6 research outputs found
Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels
We study the information rates of non-coherent, stationary, Gaussian,
multiple-input multiple-output (MIMO) flat-fading channels that are achievable
with nearest neighbour decoding and pilot-aided channel estimation. In
particular, we analyse the behaviour of these achievable rates in the limit as
the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest
neighbour decoding and pilot-aided channel estimation achieves the capacity
pre-log - which is defined as the limiting ratio of the capacity to the
logarithm of SNR as the SNR tends to infinity - of non-coherent multiple-input
single-output (MISO) flat-fading channels, and it achieves the best so far
known lower bound on the capacity pre-log of non-coherent MIMO flat-fading
channels.Comment: 5 pages, 1 figure. To be presented at the IEEE International
Symposium on Information Theory (ISIT), St. Petersburg, Russia, 2011.
Replaced with version that will appear in the proceeding
A Rate-Splitting Approach to Fading Channels with Imperfect Channel-State Information
As shown by M\'edard, the capacity of fading channels with imperfect
channel-state information (CSI) can be lower-bounded by assuming a Gaussian
channel input with power and by upper-bounding the conditional entropy
by the entropy of a Gaussian random variable with variance
equal to the linear minimum mean-square error in estimating from
. We demonstrate that, using a rate-splitting approach, this lower
bound can be sharpened: by expressing the Gaussian input as the sum of two
independent Gaussian variables and and by applying M\'edard's lower
bound first to bound the mutual information between and while
treating as noise, and by applying it a second time to the mutual
information between and while assuming to be known, we obtain a
capacity lower bound that is strictly larger than M\'edard's lower bound. We
then generalize this approach to an arbitrary number of layers, where
is expressed as the sum of independent Gaussian random variables of
respective variances , summing up to . Among
all such rate-splitting bounds, we determine the supremum over power
allocations and total number of layers . This supremum is achieved
for and gives rise to an analytically expressible capacity lower
bound. For Gaussian fading, this novel bound is shown to converge to the
Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows,
provided that the variance of the channel estimation error tends to
zero as the SNR tends to infinity.Comment: 28 pages, 8 figures, submitted to IEEE Transactions on Information
Theory. Revised according to first round of review
Nearest neighbour decoding and pilot-aided channel estimation in stationary Gaussian flat-fading channels
We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-logwhich is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinityof non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels. © 2011 IEEE