A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

We propose a new iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE) based IDD, as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreasesProyectos Nacionales Españoles del Gobierno de España TEC2017-90093-C3-2-

Reduced complexity detection for massive MIMO-OFDM wireless communication systems

PhD ThesisThe aim of this thesis is to analyze the uplink massive multiple-input multipleoutput with orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems and to design a receiver that has improved performance with reduced complexity. First, a novel receiver is proposed for coded massive MIMO-OFDM systems utilizing log-likelihood ratios (LLRs) derived from complex ratio distributions to model the approximate effective noise (AEN) probability density function (PDF) at the output of a zero-forcing equalizer (ZFE). These LLRs are subsequently used to improve the performance of the decoding of low-density parity-check (LDPC) codes and turbo codes. The Neumann large matrix approximation is employed to simplify the matrix inversion in deriving the PDF. To verify the PDF of the AEN, Monte-Carlo simulations are used to demonstrate the close-match fitting between the derived PDF and the experimentally obtained histogram of the noise in addition to the statistical tests and the independence verification. In addition, complexity analysis of the LLR obtained using the newly derived noise PDF is considered. The derived LLR can be time consuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced to this LLR using Newton’s interpolation with different orders and the results are compared to exact simulations. Further simulation results over time-flat frequency selective multipath fading channels demonstrated improved performance over equivalent systems using the Gaussian approximation for the PDF of the noise. By utilizing the PDF of the AEN, the PDF of the signal-to-noise ratio (SNR) is obtained. Then, the outage probability, the closed-form capacity and three approximate expressions for the channel capacity are derived based on that PDF. The system performance is further investigated by exploiting the PDF of the AEN to derive the bit error rate (BER) for the massive MIMO-OFDM system with different M-ary modulations. Then, the pairwise error probability (PEP) is derived to obtain the upper-bounds for the convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. Furthermore, the effect of the fixed point data representation on the performance of the massive MIMO-OFDM systems is investigated using reduced detection implementations for MIMO detectors. The motivation for the fixed point analysis is the need for a reduced complexity detector to be implemented as an optimum massive MIMO detector with low precision. Different decomposition schemes are used to build the linear detector based on the IEEE 754 standard in addition to a user-defined precision for selected detectors. Simulations are used to demonstrate the behaviour of several matrix inversion schemes under reduced bit resolution. The numerical results demonstrate improved performance when using QR-factorization and pivoted LDLT decomposition schemes at reduced precision.Iraqi Government and the Iraqi Ministry of Higher Education and Scientific researc

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we examine exact and approximate near-field channel models for XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further motivate and discuss low-complexity signal processing schemes to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems.Comment: 38 pages, 10 figure

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

The Twenty-Fifth Lunar and Planetary Science Conference. Part 3: P-Z

Various papers on lunar and planetary science are presented, covering such topics as: impact craters, tektites, lunar geology, lava flow, geodynamics, chondrites, planetary geology, planetary surfaces, volcanology, tectonics, topography, regolith, metamorphic rock, geomorphology, lunar soil, geochemistry, petrology, cometary collisions, geochronology, weathering, and meteoritic composition