636 research outputs found
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
Sequential decoding on intersymbol interference channels with application to magnetic recording
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1990.Thesis (Master's) -- Bilkent University, 1990.Includes bibliographical references leaves 27-28In this work we treat sequential decoding in the problem of sequence estimation on
intersymbol interference ( ISI ) channels. We consider the magnetic recording channel
as the particular ISI channel and investigate the coding gains that can be achieved with
sequential decoding for different information densities. Since the cutoff rate determines
this quantity , we find lower bounds to the cutoff rate.
The symmetric cutoff rate is computed as a theoretical lower bound and practical
lower bounds are found through simulations. Since the optimum decoding metric is
impractical, a sub-optimum metric has been used in the simulations. The results show
that this metric can not achieve the cutoff rate in general, but still its performance is
not far from that of the optimum metric.
We compare the results to those of Immink[9] and see that one can achieve positive
coding gains at information densities of practical interest where other practical codes
used in magnetic recording show coding loss.Alanyalı, MuratM.S
A Low-Complexity Graph-Based LMMSE Receiver Designed for Colored Noise Induced by FTN-Signaling
We propose a low complexity graph-based linear minimum mean square error
(LMMSE) equalizer which considers both the intersymbol interference (ISI) and
the effect of non-white noise inherent in Faster-than-Nyquist (FTN) signaling.
In order to incorporate the statistics of noise signal into the factor graph
over which the LMMSE algorithm is implemented, we suggest a method that models
it as an autoregressive (AR) process. Furthermore, we develop a new mechanism
for exchange of information between the proposed equalizer and the channel
decoder through turbo iterations. Based on these improvements, we show that the
proposed low complexity receiver structure performs close to the optimal
decoder operating in ISI-free ideal scenario without FTN signaling through
simulations.Comment: 6 pages, 6 figures, IEEE Wireless Communications and Networking
Conference 2014, Istanbul, Turke
Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels
Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared
Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels
Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared
Power and Bandwidth Efficient Coded Modulation for Linear Gaussian Channels
A scheme for power- and bandwidth-efficient communication on the linear Gaussian channel is proposed. A scenario is assumed in which the channel is stationary in time and the channel characteristics are known at the transmitter. Using interleaving, the linear Gaussian channel with its intersymbol interference is decomposed into a set of memoryless subchannels. Each subchannel is further decomposed into parallel binary memoryless channels, to enable the use of binary codes. Code bits from these parallel binary channels are mapped to higher-order near-Gaussian distributed constellation symbols. At the receiver, the code bits are detected and decoded in a multistage fashion. The scheme is demonstrated on a simple instance of the linear Gaussian channel. Simulations show that the scheme achieves reliable communication at 1.2 dB away from the Shannon capacity using a moderate number of subchannels
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