This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.
In this paper, we study the availability of TV white spaces in Europe. Specifically, we focus on the 470-790 MHz UHF band, which will predominantly remain in use for TV broadcasting after the analog-to-digital switch-over and the assignment of the 800 MHz band to licensed services have been completed. The expected number of unused, available TV channels in any location of the 11 countries we studied is 56 percent when we adopt the statistical channel model of the ITU-R. Similarly, a person residing in these countries can expect to enjoy 49 percent unused TV channels. If, in addition, restrictions apply to the use of adjacent TV channels, these numbers reduce to 25 and 18 percent, respectively. These figures are significantly smaller than those recently reported for the United States. We also study how these results change when we use the Longley-Rice irregular terrain model instead. We show that while the overall expected availability of white spaces is essentially the same, the local variability of the available spectrum shows significant changes. This underlines the importance of using appropriate system models before making far-reaching conclusions.  In this paper we present results from a week long measurement campaign on spectrum use in London (UK). The measurements were conducted in order to understand the characteristics and especially the variability in spectrum use over different types of areas in a major metropolitan area. Three spectrum analyzers were used in the measurement campaign, one used for long-term measurements at a single location in a given area, with the other two used to sample spectrum use around the stationary measurement point. This measurement approach yields much more detailed information about spectrum use than the typical single-location campaigns reported in the literature. We give a detailed description of the measurement campaign, including the equipment setup and rationale for the choice of areas in which the measurements were conducted. We also present results from the first exploratory data analysis of the obtained data, and study in detail the correlation structures and dynamics in spectrum use in temporal, spatial and frequency domains. In this demonstration we show an approach for constructing radio environment maps from massive data sets. Unlike earlier approaches, the methods used in the demonstrator can scale to millions of measurement points, enabling coverage prediction and other spatial estimation problems to be solved at country-wide scales. The demonstrator GUI enables attendees to construct different types of simulated measurement data sets, and perform spatial estimation based on those. The framework also allows the attendees to study the accuracy of the obtained estimates, as well as the computational time required for data processing. In this paper we provide a thorough and up to date survey of path loss prediction methods, spanning more than 60 years of fairly continuous research. These methods take a variety of approaches to modeling the signal attenuation between wireless transceivers: purely theoretical models, empirically fitted (often statistical) models, deterministic ray-optical models, and measurement-directed methods. Our work here extends and updates excellent, but now dated prior surveys of this important field. We provide a new taxonomy for reasoning about the similarities and differences of the many approaches and provide a brief but complete overview of the various methods as well as describing insights into future directions for research in this area. In this paper we analyze the efficacy of basic path loss models at predicting median path loss in urban environments. We attempt to bound the practical error of these models and look at how they may hinder practical wireless applications, and in particular dynamic spectrum access networks. This analysis is made using a large set of measurements from production networks in two US cities. We are able to show quantitatively what many experienced radio engineers understand: these models perform poorly at predicting path loss in even relatively simple outdoor environments and are of little practical use aside from making crude estimates of coverage in the least demanding applications. As a solution, we advocate a renewed focus on measurement-based, adaptive path loss models built on appropriate statistical methods.