643 research outputs found
On the Derivation of Optimal Partial Successive Interference Cancellation
The necessity of accurate channel estimation for Successive and Parallel
Interference Cancellation is well known. Iterative channel estimation and
channel decoding (for instance by means of the Expectation-Maximization
algorithm) is particularly important for these multiuser detection schemes in
the presence of time varying channels, where a high density of pilots is
necessary to track the channel. This paper designs a method to analytically
derive a weighting factor , necessary to improve the efficiency of
interference cancellation in the presence of poor channel estimates. Moreover,
this weighting factor effectively mitigates the presence of incorrect decisions
at the output of the channel decoder. The analysis provides insight into the
properties of such interference cancellation scheme and the proposed approach
significantly increases the effectiveness of Successive Interference
Cancellation under the presence of channel estimation errors, which leads to
gains of up to 3 dB.Comment: IEEE GLOBECOM 201
Low-Complexity Joint Channel Estimation and List Decoding of Short Codes
A pilot-assisted transmission (PAT) scheme is proposed for short
blocklengths, where the pilots are used only to derive an initial channel
estimate for the list construction step. The final decision of the message is
obtained by applying a non-coherent decoding metric to the codewords composing
the list. This allows one to use very few pilots, thus reducing the channel
estimation overhead. The method is applied to an ordered statistics decoder for
communication over a Rayleigh block-fading channel. Gains of up to dB as
compared to traditional PAT schemes are demonstrated for short codes with QPSK
signaling. The approach can be generalized to other list decoders, e.g., to
list decoding of polar codes.Comment: Accepted at the 12th International ITG Conference on Systems,
Communications and Coding (SCC 2019), Rostock, German
Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference
We design iterative receiver schemes for a generic wireless communication
system by treating channel estimation and information decoding as an inference
problem in graphical models. We introduce a recently proposed inference
framework that combines belief propagation (BP) and the mean field (MF)
approximation and includes these algorithms as special cases. We also show that
the expectation propagation and expectation maximization algorithms can be
embedded in the BP-MF framework with slight modifications. By applying the
considered inference algorithms to our probabilistic model, we derive four
different message-passing receiver schemes. Our numerical evaluation
demonstrates that the receiver based on the BP-MF framework and its variant
based on BP-EM yield the best compromise between performance, computational
complexity and numerical stability among all candidate algorithms.Comment: Accepted for publication in the Proceedings of 2012 IEEE
International Symposium on Information Theor
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
Low-complexity a posteriori probability approximation in EM-based channel estimation for trellis-coded systems
When estimating channel parameters in linearly modulated communication systems, the iterative expectation-maximization (EM) algorithm can be used to exploit the signal energy associated with the unknown data symbols. It turns out that the channel estimation requires at each EM iteration the a posteriori probabilities (APPs) of these data symbols, resulting in a high computational complexity when channel coding is present. In this paper, we present a new approximation of the APPs of trellis-coded symbols, which is less complex and requires less memory than alternatives from literature. By means of computer simulations, we show that the Viterbi decoder that uses the EM channel estimate resulting from this APP approximation experiences a negligible degradation in frame error rate (FER) performance, as compared to using the exact APPs in the channel estimation process
A theoretical framework for soft-information-based synchronization in iterative (Turbo) receivers
This contribution considers turbo synchronization, that is to say, the use of soft data information to estimate parameters like carrier phase, frequency, or timing offsets of a modulated signal within an iterative data demodulator. In turbo synchronization, the receiver exploits the soft decisions computed at each turbo decoding iteration to provide a reliable estimate of some signal parameters. The aim of our paper is to show that such “turbo-estimation” approach can be regarded as a special case of the expectation-maximization (EM) algorithm. This leads to a general theoretical framework for turbo synchronization that allows to derive parameter estimation procedures for carrier phase and frequency offset, as well as for timing offset and signal amplitude. The proposed mathematical framework is illustrated by simulation results reported for the particular case of carrier phase and frequency offsets estimation of a turbo-coded 16-QAM signal
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