Building accurate radio environment maps from multi-fidelity spectrum sensing data

In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated

Fixed Rank Kriging for Cellular Coverage Analysis

Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements which are gathered today during highly expensive drive tests (DT); and will be reported in the near future by users' mobile devices thanks to the 3GPP Minimizing Drive Tests (MDT) feature~\cite{3GPPproposal}. This feature consists in an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: it builds a coverage map by spatially interpolating geo-located measurements using the Kriging technique. The paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good trade-off between prediction efficiency and computational complexity. In order to go a step further towards the operational application of the proposed algorithm, a multicellular use-case is studied. Simulation results show a good performance in terms of coverage prediction and detection of the best serving cell

Experimental assessment of RRM techniques in 5 GHz dense WiFi networks using REMs

“© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”The increasing acceptance of WiFi has created unprecedented levels of congestion in the unlicensed frequency bands, especially in densely populated areas. This results mainly because of the unmanaged interference and uncoordinated op- eration between WiFi access points. Radio Environment Maps (REM) have been suggested as a support for coordination strategies that optimize the overall WiFi network performance. In this context, the main objective of this experiment is to assess the benefit of a coordinated management of radio resources in dense WiFi networks at 5 GHz band, using REMs for indoor scenarios. It was shown that REMs can detect the presence of interfering links on the network or coverage holes, and a suitable coordination strategy can use this information to reconfigure Access Points (AP) channel assignment and re-establish the client connection, at a cost of diminishing the aggregate throughput of the network. The technique of AP hand-off was tested to balance the load from one AP to another. Using REMs, the Radio Resource Management (RRM) strategy could reconfigure the network to optimize the client distribution among available APs. Although the aggregate throughput is lower after load balancing, the RRM could increase the throughput of the overloaded AP.info:eu-repo/semantics/publishedVersio

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

On the Construction of Radio Environment Maps for Cognitive Radio Networks

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc

実観測に基づく電波環境データベースを用いた空間的周波数共用に関する研究

The growth in demand for mobile communication systems has exponentially increased data traffic during the last decade. Because this exponential growth consumes finite spectrum resources, traditional spectrum utilization policies with exclusive resource allocation faces a limit. In order to develop novel spectrum resources, many researchers have shown an interest in spectrum sharing with cognitive radio (CR). This method allows secondary users (SUs) to share spectrum bands with primary users (PUs) under interference constraints for PUs. SUs are required to take into consideration the interference margin to the estimated interference temperature at PUs in order to protect communication quality of PUs. On the other hand, an excess interference margin decreases the spectrum sharing opportunity; therefore, it is important to manage the interference power properly. Spectrum estimation techniques in spectrum sharing can be categorized into two methods: spectrum sensing and spectrum database. Spectrum sensing uses the detection of PU signals to characterize radio environments. To provide good protection, signal detection must be performed under the (strict) condition that the PU signal strength be below the noise floor, even under low signal-to-noise ratios (SNRs) and fading conditions. These fluctuations make it difficult for the SUs to achieve stable detection; thus, it is very challenging to accurately estimate the actual activity of the PU. The second method is based on storing information about spectrum availabilities of each location in spectrum databases. In this method, afterSUs query the database before they utilize the spectrum, the database provides spectrum information to the SUs. Current databases usually evaluate white space (WS) based on empirical propagation models. However, it is well known that empirical propagation models cannot take into account all of the indeterminacies of radio environments, such as shadowing effects. Because SUs must not interfere toward PUs, the conventional database requires the SUs to set large margins to ensure no interference with PUs.In this dissertation, we propose and comprehensively study a measurement-based spectrum database for highly efficient spectrum management. The proposed database is a hybrid system, combining spectrum sensing and a spectrum database. The spectrum database consists of radio environment information measured by mobile terminals. After enough data are gathered, the database estimates the radio environment characteristics by statistical processing with the large datasets. Using the accurate knowledge of the received PU signal power, spectrum sharing based on PU signal quality metrics such as the signal-to-interference power ratio (SIR) can be implemented.We first introduce the proposed database architecture. After we briefly discuss a theoretical performance of the proposed database, we present experimental results for the database construction using actual TV broadcast signals. The experimental results show that the proposed database reduces the estimation error of the radio environment. Next, we propose a transmission power control method with a radio environment map (REM) for secondary networks. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the PU is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically, and an increase in the number of measurement datasets improves the spectrum sharing capability. After that, we extend the proposed database to the radio propagation estimation in distributed wireless links in order to accurately estimate interference characteristics from SUs to PUs. Although current wireless distributed networks have to rely on an empirical model to estimate the radio environment, in the spectrum sharing networks, such a path loss-based interference prediction decreases the spectrum sharing opportunity because of the requirement for the interference margin. The proposed method focuses on the spatial-correlation of radio propagation characteristics between different wireless links. Using Kriging-based shadowing estimation, the radio propagation of the wireless link that has arbitrary location relationship can be predicted. Numerical results show that the proposed method achieves higher estimation accuracy than path loss-based estimation methods. The methods discussed in this thesis can develop more spatial WSs in existing allocated bandwidth such as TVWS, and can provide these WSs to new wireless systems expected to appear in the future. Additionally, these results will contribute not only to such spectrum sharing but also to improvement of the spectrum management in existing systems. For example, in heterogeneous networks (HetNets), a suitable inter-cell interference management enables transmitters to reuse the frequency efficiently and the user equipment can select the optimum base station. We anticipate that this dissertation strongly contributes to improvingthe spectrum utilization efficiency of the whole wireless systems.電気通信大学201

Spectrum Awareness in Cognitive Radio Systems

The paper addresses the issue of the Electromagnetic Environment Situational Awareness techniques. The main focus is put on sensing and the Radio Environment Map. These two dynamic techniques are described in detail. The Radio Environment Map is considered the essential part of the spectrum management system. It is described how the density and deployment of sensors affect the quality of maps and it is analysed which methods are the most suitable for map construction. Additionally, the paper characterizes several sensing methods

Application of Radio environment map reconstruction techniques to platoon-based cellular V2X communications

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work