643 research outputs found
Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA
In this paper, we propose a fast decoder algorithm for uniquely decodable
(errorless) code sets for overloaded synchronous optical code-division
multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a
way that the users can uniquely recover the information bits with a very simple
decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML)
decoder, which has a high computational complexity for even moderate code
lengths, the proposed decoder has much lower computational complexity.
Simulation results in terms of bit error rate (BER) demonstrate that the
performance of the proposed decoder for a given BER requires only 1-2 dB higher
signal-to-noise ratio (SNR) than the ML decoder.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0395
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
Large deviations for eigenvalues of sample covariance matrices, with applications to mobile communication systems
We study sample covariance matrices of the form , where
is a matrix with i.i.d. mean zero entries. This is a generalization
of so-called Wishart matrices, where the entries of are independent and
identically distributed standard normal random variables. Such matrices arise
in statistics as sample covariance matrices, and the high-dimensional case,
when is large, arises in the analysis of DNA experiments.
We investigate the large deviation properties of the largest and smallest
eigenvalues of when either is fixed and , or with , in the case where the squares of the
i.i.d. entries have finite exponential moments. Previous results, proving a.s.
limits of the eigenvalues, only require finite fourth moments.
Our most explicit results for large are for the case where the entries of
are with equal probability. We relate the large deviation rate
functions of the smallest and largest eigenvalue to the rate functions for
independent and identically distributed standard normal entries of . This
case is of particular interest, since it is related to the problem of the
decoding of a signal in a code division multiple access system arising in
mobile communication systems. In this example, plays the role of the number
of users in the system, and is the length of the coding sequence of each of
the users. Each user transmits at the same time and uses the same frequency,
and the codes are used to distinguish the signals of the separate users. The
results imply large deviation bounds for the probability of a bit error due to
the interference of the various users.Comment: corrected some typing errors, and extended Theorem 3.1 to Wishart
matrices; to appear in Advances of Applied Probabilit
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