A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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

Low-resolution ADC receiver design, MIMO interference cancellation prototyping, and PHY secrecy analysis.

This dissertation studies three independent research topics in the general field of wireless communications. The first topic focuses on new receiver design with low-resolution analog-to-digital converters (ADC). In future massive multiple-input-multiple-output (MIMO) systems, multiple high-speed high-resolution ADCs will become a bottleneck for practical applications because of the hardware complexity and power consumption. One solution to this problem is to adopt low-cost low-precision ADCs instead. In Chapter II, MU-MIMO-OFDM systems only equipped with low-precision ADCs are considered. A new turbo receiver structure is proposed to improve the overall system performance. Meanwhile, ultra-low-cost communication devices can enable massive deployment of disposable wireless relays. In Chapter III, the feasibility of using a one-bit relay cluster to help a power-constrained transmitter for distant communication is investigated. Nonlinear estimators are applied to enable effective decoding. The second topic focuses prototyping and verification of a LTE and WiFi co-existence system, where the operation of LTE in unlicensed spectrum (LTE-U) is discussed. LTE-U extends the benefits of LTE and LTE Advanced to unlicensed spectrum, enabling mobile operators to offload data traffic onto unlicensed frequencies more efficiently and effectively. With LTE-U, operators can offer consumers a more robust and seamless mobile broadband experience with better coverage and higher download speeds. As the coexistence leads to considerable performance instability of both LTE and WiFi transmissions, the LTE and WiFi receivers with MIMO interference canceller are designed and prototyped to support the coexistence in Chapter IV. The third topic focuses on theoretical analysis of physical-layer secrecy with finite blocklength. Unlike upper layer security approaches, the physical-layer communication security can guarantee information-theoretic secrecy. Current studies on the physical-layer secrecy are all based on infinite blocklength. Nevertheless, these asymptotic studies are unrealistic and the finite blocklength effect is crucial for practical secrecy communication. In Chapter V, a practical analysis of secure lattice codes is provided