99 research outputs found
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Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G
Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems
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
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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
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