3,008 research outputs found
Graph-Based Decoding in the Presence of ISI
We propose an approximation of maximum-likelihood detection in ISI channels
based on linear programming or message passing. We convert the detection
problem into a binary decoding problem, which can be easily combined with LDPC
decoding. We show that, for a certain class of channels and in the absence of
coding, the proposed technique provides the exact ML solution without an
exponential complexity in the size of channel memory, while for some other
channels, this method has a non-diminishing probability of failure as SNR
increases. Some analysis is provided for the error events of the proposed
technique under linear programming.Comment: 25 pages, 8 figures, Submitted to IEEE Transactions on Information
Theor
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
A Unified Framework for Linear-Programming Based Communication Receivers
It is shown that a large class of communication systems which admit a
sum-product algorithm (SPA) based receiver also admit a corresponding
linear-programming (LP) based receiver. The two receivers have a relationship
defined by the local structure of the underlying graphical model, and are
inhibited by the same phenomenon, which we call 'pseudoconfigurations'. This
concept is a generalization of the concept of 'pseudocodewords' for linear
codes. It is proved that the LP receiver has the 'maximum likelihood
certificate' property, and that the receiver output is the lowest cost
pseudoconfiguration. Equivalence of graph-cover pseudoconfigurations and
linear-programming pseudoconfigurations is also proved. A concept of 'system
pseudodistance' is defined which generalizes the existing concept of
pseudodistance for binary and nonbinary linear codes. It is demonstrated how
the LP design technique may be applied to the problem of joint equalization and
decoding of coded transmissions over a frequency selective channel, and a
simulation-based analysis of the error events of the resulting LP receiver is
also provided. For this particular application, the proposed LP receiver is
shown to be competitive with other receivers, and to be capable of
outperforming turbo equalization in bit and frame error rate performance.Comment: 13 pages, 6 figures. To appear in the IEEE Transactions on
Communication
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