132 research outputs found
Improved Two-Dimensional Double Successive Projection Algorithm for Massive MIMO Detection
In a massive MIMO system, a large number of receiving antennas at the base station can simultaneously serve multiple users. Linear detectors can achieve optimal performance but require large dimensional matrix inversion, which requires a large number of arithmetic operations. Several low complexity solutions are reported in the literature. In this work, we have presented an improved two-dimensional double successive projection (I2D-DSP) algorithm for massive MIMO detection. Simulation results show that the proposed detector performs better than the conventional 2D-DSP algorithm at a lower complexity. The performance under channel correlation also improves with the I2D-DSP scheme. We further developed a soft information generation algorithm to reduce the number of magnitude comparisons. The proposed soft symbol generation method uses real domain operation and can reduce almost 90% flops and magnitude comparisons
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-
Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving
high-data rate communications are well-known, their potential advantages for
accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao
bound (CRB) on position and rotation angle estimation uncertainty from
millimeter wave signals from a single transmitter, in the presence of
scatterers. We also present a novel two-stage algorithm for position and
rotation angle estimation that attains the CRB for average to high
signal-to-noise ratio. The algorithm is based on multiple measurement vectors
matching pursuit for coarse estimation, followed by a refinement stage based on
the space-alternating generalized expectation maximization algorithm. We find
that accurate position and rotation angle estimation is possible using signals
from a single transmitter, in either line-of- sight, non-line-of-sight, or
obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages.
Also, Fig.2, Fig. 10 and Table I are adde
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
Low Complexity SLP: An Inversion-Free, Parallelizable ADMM Approach
We propose a parallel constructive interference (CI)-based symbol-level
precoding (SLP) approach for massive connectivity in the downlink of multiuser
multiple-input single-output (MU-MISO) systems, with only local channel state
information (CSI) used at each processor unit and limited information exchange
between processor units. By reformulating the power minimization (PM) SLP
problem and exploiting the separability of the corresponding reformulation, the
original problem is decomposed into several parallel subproblems via the ADMM
framework with closed-form solutions, leading to a substantial reduction in
computational complexity. The sufficient condition for guaranteeing the
convergence of the proposed approach is derived, based on which an adaptive
parameter tuning strategy is proposed to accelerate the convergence rate. To
avoid the large-dimension matrix inverse operation, an efficient algorithm is
proposed by employing the standard proximal term and by leveraging the singular
value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted
to fully eliminate the matrix inversion, and a parallel inverse-free SLP
(PIF-SLP) algorithm is finally obtained. Numerical results validate our
derivations above, and demonstrate that the proposed PIF-SLP algorithm can
significantly reduce the computational complexity compared to the
state-of-the-arts
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
Efficient distributed processing for large scale MIMO detection
In large scale multiple-input multiple-output (MIMO), high spectral and energy efficiencies comes at the expense of a high computational complexity baseband processing. Many contributions have been proposed to reduce such complexity using matrix inversion approximation techniques for instance. On the other hand, to reduce the constraint on the interconnects' bandwidth, fewer decentralized processing techniques have emerged. Here, we propose a computationally efficient technique based on embedding one single Gauss-Seidel iteration within every ADMM based detection iteration. The simulations are performed using an LTE-like TDD-OFDM frame structure and waveform, under perfect and non-perfect channel state information (CSI). Early results reveal that the proposed ADMM-GS algorithm can outperform the centralised GS based technique processing in a high SNR region and high load regime. In addition ADMM-GS' performance exhibits relatively less sensitivity to channel estimation error; a characteristic inherited from the centralised GS technique
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