92 research outputs found
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones
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.
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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
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