158 research outputs found
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO
We propose a new iterative detection and
decoding (IDD) algorithm for multiple-input multiple-output
(MIMO) based on expectation propagation (EP) with application
to massive MIMO scenarios. Two main results are presented.
We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO
detector and the soft output of the channel decoder. With this
novel approach, denoted by double-EP (DEP), the convergence
is very much improved with a computational complexity just
two times the one of the linear minimum mean square error
(LMMSE) based IDD, as illustrated by the included experiments.
Besides, as in the LMMSE MIMO detector, when the number of
antennas increases, the computational cost of the matrix inversion
operation required by the DEP becomes unaffordable. In this
work we also develop approaches of DEP where the mean and
the covariance matrix of the posterior are approximated by using
the Gauss-Seidel and Neumann series methods, respectively. This
low-complexity DEP detector has quadratic complexity in the
number of antennas, as the low-complexity LMMSE techniques.
Experimental results show that the new low-complexity DEP
achieves the performance of the DEP as the ratio between the
number of transmitting and receiving antennas decreasesProyectos Nacionales Españoles del Gobierno de España TEC2017-90093-C3-2-
Symbol Detection in 5G and Beyond Networks
Beyond 5G networks are expected to provide excellent quality of service in terms of delay and reliability for users, where they could travel with high mobility (e.g., 500 km/h) and achieve better spectral efficiency. To support these demands, advanced wireless architectures have been proposed, i.e., orthogonal time frequency space (OTFS) modulation and multiple-input multiple-output (MIMO), which are used to handle high mobility communications and increase the network’s spectral efficiency, respectively. Symbol detection in these advanced wireless architectures is essential to satisfy reliability requirements. On the one hand, the optimal maximum likelihood symbol detector is prohibitively complex as its complexity is non-deterministic polynomial-time (NP)-hard. On the other hand, conventional low-complexity symbol detectors pose a significant performance loss compared to the optimal detector. Thus they cannot be used to satisfy high-reliability requirements. One solution to this problem is to develop a low-complexity algorithm that can achieve near-optimal performance in a particular scenario (e.g., M-MIMO). Nevertheless, there are some cases where we cannot design low-complexity algorithms. To alleviate this issue, deep learning networks can be integrated into an existing algorithm and trained using a dataset obtained by simulating a corresponding scenario. In this thesis, we design symbol detectors for advanced wireless architectures (i.e., MIMO and OTFS) to support an excellent quality of service in terms of delay and reliability and better spectral efficiency beyond 5G networks
Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems
The tradeoff between the computational complexity and system performance in multipleinput
multiple-output (MIMO) wireless communication systems is critical to practical applications.
In this dissertation, we investigate efficient low-complexity data detection schemes
from conventional small-scale to recent large-scale MIMO systems, with the targeted applications
in terrestrial wireless communication systems, vehicular networks, and underwater
acoustic communication systems.
In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput
multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput
multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA)
systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition
(SQRD) based turbo equalization scheme under imperfect channel estimation.
We demonstrate the performance enhancement of the proposed scheme over the conventional
minimum mean-square error (MMSE) based turbo equalization scheme in terms of
system bit error rate (BER) and convergence performance. Furthermore, by jointly considering
channel estimation error and the a priori information from the channel decoder,
we develop low-complexity turbo equalization schemes conditioned on channel estimate for
MIMO systems. Our proposed methods generalize the expressions used for MMSE and
MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special
cases. In addition, we extend the SQRD-based soft interference cancelation scheme
to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show
an improved system BER performance of the proposed turbo detection scheme over the
conventional MMSE-based detection scheme.
In the large-scale MIMO scenario, we focus on low-complexity detection schemes because
computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive
MIMO detection schemes. We demonstrate the applicability of the stair matrix through
the study of the convergence conditions. We then investigate the system performance and
demonstrate that the convergence rate and the system BER are much improved over the
diagonal matrix based approach with the same system configuration. We further investigate
low-complexity and fast processing detection schemes for massive MIMO systems where a
block diagonal matrix is utilized in the development. Using a parallel processing structure,
the processing time can be much reduced. We investigate the convergence performance
through both the probability that the convergence conditions are satisfied and the convergence
rate, and evaluate the system performance in terms of computational complexity,
system BER, and the overall processing time. Using our proposed approach, we extend
the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic
(UWA) communications. By utilizing a recently proposed computational efficient statistic
UWA channel model, we show that the proposed scheme can effectively approach the system
performance of the original Gauss-Seidel method, but with much reduced processing delay
Channel estimation techniques for next generation mobile communication systems
Mención Internacional en el título de doctorWe are witnessing a revolution in wireless technology, where the society is demanding new
services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging
services not only are demanding an enormous increase of data rates in the range of 1000 times
higher, but also they are real-time applications with an important delay constraint. Furthermore,
an unprecedented number of different machine-type devices will be also connected to the network,
known as Internet of Things (IoT), where they will be transmitting real-time measurements from
different sensors. In this context, the Third Generation Partnership Project (3GPP) has already
developed the new Fifth Generation (5G) of mobile communication systems, which should be
capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as:
enhanced mobile broad-band (eMBB) services, massive machine type communications (mMTC)
and ultra reliable low latency communications (URLLC).
