111 research outputs found
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation
The design of message passing algorithms on factor graphs has been proven to
be an effective manner to implement channel estimation in wireless
communication systems. In Bayesian approaches, a prior probability model that
accurately matches the channel characteristics can effectively improve
estimation performance. In this work, we study the channel estimation problem
in a frequency division duplexing (FDD) downlink massive multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
system. As the prior probability, we propose the Markov chain two-state
Gaussian mixture with large variance difference (TSGM-LVD) model to exploit the
structured sparsity in the angle-frequency domain of the massive MIMO-OFDM
channel. In addition, we present a new method to derive the hybrid message
passing (HMP) rule, which can calculate the message with mixed linear and
non-linear model. To the best of the authors' knowledge, we are the first to
apply the HMP rule to practical communication systems, designing the
HMP-TSGM-LVD algorithm under the structured turbo-compressed sensing (STCS)
framework. Simulation results demonstrate that the proposed HMP-TSGM-LVD
algorithm converges faster and outperforms its counterparts under a wide range
of simulation settings
A Variational Inference Framework for Soft-In-Soft-Out Detection in Multiple Access Channels
We propose a unified framework for deriving and studying soft-in-soft-out
(SISO) detection in interference channels using the concept of variational
inference. The proposed framework may be used in multiple-access interference
(MAI), inter-symbol interference (ISI), and multiple-input multiple-outpu
(MIMO) channels. Without loss of generality, we will focus our attention on
turbo multiuser detection, to facilitate a more concrete discussion. It is
shown that, with some loss of optimality, variational inference avoids the
exponential complexity of a posteriori probability (APP) detection by
optimizing a closely-related, but much more manageable, objective function
called variational free energy. In addition to its systematic appeal, there are
several other advantages to this viewpoint. First of all, it provides unified
and rigorous justifications for numerous detectors that were proposed on
radically different grounds, and facilitates convenient joint detection and
decoding (utilizing the turbo principle) when error-control codes are
incorporated. Secondly, efficient joint parameter estimation and data detection
is possible via the variational expectation maximization (EM) algorithm, such
that the detrimental effect of inaccurate channel knowledge at the receiver may
be dealt with systematically. We are also able to extend BPSK-based SISO
detection schemes to arbitrary square QAM constellations in a rigorous manner
using a variational argument.Comment: Submitted to Transactions on Information Theor
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to
be used in next-generations wireless systems. This led to a large body of
research work that applies ML techniques to solve problems in different layers
of the wireless transmission link. However, most of these applications rely on
supervised learning which assumes that the source (training) and target (test)
data are independent and identically distributed (i.i.d). This assumption is
often violated in the real world due to domain or distribution shifts between
the source and the target data. Thus, it is important to ensure that these
algorithms generalize to out-of-distribution (OOD) data. In this context,
domain generalization (DG) tackles the OOD-related issues by learning models on
different and distinct source domains/datasets with generalization capabilities
to unseen new domains without additional finetuning. Motivated by the
importance of DG requirements for wireless applications, we present a
comprehensive overview of the recent developments in DG and the different
sources of domain shift. We also summarize the existing DG methods and review
their applications in selected wireless communication problems, and conclude
with insights and open questions
Recent Advances in Wireless Communications and Networks
This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters
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