1,648 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
MIMO radar with broadband waveforms: Smearing filter banks and 2D virtual arrays
In this paper MIMO radars with broadband
waveforms are considered. A time domain viewpoint is
taken, which allows frequency invariant beamforming
with a filter bank called the smearing filter bank. Motivated
by recent work on two dimensional arrays to
obtain frequency invariant one dimensional beams, the
generation of two dimensional virtual arrays from one
dimensional ULAs is also considered. It is also argued
that when the smearing filter bank is appropriately used,
frequency invariant 2D beams can be generated
Fast Implementation of Transmit Beamforming for Colocated MIMO Radar
Multiple-input Multiple-output (MIMO) radars benefit from spatial and waveform diversities to improve the performance potential. Phased array radars transmit scaled versions of a single waveform thereby limiting the transmit degrees of freedom to one. However MIMO radars transmit diverse waveforms from different transmit array elements thereby increasing the degrees of freedom to form flexible transmit beampatterns. The transmit beampattern of a colocated MIMO radar depends on the zero-lag correlation matrix of different transmit waveforms. Many solutions have been developed for designing the signal correlation matrix to achieve a desired transmit beampattern based on optimization algorithms in the literature. In this paper, a fast algorithm for designing the correlation matrix of the transmit waveforms is developed that allows the next generation radars to form flexible beampatterns in real-time. An efficient method for sidelobe control with negligible increase in mainlobe width is also presented
Hybrid Beamforming With Sub-arrayed MIMO Radar: Enabling Joint Sensing and Communication at mmWave Band
In this paper, we propose a beamforming design for dual-functional
radar-communication (DFRC) systems at the millimeter wave (mmWave) band, where
hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited.
We assume that a base station (BS) is serving a user equipment (UE) located in
a Non-Line-of-Sight (NLoS) channel, which in the meantime actively detects
multiple targets located in a Line-of-Sight (LoS) channel. Given the optimal
communication beamformer and the desired radar beampattern, we propose to
design the analog and digital beamformers under non-convex constant-modulus
(CM) and power constraints, such that the weighted summation of the
communication and radar beamforming errors is minimized. The formulated
optimization problem can be decomposed into three subproblems, and is solved by
the alternating minimization approach. Numerical simulations verify the
feasibility of the proposed beamforming design, and show that our approach
offers a favorable performance tradeoff between sensing and communication.Comment: 5 pages, 2 figures, submitted to ICASSP 201
Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity
This paper introduces innovative SAR system
concepts for the acquisition of high resolution radar images with
wide swath coverage from spaceborne platforms. The new concepts
rely on the combination of advanced multi-channel SAR front-end
architectures with novel operational modes. The architectures
differ regarding their implementation complexity and it is shown
that even a low number of channels is already well suited to
significantly improve the imaging performance and to overcome
fundamental limitations inherent to classical SAR systems. The
more advanced concepts employ a multidimensional encoding of
the transmitted waveforms to further improve the performance
and to enable a new class of hybrid SAR imaging modes that are
well suited to satisfy hitherto incompatible user requirements for
frequent monitoring and detailed mapping. Implementation
specific issues will be discussed and examples demonstrate the
potential of the new techniques for different remote sensing
applications
MIMO radar space–time adaptive processing using prolate spheroidal wave functions
In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space–time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity
Rationale for and design of a generic tiled hierarchical phased array beamforming architecture
The purpose of the phased array beamforming project is to develop a generic flexible efficient phased array receiver platform, using a mixed signal hardware/software-codesign approach. The results will be applicable to any radio (RF) system, but we will focus on satellite receiver (DVB-S) and radar applications. We will present a preliminary mapping of beamforming processing on a tiled architecture and determine its scalability.\ud
\ud
The functionality, size and cost constraints imply an integrated mixed signal CMOS solution. For a generic flexible multi-standard solution, a software defined radio approach is taken. Because a scalable and dependable solution is needed, a tiled hierarchical architecture is proposed with reconfigurable hardware to regain flexibility. A mapping is provided of beamforming on the proposed architecture. The advantages and disadvantages of each solution are discussed with respect to applicability and scalability.\ud
\ud
Different beamforming processing solutions can be mapped on the same proposed tiled hierarchical architecture. This provides a flexible, scalable and reconfigurable solution for a wide application domain. Beamforming is a data-driven streaming process which lends itself well for a regular scalable architecture. Beamsteering on the other hand is much more control-oriented and future work will focus on how to support beamsteering on the proposed architecture as well
- …