25,812 research outputs found
Gaussian Belief Propagation Based Multiuser Detection
In this work, we present a novel construction for solving the linear
multiuser detection problem using the Gaussian Belief Propagation algorithm.
Our algorithm yields an efficient, iterative and distributed implementation of
the MMSE detector. We compare our algorithm's performance to a recent result
and show an improved memory consumption, reduced computation steps and a
reduction in the number of sent messages. We prove that recent work by
Montanari et al. is an instance of our general algorithm, providing new
convergence results for both algorithms.Comment: 6 pages, 1 figures, appeared in the 2008 IEEE International Symposium
on Information Theory, Toronto, July 200
Improving the Spectral Efficiency of Nonlinear Satellite Systems through Time-Frequency Packing and Advanced Processing
We consider realistic satellite communications systems for broadband and
broadcasting applications, based on frequency-division-multiplexed linear
modulations, where spectral efficiency is one of the main figures of merit. For
these systems, we investigate their ultimate performance limits by using a
framework to compute the spectral efficiency when suboptimal receivers are
adopted and evaluating the performance improvements that can be obtained
through the adoption of the time-frequency packing technique. Our analysis
reveals that introducing controlled interference can significantly increase the
efficiency of these systems. Moreover, if a receiver which is able to account
for the interference and the nonlinear impairments is adopted, rather than a
classical predistorter at the transmitter coupled with a simpler receiver, the
benefits in terms of spectral efficiency can be even larger. Finally, we
consider practical coded schemes and show the potential advantages of the
optimized signaling formats when combined with iterative detection/decoding.Comment: 8 pages, 8 figure
Trellis-Based Equalization for Sparse ISI Channels Revisited
Sparse intersymbol-interference (ISI) channels are encountered in a variety
of high-data-rate communication systems. Such channels have a large channel
memory length, but only a small number of significant channel coefficients. In
this paper, trellis-based equalization of sparse ISI channels is revisited. Due
to the large channel memory length, the complexity of maximum-likelihood
detection, e.g., by means of the Viterbi algorithm (VA), is normally
prohibitive. In the first part of the paper, a unified framework based on
factor graphs is presented for complexity reduction without loss of optimality.
In this new context, two known reduced-complexity algorithms for sparse ISI
channels are recapitulated: The multi-trellis VA (M-VA) and the
parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does
not lead to a reduced computational complexity. The P-VA, on the other hand,
leads to a significant complexity reduction, but can only be applied for a
certain class of sparse channels. In the second part of the paper, a unified
approach is investigated to tackle general sparse channels: It is shown that
the use of a linear filter at the receiver renders the application of standard
reduced-state trellis-based equalizer algorithms feasible, without significant
loss of optimality. Numerical results verify the efficiency of the proposed
receiver structure.Comment: To be presented at the 2005 IEEE Int. Symp. Inform. Theory (ISIT
2005), September 4-9, 2005, Adelaide, Australi
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