2,512 research outputs found
Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models
In this paper, we deal with low-complexity near-optimal
detection/equalization in large-dimension multiple-input multiple-output
inter-symbol interference (MIMO-ISI) channels using message passing on
graphical models. A key contribution in the paper is the demonstration that
near-optimal performance in MIMO-ISI channels with large dimensions can be
achieved at low complexities through simple yet effective
simplifications/approximations, although the graphical models that represent
MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1)
use of Markov Random Field (MRF) based graphical model with pairwise
interaction, in conjunction with {\em message/belief damping}, and 2) use of
Factor Graph (FG) based graphical model with {\em Gaussian approximation of
interference} (GAI). The per-symbol complexities are and
for the MRF and the FG with GAI approaches, respectively, where
and denote the number of channel uses per frame, and number of transmit
antennas, respectively. These low-complexities are quite attractive for large
dimensions, i.e., for large . From a performance perspective, these
algorithms are even more interesting in large-dimensions since they achieve
increasingly closer to optimum detection performance for increasing .
Also, we show that these message passing algorithms can be used in an iterative
manner with local neighborhood search algorithms to improve the
reliability/performance of -QAM symbol detection
Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays
This work analyzes the performance of the implementable detectors for
multiple-input-multiple-output (MIMO) orthogonal frequency division
multiplexing (OFDM) technique under specific and realistic operation system
condi- tions, including antenna correlation and array configuration.
Time-domain channel model has been used to evaluate the system performance
under realistic communication channel and system scenarios, including different
channel correlation, modulation order and antenna arrays configurations. A
bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high
performance combined with high capacity systems and manageable computational
complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel
selectivity effect, while the impact of the number of antennas, adoption of
linear against heuristic-based detection schemes, and the spatial correlation
effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM
context.Comment: 26 pgs, 16 figures and 5 table
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
Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference
The problem of modulation classification for a multiple-antenna (MIMO) system
employing orthogonal frequency division multiplexing (OFDM) is investigated
under the assumption of unknown frequency-selective fading channels and
signal-to-noise ratio (SNR). The classification problem is formulated as a
Bayesian inference task, and solutions are proposed based on Gibbs sampling and
mean field variational inference. The proposed methods rely on a selection of
the prior distributions that adopts a latent Dirichlet model for the modulation
type and on the Bayesian network formalism. The Gibbs sampling method converges
to the optimal Bayesian solution and, using numerical results, its accuracy is
seen to improve for small sample sizes when switching to the mean field
variational inference technique after a number of iterations. The speed of
convergence is shown to improve via annealing and random restarts. While most
of the literature on modulation classification assume that the channels are
flat fading, that the number of receive antennas is no less than that of
transmit antennas, and that a large number of observed data symbols are
available, the proposed methods perform well under more general conditions.
Finally, the proposed Bayesian methods are demonstrated to improve over
existing non-Bayesian approaches based on independent component analysis and on
prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog
Low Complexity V-BLAST MIMO-OFDM Detector by Successive Iterations Reduction
V-BLAST detection method suffers large computational complexity due to its
successive detection of symbols. In this paper, we propose a modified V-BLAST
algorithm to decrease the computational complexity by reducing the number of
detection iterations required in MIMO communication systems. We begin by
showing the existence of a maximum number of iterations, beyond which, no
significant improvement is obtained. We establish a criterion for the number of
maximum effective iterations. We propose a modified algorithm that uses the
measured SNR to dynamically set the number of iterations to achieve an
acceptable bit-error rate. Then, we replace the feedback algorithm with an
approximate linear function to reduce the complexity. Simulations show that
significant reduction in computational complexity is achieved compared to the
ordinary V-BLAST, while maintaining a good BER performance.Comment: 6 pages, 7 figures, 2 tables. The final publication is available at
www.aece.r
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