17,324 research outputs found
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
Information-theoretic analysis of MIMO channel sounding
The large majority of commercially available multiple-input multiple-output
(MIMO) radio channel measurement devices (sounders) is based on time-division
multiplexed switching (TDMS) of a single transmit/receive radio-frequency chain
into the elements of a transmit/receive antenna array. While being
cost-effective, such a solution can cause significant measurement errors due to
phase noise and frequency offset in the local oscillators. In this paper, we
systematically analyze the resulting errors and show that, in practice,
overestimation of channel capacity by several hundred percent can occur.
Overestimation is caused by phase noise (and to a lesser extent frequency
offset) leading to an increase of the MIMO channel rank. Our analysis
furthermore reveals that the impact of phase errors is, in general, most
pronounced if the physical channel has low rank (typical for line-of-sight or
poor scattering scenarios). The extreme case of a rank-1 physical channel is
analyzed in detail. Finally, we present measurement results obtained from a
commercially employed TDMS-based MIMO channel sounder. In the light of the
findings of this paper, the results obtained through MIMO channel measurement
campaigns using TDMS-based channel sounders should be interpreted with great
care.Comment: 99 pages, 14 figures, submitted to IEEE Transactions on Information
Theor
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