60,521 research outputs found
Network Information Flow with Correlated Sources
In this paper, we consider a network communications problem in which multiple
correlated sources must be delivered to a single data collector node, over a
network of noisy independent point-to-point channels. We prove that perfect
reconstruction of all the sources at the sink is possible if and only if, for
all partitions of the network nodes into two subsets S and S^c such that the
sink is always in S^c, we have that H(U_S|U_{S^c}) < \sum_{i\in S,j\in S^c}
C_{ij}. Our main finding is that in this setup a general source/channel
separation theorem holds, and that Shannon information behaves as a classical
network flow, identical in nature to the flow of water in pipes. At first
glance, it might seem surprising that separation holds in a fairly general
network situation like the one we study. A closer look, however, reveals that
the reason for this is that our model allows only for independent
point-to-point channels between pairs of nodes, and not multiple-access and/or
broadcast channels, for which separation is well known not to hold. This
``information as flow'' view provides an algorithmic interpretation for our
results, among which perhaps the most important one is the optimality of
implementing codes using a layered protocol stack.Comment: Final version, to appear in the IEEE Transactions on Information
Theory -- contains (very) minor changes based on the last round of review
On practical design for joint distributed source and network coding
This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation
Time Delay Estimation from Low Rate Samples: A Union of Subspaces Approach
Time delay estimation arises in many applications in which a multipath medium
has to be identified from pulses transmitted through the channel. Various
approaches have been proposed in the literature to identify time delays
introduced by multipath environments. However, these methods either operate on
the analog received signal, or require high sampling rates in order to achieve
reasonable time resolution. In this paper, our goal is to develop a unified
approach to time delay estimation from low rate samples of the output of a
multipath channel. Our methods result in perfect recovery of the multipath
delays from samples of the channel output at the lowest possible rate, even in
the presence of overlapping transmitted pulses. This rate depends only on the
number of multipath components and the transmission rate, but not on the
bandwidth of the probing signal. In addition, our development allows for a
variety of different sampling methods. By properly manipulating the low-rate
samples, we show that the time delays can be recovered using the well-known
ESPRIT algorithm. Combining results from sampling theory with those obtained in
the context of direction of arrival estimation methods, we develop necessary
and sufficient conditions on the transmitted pulse and the sampling functions
in order to ensure perfect recovery of the channel parameters at the minimal
possible rate. Our results can be viewed in a broader context, as a sampling
theorem for analog signals defined over an infinite union of subspaces
Free Probability based Capacity Calculation of Multiantenna Gaussian Fading Channels with Cochannel Interference
During the last decade, it has been well understood that communication over
multiple antennas can increase linearly the multiplexing capacity gain and
provide large spectral efficiency improvements. However, the majority of
studies in this area were carried out ignoring cochannel interference. Only a
small number of investigations have considered cochannel interference, but even
therein simple channel models were employed, assuming identically distributed
fading coefficients. In this paper, a generic model for a multi-antenna channel
is presented incorporating four impairments, namely additive white Gaussian
noise, flat fading, path loss and cochannel interference. Both point-to-point
and multiple-access MIMO channels are considered, including the case of
cooperating Base Station clusters. The asymptotic capacity limit of this
channel is calculated based on an asymptotic free probability approach which
exploits the additive and multiplicative free convolution in the R- and
S-transform domain respectively, as well as properties of the eta and Stieltjes
transform. Numerical results are utilized to verify the accuracy of the derived
closed-form expressions and evaluate the effect of the cochannel interference.Comment: 16 pages, 4 figures, 1 tabl
Performance Analysis of Dual-User Macrodiversity MIMO Systems with Linear Receivers in Flat Rayleigh Fading
The performance of linear receivers in the presence of co-channel
interference in Rayleigh channels is a fundamental problem in wireless
communications. Performance evaluation for these systems is well-known for
receive arrays where the antennas are close enough to experience equal average
SNRs from a source. In contrast, almost no analytical results are available for
macrodiversity systems where both the sources and receive antennas are widely
separated. Here, receive antennas experience unequal average SNRs from a source
and a single receive antenna receives a different average SNR from each source.
Although this is an extremely difficult problem, progress is possible for the
two-user scenario. In this paper, we derive closed form results for the
probability density function (pdf) and cumulative distribution function (cdf)
of the output signal to interference plus noise ratio (SINR) and signal to
noise ratio (SNR) of minimum mean squared error (MMSE) and zero forcing (ZF)
receivers in independent Rayleigh channels with arbitrary numbers of receive
antennas. The results are verified by Monte Carlo simulations and high SNR
approximations are also derived. The results enable further system analysis
such as the evaluation of outage probability, bit error rate (BER) and
capacity.Comment: 24 pages, 7 figures; IEEE Transaction of Wireless Communication 2012
Corrected typo
Out-of-Band Radiation Measure for MIMO Arrays with Beamformed Transmission
The spatial characteristics of the out-of-band radiation that a multiuser
MIMO system emits in the environment, due to its power amplifiers (modeled by a
polynomial model) are nonlinear, is studied by deriving an analytical
expression for the continuous-time cross-correlation of the transmit signals.
At a random spatial point, the same power is received at any frequency on
average with a MIMO base station as with a SISO base station when the two
radiate the same amount of power. For a specific channel realization however,
the received power depends on the channel. We show that the power received
out-of-band only deviates little from the average in a MIMO system with
multiple users and that the deviation can be significant with only one user.
Using an ergodicity argument, we conclude that out-of-band radiation is less of
a problem in massive MIMO, where total radiated power is lower compared to SISO
systems and that requirements on spectral regrowth can be relaxed in MIMO
systems without causing more total out-of-band radiation
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