Multi-Panel Sparse Base Station Design with Physical Antenna Effects in Massive MU-MIMO

A novel base station antenna (BSA) configuration is presented to mitigate degrading physical antenna effects in massive multiple-input multiple-output (MIMO) systems, while minimizing implementation complexities. Instead of using a commonly considered single antenna panel comprising of many elements covering a wide field-of-view (FOV) of 120 degrees, L tilted panels are used employing L times fewer elements and L times smaller FOV per panel. The spatial resolution of each panel is enhanced by employing sparse arrays with suppressed (grating-lobe) radiation outside its corresponding FOV. Therefore, more directive antenna elements can be deployed in each panel to compensate for the effective isotropic radiated power (EIRP) reduction. While sectorisation reduces the antenna gain variation in 120 degrees FOV, cooperation among multiple panels in downlink beamforming is seen to be capable of inter-panel interference suppression for sum-rate enhancement. A network model is used as a multi-user (MU) MIMO simulator incorporating both antenna and channel effects. It is shown that when the number of base station antennas is ten times the number of users, the average downlink sum-rate in pure line-of-sight (LOS), rich and poor multipath environments is increased up to 60.2%, 23% and 11.1%, respectively, by multi-panel sparse arrays applying zero-forcing (ZF) precoding

Sparse Array Architectures for Wireless Communication and Radar Applications

This thesis focuses on sparse array architectures for the next generation of wireless communication, known as fifth-generation (5G), and automotive radar direction-of-arrival (DOA) estimation. For both applications, array spatial resolution plays a critical role to better distinguish multiple users/sources. Two novel base station antenna (BSA) configurations and a new sparse MIMO radar, which both outperform their conventional counterparts, are proposed.\ua0We first develop a multi-user (MU) multiple-input multiple-output (MIMO) simulation platform which incorporates both antenna and channel effects based on standard network theory. The combined transmitter-channel-receiver is modeled by cascading Z-matrices to interrelate the port voltages/currents to one another in the linear network model. The herein formulated channel matrix includes physical antenna and channel effects and thus enables us to compute the actual port powers. This is in contrast with the assumptions of isotropic radiators without mutual coupling effects which are commonly being used in the Wireless Community.\ua0Since it is observed in our model that the sum-rate of a MU-MIMO system can be adversely affected by antenna gain pattern variations, a novel BSA configuration is proposed by combining field-of-view (FOV) sectorization, array panelization and array sparsification. A multi-panel BSA, equipped with sparse arrays in each panel, is presented with the aim of reducing the implementation complexities and maintaining or even improving the sum-rate.\ua0We also propose a capacity-driven array synthesis in the presence of mutual coupling for a MU-MIMO system. We show that the appearance of\ua0grating lobes is degrading the system capacity and cannot be disregarded in a MU communication, where space division\ua0multiple access (SDMA) is applied. With the aid of sparsity and aperiodicity, the adverse effects of grating lobes and mutual coupling\ua0are suppressed and capacity is enhanced. This is performed by proposing a two-phase optimization. In Phase I, the problem\ua0is relaxed to a convex optimization by ignoring the mutual coupling and weakening the constraints. The solution of Phase I\ua0is used as the initial guess for the genetic algorithm (GA) in phase II, where the mutual coupling is taken into account. The\ua0proposed hybrid algorithm outperforms the conventional GA with random initialization.\ua0A novel sparse MIMO radar is presented for high-resolution single snapshot DOA estimation. Both transmit and receive arrays are divided into two uniform arrays with increased inter-element spacings to generate two uniform sparse virtual arrays. Since virtual arrays are uniform, conventional spatial smoothing can be applied for temporal correlation suppression among sources. Afterwards, the spatially smoothed virtual arrays satisfy the co-primality concept to avoid DOA ambiguities. Physical antenna effects are incorporated in the received signal model and their effects on the DOA estimation performance are investigated

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