Received signal interpolation for context discovery in cognitive radio

This paper addresses the context acquisition to characterize different features of a primary network based on the power measurements obtained by a sensor network. In particular, it presents a comparative study of the impact of natural neighbor, linear, and nearest neighbor interpolation techniques carried out over the measurements at different geographical positions. Extracted features include transmitter position, antenna pattern or propagation model. An evaluation is carried out in scenarios including the effect of both correlated and non-correlated shadowing.Peer ReviewedPostprint (author’s final draft

Directional Antenna System-Based DoA/RSS Estimation, Localization and Tracking in Future Wireless Networks: Algorithms and Performance Analysis

Location information plays an important role in many emerging technologies such as robotics, autonomous vehicles, and augmented reality. Already now the majority of smartphone owners use their devices' localization capabilities for a broad range of location-based services. Currently, location information in smartphones is mostly obtained in a device-centric approach, where the device to be localized, here referred to as the target node (TN), estimates its own location using, for example, the global positioning system (GPS). However, TNs with wireless communication capabilities can be localized based on their transmitted signals by a third party. In particular, localization can be implemented as a functionality of a wireless network. Depending on the application area and implementation, this network-centric approach has several advantages compared to device-centric localization, such as reducing the energy consumption within the TNs, enabling localization of non-cooperative TNs, and making location information available in the network itself. Current generation wireless networks are already capable of coarse localization. However, these existing localization capabilities do not suffice for the challenging demands of future applications. The majority of approaches moreover does not exploit the fact that an increasing number of base stations (BSs) and user devices are equipped with directional antennas. However, directional antennas enable direction of arrival (DoA) estimation that can, in turn, serve as the basis for advanced localization and location tracking. In this thesis, we thus study the application of directional antennas for localization and location tracking in future generation wireless networks. The contributions of this thesis can be grouped into two topics.First, this thesis provides a detailed study of DoA/received signal strength (RSS) estimation and localization with a group of directional antennas herein denoted as sectorized antennas. This group of antennas is of particular interest as it encompasses a broad range of directional antennas that can be implemented with a single RF front-end. Thus, the hardware complexity of sectorized antennas is low in comparison to the conventionally used antenna arrays that require multiple transceiver branches. However, at the same time this means that DoA estimation with sectorized antennas has to be implemented in a fundamentally different way. In order to address these differences, the study of sectorized antennas in this thesis includes the derivation of Cramer-Rao bounds (CRBs) for DoA/RSS estimation and localization, the proposal of three different DoA/RSS estimators, as well as numerical and analytical performance evaluations of DoA/RSS estimation and localization using sectorized antennas.Second, this thesis deals with localization based on the fusion of DoA and RSS estimates as well as DoA and time of arrival (ToA) estimates. It is shown that the combination of these estimates can result in a much increased localization performance compared to a localization based on one of these estimates alone. For the localization based on DoA/RSS estimates, a mechanism explaining this improvement is revealed by means of a CRB analysis. Thereafter, DoA/RSS-based fusion is further studied using an extended Kalman filter (EKF) as an example location tracking algorithm. Finally, an EKF is proposed that tracks the location of a TN by fusing DoA and ToA estimates. Apart from a significantly improved tracking performance, this joint DoA/ToA-EKF moreover provides estimates for the TN device clock offset and is able to localize the TN in situations where a classical DoA-only EKF fails to provide a location estimate altogether.Overall, this thesis thus provides insights into benefits of localization and location tracking using directional antennas, accompanied by specific DoA/RSS estimation, localization and location tracking solutions, as well as design guidelines for implementing localization systems in future generation wireless networks

D6.2 - Prototype description and field trial results

Deliverable D6.2 del projecte FARAMIRPostprint (published version

Cooperative Power and DoT Estimation for a Directive Source

Reliable estimation of the source power as well as the direction of transmission (DoT) is required in a large number of applications, e.g. radio environment mapping for cognitive radios, security, system performance and interference monitoring. In this paper, we develop a multi-sensor cooperative estimation algorithm for joint power and DoT estimation of a source with a known location and equipped with a directive antenna pattern. The source signal is assumed to be known, e.g. a training sequence, and the channel is modeled by the free-space path loss. Simulation results show that the developed algorithm can deliver a reliable estimation accuracy

GEOLOCATION AWARE RESOURCE ALLOCATION IN CELLULAR BASED COGNITIVE RADIO NETWORKS WITH GREEN COMMUNICATION PERSPECTIVE

This paper puts forward a Geolocation aware spectrum and power allocation scheme for cellular-based cognitive radio network using the principle of sensing free spectrum access. The problem formulation to test the feasibility of deploying the secondary system within Terrestrial Trunked Radio (TETRA) based cellular primary system is carried out to maximize the served Secondary Users (SU) while keeping the interference to Primary Users (PU) under a predefined threshold. A novel model called Primary Mobility Contour (PMC) for the avoidance of harmful interference to PU is proposed, which will consider the velocity of PU, the time taken by the secondary base station for transmission and Geolocation information. Using this model sensing free spectrum and power allocation algorithm is developed for cellular-based cognitive radio network to maximize the served SU to enhance system throughput and achieve an enhanced energy efficiency of the system to attain green communication. Simulation results confirm that the proposed scheme maximizes the served SUs per cell, throughput and energy efficiency

Distributed Localization of Active Transmitters in a Wireless Sensor Network

In today\u27s military environment, emphasis has been placed on bandwidth efficiency and total use of the available spectrum. Current communication standards divide the spectrum into several different frequency bands, all of which are assigned to one or multiple primary users. Cognitive Radio utilizes potential white spaces that exist between currently defined channels or in time. One under-explored dimension of white space exploration is spatial. If a frequency band is being used in one region, it may be underutilized, or not occupied in another. Using an active localization method can allow for the discovery of spatial white; trying to spatially map all of the frequencies in a large area would become very computationally intensive, and may even be impractical using modern centralized methods. Applying a distributed method and the concepts discussed in Wireless Distributed Computing to the problem can be scaled onto many small wireless sensors and could improve the measuring system\u27s effectiveness. For a bandwidth contested environment that must be spectrally mapped, three metrics stand out: Accuracy, Power Consumption, and Latency. All of these metrics must be explored and measured to determine which method could be most effectively applied to the spectral mapping of a spatial environment

Doctor of Philosophy

dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements