283 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
Variational Inference-based Joint Interference Mitigation and OFDM Equalization Under High Mobility
In OFDM-based spectrum sharing networks, due to inefficient coordination or imperfect spectrum sensing, the signals from femtocells or secondary users appear as interference in a subset of subcarriers of the primary systems. Together with the inter-carrier interference (ICI) introduced by high mobility, equalizing one subcarrier now depends not only on whether interference exists, but also the neighboring subcarrier data. In this letter, we propose a novel approach to iteratively learn the statistics of noise plus interference across different subcarriers, and refine the soft data estimates of each subcarrier based on the variational inference. Simulation results show that the pro- posed method avoids the error floor effect, which is exhibited by existing algorithms without considering interference mitigation, and performs close to the ideal case with perfect ICI cancelation and knowledge of noise plus interference powers for optimal maximum a posteriori probability (MAP) equalizer.published_or_final_versio
Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility
In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. © 2006 IEEE.published_or_final_versio
Signal Detection for OFDM-Based Virtual MIMO Systems under Unknown Doubly Selective Channels, Multiple Interferences and Phase Noises
In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.published_or_final_versio
Effect of Synchronizing Coordinated Base Stations on Phase Noise Estimation
In this paper, we study the problem of oscillator phase noise (PN) estimation
in coordinated multi-point (CoMP) transmission systems. Specifically, we
investigate the effect of phase synchronization between coordinated base
stations (BSs) on PN estimation at the user receiver (downlink channel). In
this respect, the Bayesian Cram\'er-Rao bound for PN estimation is derived
which is a function of the level of phase synchronization between the
coordinated BSs. Results show that quality of BS synchronization has a
significant effect on the PN estimation
Constrained Phase Noise Estimation in OFDM Using Scattered Pilots Without Decision Feedback
In this paper, we consider an OFDM radio link corrupted by oscillator phase
noise in the receiver, namely the problem of estimating and compensating for
the impairment. To lessen the computational burden and delay incurred onto the
receiver, we estimate phase noise using only scattered pilot subcarriers, i.e.,
no tentative symbol decisions are used in obtaining and improving the phase
noise estimate. In particular, the phase noise estimation problem is posed as
an unconstrained optimization problem whose minimizer suffers from the
so-called amplitude and phase estimation error. These errors arise due to
receiver noise, estimation from limited scattered pilot subcarriers and
estimation using a dimensionality reduction model. It is empirically shown
that, at high signal-to-noise-ratios, the phase estimation error is small. To
reduce the amplitude estimation error, we restrict the minimizer to be drawn
from the so-called phase noise geometry set when minimizing the cost function.
The resulting optimization problem is a non-convex program. However, using the
S-procedure for quadratic equalities, we show that the optimal solution can be
obtained by solving the convex dual problem. We also consider a less complex
heuristic scheme that achieves the same objective of restricting the minimizer
to the phase noise geometry set. Through simulations, we demonstrate improved
coded bit-error-rate and phase noise estimation error performance when
enforcing the phase noise geometry. For example, at high
signal-to-noise-ratios, the probability density function of the phase noise
estimation error exhibits thinner tails which results in lower bit-error-rate
A New Method For Increasing the Accuracy of EM-based Channel Estimation
It was recently shown that the detection performance can be significantly improved if the statistics of channel estimation errors are available and properly used at the receiver. Although in pilot-only channel estimation it is usually straightforward to characterize the statistics of channel estimation errors, this is not the case for the class of data-aided (semi-blind) channel estimation techniques. In this paper, we focus on the widely-used data-aided channel estimation techniques based on the expectation-maximization (EM) algorithm. This is achieved by a modified formulation of the EM algorithm which provides the receiver with the statistics of the estimation errors and properly using this additional information. Simulation results show that the proposed data-aided estimator outperform its classical counterparts in terms of accuracy, without requiring additional complexity at the receiver
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