| Summary: | 碩士 === 國立交通大學 === 電信工程系所 === 97 === Many solutions for detecting signals transmitted over flat-faded
multiple input multiple output (MIMO) channels have been proposed,
e.g., the zero-forcing (ZF), minimum mean squared error (MMSE),
lattice reduction and V-BLAST algorithms, to name a few. However,
these approaches suffer from either unsatisfactory performance or
high complexity.
We present an alternative method for detecting quadrature
amplitude modulated (QAM) MIMO signals. This method tries to
estimate the probability distribution of the candidate signal
location by sampling over a neighborhood of the received waveform.
The proposed random sampling based iterative distribution
estimator is similar to the class of Monte-Carlo based
optimization approach and if the distance used in measuring the
distance between a tentative distribution and the optimal
distribution is the Kullback-Leibler distance (cross entropy) then
our solution is identical to the one known as the Cross-Entropy
(CE) method. The CE method is motivated by the search for an
efficient rare-event simulation solution. The problem is
equivalent to finding the optimal importance sampling density. The
desired density is obtained by iterative random search in the
space of exponential distributions with the CE metric.
The proposed CE-based detector yields bit-error-rate (BER)
performance which is close to that achievable by the
Maximum-Likelihood (ML) detector when the signal-to-noise ratio
(SNR) is relatively low. Unfortunately the performance curves
exhibit error floors in high SNR region. To improve the
performance in high SNR region, we borrow the concept of particle
swarm optimization (PSO) in designing our detector. PSO is a
population-based iterative search algorithm which moves a number
of particles through the feasible solution space towards the
optimal solution with the information obtained in previous
iterations. The modified iterative detector incorporates extra
terms, which are generated by a PS-like process and represent a
driving force to pull the iterative optimization process from
being trapped in local minimums, in updating of the importance
density and is called the particle-swarm-driven cross-entropy
(PSD-CE) MIMO detector. The PSD-CE detector gives significant BER
performance improvement in medium-to-high SNR region. We also
consider the case when channel state information is imperfect and
suggest a robust detector structure based on a modified score
function.
|
|---|
