End-to-End UAV-Enabled Adaptive 3D Radio Mapping via Joint Optimization of Sparse Sampling and Reconstruction

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 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.

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.

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.

This paper addresses the problem of scheduling a distributed set of RF sensors for the creation of a multichannel Radio Environment Map (REM). We propose a method for tracking the spatial distribution of the activity patterns on each channel via Kaiman filtering. The activity patterns on each channel are then used to inform schedule creation by balancing the achievable expected uncertainty across all channels by using the concepts of Permissible Consecutive Observation Loss (PCOL) and Least Consecutive Observation (LCO). For a stationary wireless sensor network, where each sensor can scan one channel at a time, we create an adaptive stochastic channel sensing order that balances the spatial uncertainty of the estimate of the REM for each of the channels of interest. Herein, the algorithm's effectiveness is demonstrated through simulation. It will also be demonstrated on actual spectrum measurements under the DARPA RadioMap, for which this algorithm was developed.

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.

Chapter 11 - Network Support: The Radio Environment Map

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.

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.

The aim of this paper is to preserve location privacy of crowdsourced-based spectrum sensing agents using geo-indistinguishability. We considered database-driven dynamic spectrum access, where a radio environment map provides spectrum availability information for dynamic spectrum access management. Moreover, we assumed crowdsourced-based spectrum sensing, where a pool of allocated mobile users, called crowdsourced-based spectrum sensing agents, sense the spectrum and report their actual location and the received signal strength to the spectrum manager that constructs a radio environment map. This discloses location information of crowdsourced-based spectrum sensing agents and violates their location privacy. Consequently, crowdsourced-based spectrum sensing agents could be discouraged to participate in spectrum sensing. In our paper, to solve the problem of location disclosure, we adopted planar Laplacian mechanism, where each crowdsourced-based spectrum sensing agent reports an obfuscated location instead of its actual location, which achieves geo-indistinguishability. Our simulation results were based on real-world CRAWDAD dataset. Our results showed that with a moderate privacy level, location privacy of crowdsourced-based spectrum sensing agents was preserved while the effect of introduced location noise on the accuracy of radio environment map was insignificant.

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.

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 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.

Cognitive Radio (CR) is considered as a promising solution to improve wireless spectrum utilization. Moreover, through optimization frameworks, Radio Resource Management (RRM) procedures are designed to enhance the efficient utilization of resources in CR networks. To face this issue in a holistic perspective, the RRM solutions should take into account not only the optimization task but also their impact on the design task. Thus, in this paper, a system architecture of a novel RRM is proposed for CR networks. In particular, it is fundamental to construct flexible systems to work across heterogeneous systems and help realize their full potential. Therefore, in the proposed system, heterogeneous Primary Users (PUs) with multiple features and variable CR demands are considered. A Radio Environment Map (REM) is used to obtain the required PU features, which are exploited to improve the adaptability in CR networks and, thus, to design an efficient Cognitive RRM. The functionalities of the network elements are described in detail, along with the message exchange to carry out the resource management.

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.