Preliminary Results on 3D Channel Modeling: From Theory to Standardization

Three dimensional beamforming (3D) (also elevation beamforming) is now gaining a growing interest among researchers in wireless communication. The reason can be attributed to its potential to enable a variety of strategies like sector or user specific elevation beamforming and cell-splitting. Since these techniques cannot be directly supported by current LTE releases, the 3GPP is now working on defining the required technical specifications. In particular, a large effort is currently made to get accurate 3D channel models that support the elevation dimension. This step is necessary as it will evaluate the potential of 3D and FD(Full Dimensional) beamforming techniques to benefit from the richness of real channels. This work aims at presenting the on-going 3GPP study item "Study on 3D-channel model for Elevation Beamforming and FD-MIMO studies for LTE", and positioning it with respect to previous standardization works

Uncertainty Analysis Methodology for Measurements of Dynamic Millimeter-Wave Channels

Quantification of uncertainties in the results of channel sounding measurements is important for their interpretation and further usage. In this paper, a novel uncertainty analysis methodology to quantify uncertainties of condensed parameters in measurements of dynamic millimeter-wave channels is presented. The bandwidth limitation and multipath threshold are identified as important impairments. Therefore, the methodology provides three uncertainty metrics for condensed parameters, namely a standard uncertainty to quantify the impact of random variations; a bias due to the multipath threshold; and a total bias including the impact of the bandwidth limitation. These uncertainty metrics are highly channel dependent. Therefore, the proposed methodology creates reference channels, which are representative of corresponding measured channels. Hardware and processing impairments are included in the analysis via a Monte Carlo simulation. This results in a general methodology that can quantify uncertainties in both static and dynamic channel measurements of any wideband channel sounder. The methodology is implemented, verified and demonstrated for the TU/e channel sounder, which exemplifies how it can be used. The proposed methodology can improve the analysis, interpretation and reporting of channel measurement results.</p

Space-time-frequency methods for interference-limited communication systems

textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin

Heterogeneous Acceleration for 5G New Radio Channel Modelling Using FPGAs and GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Sliding Correlator Channel Sounder for Ultra-Wideband Measurements

This body of work forms a detailed and comprehensive guide for those interested in performing broadband wireless channel measurements. Discussion addresses the theoretical and practical aspects of designing and implementing a sliding correlator channel sounder, as well as how such a system may be used to measure and model the ultra-wideband wireless channel. The specific contributions of this work are as follows: Developed a systematic methodology for designing optimal sliding correlator-based channel sounders. Constructed a UWB channel sounder based upon a 17-bit LFSR that attained 1.66 ns of temporal resolution and 34 dB of dynamic range. Performed an exemplary measurement campaign of the UWB channel from which UWB angular spreads and RMS delay spreads are reported. The design procedure developed in Chapter 3 will allow researchers to build optimal channel sounders for investigating next-generation wireless channels. Chapter 4 s discussion addresses the real-world challenges of constructing a high performance sliding correlator channel sounder. Finally, the measurement campaign discussed in Chapter 5 outlines a procedure for investigating the spatio-temporal characteristics of the wireless channel and provides some of the first examples of UWB angular spread measurements.M.S.Committee Chair: Durgin, Gregory; Committee Member: Ingram, Mary Ann; Committee Member: Smith, Whi

Development and Evaluation of a Multistatic Ultrawideband Random Noise Radar

This research studies the AFIT noise network (NoNET) radar node design and the feasibility in processing the bistatic channel information of a cluster of widely distributed noise radar nodes. A system characterization is used to predict theoretical localization performance metrics. Design and integration of a distributed and central signal and data processing architecture enables the Matlab®-driven signal data acquisition, digital processing and multi-sensor image fusion. Experimental evaluation of the monostatic localization performance reveals its range measurement error standard deviation is 4.8 cm with a range resolution of 87.2(±5.9) cm. The 16-channel multistatic solution results in a 2-dimensional localization error of 7.7(±3.1) cm and a comparative analysis is performed against the netted monostatic solution. Results show that active sensing with a low probability of intercept (LPI) multistatic radar, like the NoNET, is capable of producing sub-meter accuracy and near meter-resolution imagery

Novel Insights into Orbital Angular Momentum Beams: From Fundamentals, Devices to Applications

It is well-known by now that the angular momentum carried by elementary particles can be categorized as spin angular momentum (SAM) and orbital angular momentum (OAM). In the early 1900s, Poynting recognized that a particle, such as a photon, can carry SAM, which has only two possible states, i.e., clockwise and anticlockwise circular polarization states. However, only fairly recently, in 1992, Allen et al. discovered that photons with helical phase fronts can carry OAM, which has infinite orthogonal states. In the past two decades, the OAM-carrying beam, due to its unique features, has gained increasing interest from many different research communities, including physics, chemistry, and engineering. Its twisted phase front and intensity distribution have enabled a variety of applications, such as micromanipulation, laser beam machining, nonlinear matter interactions, imaging, sensing, quantum cryptography and classical communications. This book aims to explore novel insights of OAM beams. It focuses on state-of-the-art advances in fundamental theories, devices and applications, as well as future perspectives of OAM beams

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments