154 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
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
Spherical Wavefront Near-Field DoA Estimation in THz Automotive Radar
Automotive radar at terahertz (THz) band has the potential to provide compact
design. The availability of wide bandwidth at THz-band leads to high range
resolution. Further, very narrow beamwidth arising from large arrays yields
high angular resolution up to milli-degree level direction-of-arrival (DoA)
estimation. At THz frequencies and extremely large arrays, the signal wavefront
is spherical in the near-field that renders traditional far-field DoA
estimation techniques unusable. In this work, we examine near-field DoA
estimation for THz automotive radar. We propose an algorithm using multiple
signal classification (MUSIC) to estimate target DoAs and ranges while also
taking beam-squint in near-field into account. Using an array transformation
approach, we compensate for near-field beam-squint in noise subspace
computations to construct the beam-squint-free MUSIC spectra. Numerical
experiments show the effectiveness of the proposed method to accurately
estimate the target parameters
Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data
This paper introduces a method based on a deep neural network (DNN) that is
perfectly capable of processing radar data from extremely thinned radar
apertures. The proposed DNN processing can provide both aliasing-free radar
imaging and super-resolution. The results are validated by measuring the
detection performance on realistic simulation data and by evaluating the
Point-Spread-function (PSF) and the target-separation performance on measured
point-like targets. Also, a qualitative evaluation of a typical automotive
scene is conducted. It is shown that this approach can outperform
state-of-the-art subspace algorithms and also other existing machine learning
solutions. The presented results suggest that machine learning approaches
trained with sufficiently sophisticated virtual input data are a very promising
alternative to compressed sensing and subspace approaches in radar signal
processing. The key to this performance is that the DNN is trained using
realistic simulation data that perfectly mimic a given sparse antenna radar
array hardware as the input. As ground truth, ultra-high resolution data from
an enhanced virtual radar are simulated. Contrary to other work, the DNN
utilizes the complete radar cube and not only the antenna channel information
at certain range-Doppler detections. After training, the proposed DNN is
capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers
nearly the same resolution and image quality as would be achieved with a fully
occupied array.Comment: 15 pages, 12 figures, Accepted to IEEE Journal of Microwave
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