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RESEARCH
Main achievements of the project
I - HSUPA 4 System :
• Implementation of the transmission channel
of the HSUPA system, in conformity with the
3GPP norm.
• Performance evaluation for the HSUPA
system on the Gaussian channel, then on a
multipath channel. Several configurations
were studied, with the goal of meeting the
needs of the operator, Orange. Rake and
LMMSE receivers were used according to
the system configuration.
• Implementation of a rapid simulation
methodology for HSUPA with power control,
as well as HARQ retransmission techniques.
• Integration of the 16-QAM modulation for
HSUPA.
II - Turbo Equalization :
The Turbo equalization part deals essentially with
the writing of articles (IEEE Com. Letters and
IEEE Symposium on Turbo Codes) on the following
two points:
• Comparison of the performance on MIMO
channels between single-carrier and multicarrier
transmissions using an MMSE turbo
equalizer as a receiver in the frequency
domain.
• Complexity reduction of MMSE equalizers by
replacing a matrix inversion by its
development in truncated series. Illustration
of the contribution of this technique in turbo
equalization.
III- Studies of joint error-correcting
coding and modulation techniques for
PLC communication systems
• Validation by simulation of Turbo-decoding
adapted to Bernoulli-Gaussian type
impulsive noise (modification of the Log-
Likelihood Ratio (LLR)) channel information
in BPSK 7 constellation. This method was
proposed by Umehara for a class A
Middleton-type impulsive noise.
• Extension of the preceding method to multicarrier
situations, with OFDM in particular.
• Determination of the LLR modification for a
4) HSUPA : High Speed Uplink Packet Access
5) 3GPP : 3rd Generation Partnership Project
system implementing a receiver amplitude
limiter to fight impulsive noise. A study in
BPSK and multicarrier (OFDM) was carried
out. Important gains can be made for OFDM,
whereas the exact calculation of LLR without
using a limiter is still the best solution.
• Proposition for an optimisation of the
threshold value based on the parameters of
impulsive noise (BG). Method based on
signal detection theory. This approach offers
the advantage of being able to be
automatized because it adapts to SNR and to
the characteristics of BG. Validation of the
approach by simulation, in BPSK alone and
in OFDM, and on the HPAV chain for various
BG scenarios.
• Behavior study of OFDM/OQAM in the
presence of impulsive noise. Analytical proof
and proof by simulation, from the identical
behavior of OFDM and OQAM with a
rectangular prototype. We also
demonstrated the dependence of
performance on the prototype used for a
small number of carriers, and the
asymptotic convergence toward OFDM when
the number of carriers is increased.
IV - SPARSITY :
The sparse representation of signals consists of
the representation of a signal with a small
number of significant coefficients. By definition, a
signal is called sparse when most of these
coefficients are (approximately) null. The sparse
representation of signals has seen an important
upshoot over the past ten years. This has made it
possible to successfully attack a number of signal
processing and image problems. In compression,
for example, the media coding standard JPEG and
its successor, JPEG-2000, became the obvious
choices when it came to image compression
techniques by default. These two norms are based
on the principle of transformational coding.
The data vector representing the pixel line is
transformed in a domain where it is sparse
(meaning that it is represented in a new
coordinate system with a small number of
significant coefficients), and the resulting
coordinates are then processed in order to
produce the encoded binary output. JPEG
6) LMMSE : Linear Minimum Mean Squared Error
7) BPSK : Binary phase-shift keying
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