64 research outputs found
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
Multi-dimensional Channel Parameter Estimation for mmWave Cylindrical Arrays
University of Technology Sydney. Faculty of Engineering and Information Technology.Millimeter-wave (mmWave) large-scale antenna arrays, standardized for the fifth-generation (5G) communication networks, have the potential to estimate channel parameters with unprecedented accuracy, due to their high temporal resolution and excellent directivity. However, most existing techniques have very high complexities in hardware and software, and they cannot effectively exploit the properties of mmWave large-array systems for channel estimation. As a result, their application in 5G mmWave large array systems is limited in practice.
This thesis develops new and efficient solutions to channel parameter estimation using large-scale mmWave uniform cylindrical arrays (UCyAs). The key contributions of this thesis are on the following four aspects:
We first present a channel compression-based channel estimation method, which reduces the computational complexity substantially at a negligible cost of estimation accuracy. By capitalizing on the sparsity of mmWave channel, the method effectively filters out the useless signal components. As a result, the dimension of the element space of the received signals can be reduced.
Next, we extend the channel estimation to the hybrid UCyA case, and design new hybrid beamformers. By exploiting the convergence property of the Bessel function, the designed beamformers can preserve the recurrence relationship of the received signals with a small number of radio frequency (RF) chains.
We then arrange the received signals in a tensor form and propose a new tensor-based channel estimation algorithm. By suppressing the receiver noises in all dimensions (time, frequency, and space), the algorithm can achieve substantially higher estimation accuracy than existing matrix-based techniques.
Finally, to reduce cost and power consumption while maintaining a high network access capability, we develop a novel nested hybrid UCyA and present the corresponding parameter estimation algorithm based on the second-order channel statistics. Simulation results show that by exploiting the sparse array technique to design the RF chain connection network, the angles of a large number of devices can be accurately estimated with much fewer RF chains than antennas.
Overall, this thesis presents several applicable UCyA design schemes and proposes the efficient channel parameter estimation algorithms. The presented new UCyAs can significantly reduce the hardware cost of the system with a marginal accuracy loss, and the proposed algorithms are capable of accurately estimating the channel parameters with low computational complexities. By employing the presented UCyAs and implementing the proposed novel algorithms cohesively, the different communication and deployment requirements of a variety of mmWave communication scenarios can be met
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
Estudo de formas de onda e conceção de algoritmos para operação conjunta de sistemas de comunicação e radar
The focus of this thesis is the processing of signals and design of algorithms
that can be used to enable radar functions in communications systems.
Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier
modulation waveform in communication systems. As a wideband
signal, OFDM improves resolution and enables spectral efficiency in radar
systems, while also improving detection performance thanks to its inherent
frequency diversity. This thesis aims to use multicarrier waveforms for radar
systems, to enable the simultaneous operation of radar and communication
functions on the same device. The thesis is divided in two parts. The first
part, studies the adaptation and application of other multicarrier waveforms
to radar functions. At the present time many studies have been carried out
to jointly use the OFDM signal for communication and radar functions, but
other waveforms have shown to be possible candidates for communication
applications. Therefore, studies on the evaluation of the application of these
same signals to radar functions are necessary. In this thesis, to demonstrate
that other multicarrier waveforms can overcome the OFDM waveform
in radar/communication (RadCom) systems, we propose the adaptation of
the filter bank multicarrier (FBMC), generalized frequency division multiplexing
(GFDM) and universal filtering multicarrier (UFMC) waveforms for radar
functions. These alternative waveforms were compared performance-wise
regarding achievable target parameter estimation performance, amount of
residual background noise in the radar image, impact of intersystem interference
and flexibility of parameterization. In the second part of the thesis,
signal processing techniques are explored to solve some of the limitations
of the use of multicarrier waveforms for RadCom systems. Radar systems
based on OFDM are promising candidates for future intelligent transport networks.
Exploring the dual functionality enabled by OFDM, we presents cooperative
methods for high-resolution delay-Doppler and direction-of-arrival
estimation. High-resolution parameter estimation is an important requirement
for automotive radar systems, especially in multi-target scenarios that
require reliable target separation performance. By exploring the cooperation
between vehicles, the studies presented in this thesis also enable the distributed
tracking of targets. The result is a highly accurate multi-target tracking
across the entire cooperative vehicle network, leading to improvements
in transport reliability and safety.O foco desta tese é o processamento de sinais e desenvolvimento de algoritmos
que podem ser utilizados para a habilitar a função de radar nos sistemas
de comunicação. OFDM (Orthogonal Frequency Division Multiplexing)
é uma forma de onda com modulação multi-portadora, popular em sistemas
de comunicação. Para sistemas de radar, O OFDM melhora a resolução e
fornece eficiência espectral, além disso sua diversidade de frequências melhora
o desempenho na detecção do radar. Essa tese tem como objetivo
utilizar formas de onda multi-portadoras para sistemas de radar, possibilitando
a operação simultânea de funções de radar e de comunicação num
mesmo dispositivo. A tese esta dividida em duas partes. Na primeira parte
da tese são realizados estudos da adaptabilidade de outras formas de onda
multi-portadora para funções de radar. Nos dias atuais, muitos estudos sobre
o uso do sinal OFDM para funções de comunicação e radar vêm sendo
realizados, no entanto, outras formas de onda mostram-se possíveis candidatas
a aplicações em sistemas de comunicação, e assim, avaliações para
funções de sistema de radar se tornam necessárias. Nesta tese, com a
intenção de demonstrar que formas de onda multi-portadoras alternativas
podem superar o OFDM nos sistemas de Radar/comunicação (RadCom),
propomos a adaptação das seguintes formas de onda: FBMC (Filter Bank
Multicarrier); GFDM (Generalized Frequency Division Multiplexing); e UFMC
(Universal Filtering Multicarrier) para funções de radar. Também produzimos
uma análise de desempenho dessas formas de onda sobre o aspecto
da estimativa de parâmetros-alvo, ruído de fundo, interferência entre sistemas
e parametrização do sistema. Na segunda parte da tese serão explorados
técnicas de processamento de sinal de forma a solucionar algumas
das limitações do uso de formas de ondas multi-portadora para sistemas
RadCom. Os sistemas de radar baseados no OFDM são candidatos
promissores para futuras redes de transporte inteligentes, porque combinam
funções de estimativa de alvo com funções de rede de comunicação
em um único sistema. Explorando a funcionalidade dupla habilitada pelo
OFDM, nesta tese, apresentamos métodos cooperativos de alta resolução
para estimar o posição, velocidade e direção dos alvos. A estimativa de
parâmetros de alta resolução é um requisito importante para sistemas de
radar automotivo, especialmente em cenários de múltiplos alvos que exigem
melhor desempenho de separação de alvos. Ao explorar a cooperação entre
veículos, os estudos apresentados nesta tese também permitem o rastreamento
distribuído de alvos. O resultado é um rastreamento multi-alvo altamente
preciso em toda a rede de veículos cooperativos, levando a melhorias
na confiabilidade e segurança do transporte.Programa Doutoral em Telecomunicaçõe
- …