Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels

In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.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

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

Group-Lasso on Splines for Spectrum Cartography

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finitely-parameterized (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that ``better'' explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Simulated tests corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.Comment: Submitted to IEEE Transactions on Signal Processin

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

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

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented

Location-free Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on Signal Processin