In order to accomplish all the mentioned requirements, it is important to develop new key
radio technologies capable of exploiting the wireless environment with a higher efficiency. Orthogonal
frequency division multiplexing (OFDM) is the most widely used waveform by the industry,
however, it also exhibits high side lobes reducing considerably the spectral efficiency. Therefore,
filter-bank multi-carrier combined with offset quadrature amplitude modulation (FBMC-OQAM)
is a waveform candidate to replace OFDM due to the fact that it provides extremely low out-ofband
emissions (OBE). The traditional spectrum frequencies range is close to saturation, thus,
there is a need to exploit higher bands, such as millimeter waves (mm-Wave), making possible the
deployment of ultra broad-band services. However, the high path loss in these bands increases the
blockage probability of the radio-link, forcing us to use massive multiple-input multiple-output
(MIMO) systems in order to increase either the diversity or capacity of the overall link.
All these emergent radio technologies can make 5G a reality. However, all their benefits can be
only exploited under the knowledge and availability of the channel state information (CSI) in order
to compensate the effects produced by the channel. The channel estimation process is a well known
procedure in the area of signal processing for communications, where it is a challenging task due to the fact that we have to obtain a good estimator, maintaining at the same time the efficiency and
reduced complexity of the system and obtaining the results as fast as possible. In FBMC-OQAM,
there are several proposed channel estimation techniques, however, all of them required a high
number of operations in order to deal with the self-interference produced by the prototype filter,
hence, increasing the complexity. The existing channel estimation and equalization techniques for
massive MIMO are in general too complex due to the large number of antennas, where we must
estimate the channel response of each antenna of the array and perform some prohibitive matrix
inversions to obtain the equalizers. Besides, for the particular case of mm-Wave, the existing
techniques either do not adapt well to the dynamic ranges of signal-to-noise ratio (SNR) scenarios
or they assume some approximations which reduce the quality of the estimator.
In this thesis, we focus on the channel estimation for different emerging techniques that are
capable of obtaining a better performance with a lower number of operations, suitable for low complexity
devices and for URLLC. Firstly, we proposed new pilot sequences for FBMC-OQAM
enabling the use of a simple averaging process in order to obtain the CSI. We show that our
technique outperforms the existing ones in terms of complexity and performance. Secondly, we
propose an alternative low-complexity way of computing the precoding/postcoding equalizer under
the scenario of massive MIMO, keeping the quality of the estimator. Finally, we propose a new
channel estimation technique for massive MIMO for mm-Wave, capable of adapting to very variable
scenarios in terms of SNR and outperforming the existing techniques. We provide some analysis
of the mean squared error (MSE) and complexity of each proposed technique. Furthermore,
some numerical results are given in order to provide a better understanding of the problem and
solutions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Antonia María Tulino.- Secretario: Máximo Morales Céspedes.- Vocal: Octavia A. Dobr
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array
gain and spatial multiplexing offered by Massive MIMO. Since its inception, the
coherent interference caused by pilot contamination has been believed to create
a finite capacity limit, as the number of antennas goes to infinity. In this
paper, we prove that this is incorrect and an artifact from using simplistic
channel models and suboptimal precoding/combining schemes. We show that with
multicell MMSE precoding/combining and a tiny amount of spatial channel
correlation or large-scale fading variations over the array, the capacity
increases without bound as the number of antennas increases, even under pilot
contamination. More precisely, the result holds when the channel covariance
matrices of the contaminating users are asymptotically linearly independent,
which is generally the case. If also the diagonals of the covariance matrices
are linearly independent, it is sufficient to know these diagonals (and not the
full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages,
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