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

Comparison of Methods for Radio Position of Non-Emitting Dismounts

Radio Tomographic Imaging (RTI) is a form of Device Free Passive Localization (DFPL) that utilizes the Received Signal Strength (RSS) values from a collection of wireless transceivers to produce an image in order to localize a subject within a Wireless Sensor Network (WSN). Radio Mapping is another form of DFPL that can utilize the same RSS values from a WSN to localize a subject by comparing recent values to a set of calibration data. RTI and Radio Mapping have never been directly compared to one another as a means of localization within a WSN. The goal of this research is to compare using TelosB mote devices these approaches in a side-by-side manner. A real world WSN was constructed and both RTI and Radio Mapping methodologies were applied to identical data sets with the results compared and discussed. Initial results show that both methodologies have inherent advantages and disadvantages respective to one another; Radio Mapping performs significantly better in WSNs with a low number of transceivers being 100% accurate within the bounds of this experimentation, while RTI has significantly more simple calibration procedures

Radio Tomographic Imaging using a Modified Maximum Likelihood Estimator for Image Reconstruction in Various Environments

Radio Tomographic Imaging (RTI) is an emerging Device-Free Passive Localization (DFPL) technology. Radio Tomographic Imaging (RTI) involves using a set of small low cost wireless transceivers to create a Wireless Sensor Network (WSN) around an Area of Interest (AoI). Furthermore, the Received Signal Strength (RSS) between transceiver pairs is utilized to reconstruct an image from the signal attenuation caused by an object disrupting the links. This image can then be utilized for multiple applications ranging from localization to target detection and tracking. This enhances the importance of image resolution in order to capture the actual size of the objects as well as the ability to resolve multiple objects in an AoI. The objective of this research is to propose a new image formation technique for a reconstructed image within aWSN. This was accomplished using a modified Maximum Likelihood Estimate (MLE) function that forces the desired solution to be positive. Other regularization techniques must implement different methods to mitigate the undesired singular values caused from a non-invertible matrix. Additionally, the research highlights the performance of the modified MLE estimator and the robustness of improved image resolution in three different environments

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Blind Radio Tomography

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Characterizing Multiple Wireless Sensor Networks for Large-Scale Radio Tomography

Radio Tomographic Imaging (RTI) is an emerging Device-Free Passive Localization (DFPL) technology that uses a collection of cheap wireless transceivers to form a Wireless Sensor Network (WSN). Unlike device-based active localization, DFPL does not require a target of interest to be wearing any kind of device. The basic concept of RTI utilizes the changes in Received Signal Strength (RSS) between the links of each transceiver to create an attenuation image of the area. This image can then be used for target detection, tracking, and localization. Each transceiver in the WSN must transmit sequentially to prevent collisions. This is not a problem when the number of transceivers in the WSN are small. However, large-scale RTI with a large number of transceivers suffer from high computational complexity, low frame rates, and physical distance limitations on the range of the transceivers. The goal of this research is to determine the applicability and characterize the feasibility of using multiple WSNs to address the limitations with a large-scale RTI network. The concept to this new variant of RTI, called Multiple-Networks RTI (mnRTI), is to divide the transceivers into multiple WSNs as opposed to using one WSN. Analytical, simulated, and experimental data are computed, collected, and compared between a RTI network with one WSN to a mnRTI network with two WSNs. The WSN(s) comprise a total of 70 wireless transceivers covering an area of no more than 19 ft x 16 ft. Simulated and experimental results are presented from a series of stationary and moving target data collection. Preliminary results demonstrate multiple WSNs can potentially provide similar or better results than the traditional RTI method with one WSN. Multiple WSNs have higher frame rates and lower computational complexity. Also, position estimation accuracy are comparable, if not better, than the traditional RTI method with one WSN

Data-driven Channel Learning for Next-generation Communication Systems

University of Minnesota Ph.D. dissertation. October 2019. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); x, 116 pages.The turn of the decade has trademarked the `global society' as an information society, where the creation, distribution, integration, and manipulation of information have significant political, economic, technological, academic, and cultural implications. Its main drivers are digital information and communication technologies, which have resulted in a "data deluge", as the number of smart and Internet-capable devices increases rapidly. Unfortunately, establishing information infrastructure to collect data becomes more challenging particularly as communication networks for those devices become larger, denser, and more heterogeneous to meet the quality-of-service (QoS) for the users. Furthermore, scarcity in spectral resources due to an increased demand for mobile devices urges the development of a new methodology for wireless communications possibly facing unprecedented constraints both on hardware and software. At the same time, recent advances in machine learning tools enable statistical inference with efficiency as well as scalability in par with the volume and dimensionality of the data. These considerations justify the pressing need for machine learning tools that are amenable to new hardware and software constraints, and can scale with the size of networks, to facilitate the advanced operation of next-generation communication systems. The present thesis is centered on analytical and algorithmic foundations enabling statistical inference of critical information under practical hardware/software constraints to design and operate wireless communication networks. The vision is to establish a unified and comprehensive framework based on state-of-the-art data-driven learning and Bayesian inference tools to learn the channel-state information that is accurate yet efficient and non-demanding in terms of resources. The central goal is to theoretically, algorithmically, and experimentally demonstrate how valuable insights from data-driven learning can lead to solutions that markedly advance the state-of-the-art performance on inference of channel-state information. To this end, the present thesis investigates two main research thrusts: i) channel-gain cartography leveraging low-rank and sparsity; and ii) Bayesian approaches to channel-gain cartography for spatially heterogeneous environment. The aforementioned research thrusts introduce novel algorithms that aim to tackle the issues of next-generation communication networks. Potential of the proposed algorithms is showcased by rigorous theoretical results and extensive numerical tests