Kriging-based Trust Nodes Aided REM Construction under Threatening Environment

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.

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.

In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.

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.

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 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.

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 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.

The knowledge of the statistical characterization of the primary user activity can make more efficient the spectrum selection techniques, which are a key technology to improve the Cognitive Radio (CR) systems performance. In order to provide this kind of primary user information, Radio Environment Maps (REM) data bases has been proposed. This paper presents a REM-based Real Time Testbed designed to validate and exploit the dynamicity of the REM data base to improve the performances of the Radio Resource Management (RRM) algorithms in the context of cognitive radio. The implemented testbed is a flexible HW/SW tool able to use spectrum measurements in licensed bands and validate the Cognitive Radio performance of the secondary users, without causing any interference on these primary users. In particular, the RRM strategy under study in the testbed envisages the optimization of the spectrum selection policy when secondary users, with different service profiles, access opportunistically a set of channels during the inactivity periods of primary users. The selection is done taking as input the statistical characterization of the primary user activity, which will be stored in the REM data base, and the secondary user service profiles.

Chapter 11 - Network Support: The Radio Environment Map

Radio Environment Map (REM) is a map that indicates the radio signal strength (RSS) over a finite geographical region. It enables cognitive radio (CR) users to opportunistically access to the spectrum unoccupied by the primary users (PUs) who are licensed to use specific spectrum. In this paper, a basis expansion based scheme is presented to estimate REM. We take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares (RLS) problem with an l1 norm constraint. A distributed algorithm is presented to solve the RLS problem. Each SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal strength. Finally, simulation studies are presented to validate the performance of the proposed method.

This paper focuses on femtocell self-organization through radio environment map (REM) based power control. Different downlink power control strategies with REM have been studied with realistic LTE system evaluation. We have shown that downlink power control can be an effective means for femtocell self-organization. With the help of REM information, the performance between macrocell and femtocell is better balanced. Our study suggests that REM-based power control is useful in practical femtocell network planning, in which a careful balance should be struck in maintaining satisfactory network performance and user QoS (quality of service) in heterogeneous networks.

In this paper we discuss the dependency between deployment of sensors 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 the problem of REM quality are presented. The results of field tests done for UHF range with different arrangements of sensors are shown. Exemplary REM maps with selected interpolation techniques are presented. Finally, the problem of deployment of sensors versus REM map quality is analyzed.