229 research outputs found
Code diversity in multiple antenna wireless communication
The standard approach to the design of individual space-time codes is based
on optimizing diversity and coding gains. This geometric approach leads to
remarkable examples, such as perfect space-time block codes, for which the
complexity of Maximum Likelihood (ML) decoding is considerable. Code diversity
is an alternative and complementary approach where a small number of feedback
bits are used to select from a family of space-time codes. Different codes lead
to different induced channels at the receiver, where Channel State Information
(CSI) is used to instruct the transmitter how to choose the code. This method
of feedback provides gains associated with beamforming while minimizing the
number of feedback bits. It complements the standard approach to code design by
taking advantage of different (possibly equivalent) realizations of a
particular code design. Feedback can be combined with sub-optimal low
complexity decoding of the component codes to match ML decoding performance of
any individual code in the family. It can also be combined with ML decoding of
the component codes to improve performance beyond ML decoding performance of
any individual code. One method of implementing code diversity is the use of
feedback to adapt the phase of a transmitted signal as shown for 4 by 4
Quasi-Orthogonal Space-Time Block Code (QOSTBC) and multi-user detection using
the Alamouti code. Code diversity implemented by selecting from equivalent
variants is used to improve ML decoding performance of the Golden code. This
paper introduces a family of full rate circulant codes which can be linearly
decoded by fourier decomposition of circulant matrices within the code
diversity framework. A 3 by 3 circulant code is shown to outperform the
Alamouti code at the same transmission rate.Comment: 9 page
Coherence-Based Performance Guarantees of Orthogonal Matching Pursuit
In this paper, we present coherence-based performance guarantees of
Orthogonal Matching Pursuit (OMP) for both support recovery and signal
reconstruction of sparse signals when the measurements are corrupted by noise.
In particular, two variants of OMP either with known sparsity level or with a
stopping rule are analyzed. It is shown that if the measurement matrix
satisfies the strong coherence property, then with
, OMP will recover a -sparse signal with high
probability. In particular, the performance guarantees obtained here separate
the properties required of the measurement matrix from the properties required
of the signal, which depends critically on the minimum signal to noise ratio
rather than the power profiles of the signal. We also provide performance
guarantees for partial support recovery. Comparisons are given with other
performance guarantees for OMP using worst-case analysis and the sorted one
step thresholding algorithm.Comment: appeared at 2012 Allerton conferenc
Boolean Functions, Projection Operators and Quantum Error Correcting Codes
This paper describes a fundamental correspondence between Boolean functions
and projection operators in Hilbert space. The correspondence is widely
applicable, and it is used in this paper to provide a common mathematical
framework for the design of both additive and non-additive quantum error
correcting codes. The new framework leads to the construction of a variety of
codes including an infinite class of codes that extend the original ((5,6,2))
code found by Rains [21]. It also extends to operator quantum error correcting
codes.Comment: Submitted to IEEE Transactions on Information Theory, October 2006,
to appear in IEEE Transactions on Information Theory, 200
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