Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao bound (CRB) on position and rotation angle estimation uncertainty from millimeter wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of- sight, non-line-of-sight, or obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages. Also, Fig.2, Fig. 10 and Table I are adde

Millimeter wave wearable communication networks : analytic modeling and MIMO support

Future high-end wearable electronic devices including virtual reality goggles and augmented reality glasses require rates of the order of gigabits-per-second and potentially very low latency. Supporting high data rate wireless connectivity for applications such as uncompressed video streaming among wearable devices in a densely crowded environment is challenging. This is primarily due to bandwidth scarcity when many users operate multiple devices simultaneously. The millimeter wave (mmWave) band has the potential to address this bottleneck, thanks to more spectrum and less interference because of signal blockage at these frequencies. This dissertation addresses key questions that need to be answered before realizing mmWave-based wearables in practice: (i) what are the expected achievable rates in a crowded user environment, with mmWave devices using a given hardware configuration? (ii) how is the wireless connectivity affected in an indoor operation, which is prone to surface reflections? (iii) can multi-stream data transmission, involving large bandwidth communication under hardware constraints be realized? To answer these, tools from stochastic geometry and compressive sensing, and architectures involving hybrid analog/digital multiple-input multiple-output (MIMO) are leveraged. The main contributions of this dissertation are 1) analytical modeling to compute average achievable rates in mmWave wearable networks consisting of finite number of user devices and human blockages, 2) characterizing the impact of reflections and non-isotropic performance of mmWave wearable networks in crowded indoor environments, 3) channel estimation to support MIMO for wideband mmWave wearable devices using hybrid architecture, and 4) designing optimal, but easy-to-implement, precoding/combining strategies in frequency-selective mmWave systems. Both analysis and numerical simulations show how the proposed evaluation methodology and solutions serve to enable mmWave based communication among next generation wearable electronic devices.Electrical and Computer Engineerin

Energy efficient and low complexity techniques for the next generation millimeter wave hybrid MIMO systems

The fifth generation (and beyond) wireless communication systems require increased capacity, high data rates, improved coverage and reduced energy consumption. This can be potentially provided by unused available spectrum such as the Millimeter Wave (MmWave) frequency spectrum above 30 GHz. The high bandwidths for mmWave communication compared to sub-6 GHz microwave frequency bands must be traded off against increased path loss, which can be compensated using large-scale antenna arrays such as the Multiple-Input Multiple- Output (MIMO) systems. The analog/digital Hybrid Beamforming (HBF) architectures for mmWave MIMO systems reduce the hardware complexity and power consumption using fewer Radio Frequency (RF) chains and support multi-stream communication with high Spectral Efficiency (SE). Such systems can also be optimized to achieve high Energy Efficiency (EE) gains with low complexity but this has not been widely studied in the literature. This PhD project focussed on designing energy efficient and low complexity communication techniques for next generation mmWave hybrid MIMO systems. Firstly, a novel architecture with a framework that dynamically activates the optimal number of RF chains was designed. Fractional programming was used to solve an EE maximization problem and the Dinkelbach Method (DM) based framework was exploited to optimize the number of active RF chains and the data streams. The DM is an iterative and parametric algorithm where a sequence of easier problems converge to the global solution. The HBF matrices were designed using a codebook-based fast approximation solution called gradient pursuit which was introduced as a cost-effective and fast approximation algorithm. This work maximizes EE by exploiting the structure of RF chains with full resolution sampling unlike existing baseline approaches that use fixed RF chains and aim only for high SE. Secondly, an efficient sparse mmWave channel estimation algorithm was developed with low resolution Analog-to-Digital Converters (ADCs) at the receiver. The sparsity of the mmWave channel was exploited and the estimation problem was tackled using compressed sensing through the Stein's unbiased risk estimate based parametric denoiser. The Expectation-maximization density estimation was used to avoid the need to specify the channel statistics. Furthermore, an energy efficient mmWave hybrid MIMO system was developed with Digital-to- Analog Converters (DACs) at the transmitter where the best subset of the active RF chains and the DAC resolution were selected. A novel technique based on the DM and subset selection optimization was implemented for EE maximization. This work exploits the low resolution sampling at the converting units and provides more efficient solutions in terms of EE and channel estimation than existing baselines in the literature. Thirdly, the DAC and ADC bit resolutions and the HBF matrices were jointly optimized for EE maximization. The flexibility in choosing the bit resolution for each DAC and ADC was considered and they were optimized on a frame-by-frame basis unlike the existing approaches, based on the fixed resolution sampling. A novel decomposition of the HBF matrices to three parts was introduced to represent the analog beamformer matrix, the DAC/ADC bit resolution matrix and the baseband beamformer matrix. The alternating direction method of multipliers was used to solve this matrix factorization problem as it has been successfully applied to other non-convex matrix factorization problems in the literature. This work considers EE maximization with low resolution sampling at both the DACs and the ADCs simultaneously, and jointly optimizes the HBF and DAC/ADC bit resolution matrices, unlike the existing baselines that use fixed bit resolution or otherwise optimize either DAC/ADC bit resolution or HBF matrices

Millimeter-wave sensing of the environment: A bibliographic survey

This literature survey was conducted to examine the field of millimeter wave remote sensing of the environment and collect all relevant observations made in the atmospheric windows near 90, 140, and 230 GHz of ocean, terrain, man-made features, and the atmosphere. Over 170 articles and reports were examined; bibliographic references are provided for all and abstracts are quoted when available. Selected highlights were extracted from the pertinent articles