduced to estimate the REM using the sampled values. To validate our methodology, a measurement is conducted in Beijing in the TV band. The result shows that our methodology has lower mean absolute error and root mean square error and the superior performance of our methodology is also proved.

The need for extra spectrum to support future 5G networks and the fact that a large amount of spectrum below 6 GHz is allocated for different radar systems have motivated regulatory bodies and wireless researchers to investigate the feasibility of Dynamic Spectrum Access (DSA) in radar bands. Moreover, the next generation of infrastructures and devices will need to be more flexible to support a much wider range of application services, operate over much wider bandwidths, and exploit multiple radio access technologies. Radio Environment Maps (REMs) can be utilized to enhance the awareness of network entities of their operational radio environment. In the context of potential spectrum sharing in radar bands, REMs can be used to determine spectrum usage and propagation patterns of different incumbent radar systems operating in a given area. This information in turn can be utilized to establish different radar systems protection requirements in a particular band, and can also help to mitigate any interference between incumbent radar systems and Secondary Users (SUs). In this paper, based on spectrum measurement campaigns in Finland and Ireland, we explore the functional architecture of REMs for potential spectrum sharing in radar bands. We argue that the unique operating principle and signal characteristics of this type of incumbent needs to be considered in implementation of the sensor network, REM communication protocol, and REM architecture. We also discuss the potential of REM to facilitate the use of more advanced interference cancellation or avoidance technologies such as temporal sharing that can significantly reduce exclusion zones.

This paper presents the indirect methods for constructing radio environment maps (REMs), which utilize known model information, to first estimate the primary transmitter parameters and then generate REMs. Two indirect methods under lognormal shadowing are presented and compared. The better of these two methods is further investigated in different scenarios. These scenarios include different number of sensors, varied size of measurements, several shadowing spread values, different percentages of error in path-loss exponent, and the effect of the number of moving sensors and their speeds to the REM quality. The results show that performance is enhanced as the number of sensors and the size of measurements increase, whereas clear degradation in REM quality is shown when shadowing spread increases or the model parameters are not well calibrated. Also, as the number of moving sensors or their speeds increase, the REM performance becomes less effective.

In this paper we present the dependency between density of sensor network and map quality in the Radio Environment Map (REM) concept. The architecture of REM supporting military communications systems is described. The map construction techniques based on spatial statistics and transmitter location determination are presented. The problem of REM quality and relevant metrics are discussed. The results of field tests for UHF range with different number of sensors are shown. Exemplary REM maps with different interpolation algorithms are presented. Finally, the problem of density of sensors network versus REM map quality is analyzed.

− The increasing importance of mobile devices as a mean to exchange information in wireless environments has prompted the need to establish secure sessions to avoid potential security attacks. The Wireless Transport Layer Security (WTLS) protocol provides the mechanism for obtaining secure sessions using public-key cryptography. WTLS supports two public key cryptosystems: RSA and Elliptic Curve Cryptography (ECC). Nowadays, RSA is the most popular option for implementing confidential data exchange in applications such as e-commerce and mobile e-commerce. However, it has been theoretically argued that bit by bit, ECC provides much more security than RSA at a lower computational cost. In this paper we derive an analytical performance model of the WTLS handshake protocol when using the two aforementioned public-key cryptosystems. The performance figures predicted by our model were contrasted with experimental results acquired from a real wireless implementation of the said protocol. The obtained results confirms our model theoretical predictions providing experimental evidence that ECC indeed outperforms RSA in realistic wireless secure scenarios.

Radio environment maps (REMs) and geolocation database represent an important source of information for the operation of cognitive radio networks, replacing or complementing spectrum sensing information. This paper provides a survey of methods for constructing the radio frequency layer of radio environment map (RF-REM) using distributed measurements of the signal levels at a given frequency in space and time. The signal level measurements can be obtained from fixed or mobile devices capable of sensing radio environment and sending this information to the REM. The signal measurements are complemented with information already stored in different REM content layers. The combined information is applied for estimation of the RF-REM layer. The RF-REM construction methods are compared, and their advantages and disadvantages with respect to the spatial distribution of signal measurements and computational complexity is given. This survey also indicates possible directions of further research in indirect RF-REM construction methods. It emphasizes that accurate RF-REM construction methods should in the best case support operation with random and clustered signal measurements, their operation should not be affected by measurements outliers, and it must estimate signal levels comparably on all RF-REM locations with moderate computational effort.

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.

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.

Radio Environment Map (REM) is a map which indicates the radio signal strength (RSS) over a geographical region. With the help of REM, Cognitive Radio (CR) users can opportunistically access the licensed spectrum. Distributed cooperative REM estimation is vulnerable to malicious sensors that submits false sensing reports. In this paper, we develop a secure distributed scheme to estimate the REM in hierarchical wireless CR networks. We formulate the estimation process as a LS problem with two ii-norm constraints using the basis pursuit approach. Reputation factors are introduced to further improve the estimation accuracy. Our scheme enables joint valid estimation result and malicious sensor identification. The performance of the proposed scheme is confirmed by extensive simulation studies.

Recent advances in cognitive radio have identified the small-cells among the most promising future wireless networking scenarios. Utilizing radio context information, smallcells should perform the most optimal radio resource management (RRM) to maximize performances and minimize inter-cell interference. Radio Environmental Maps (REM) data: empirical propagation models, active transmitters’ locations, upto-date interference levels, statistical channels occupancies, are especially beneficial in these scenarios. The proposed demonstration aims to showcase the benefits of using REM information in the small-cell optimization. The demonstration utilizes a modular/flexible REM prototype, performing a realtime REM data acquisition, processing and inference as input to an enhanced small-cell optimization.