70 research outputs found
On the Non-Coherent Wideband Multipath Fading Relay Channel
We investigate the multipath fading relay channel in the limit of a large
bandwidth, and in the non-coherent setting, where the channel state is unknown
to all terminals, including the relay and the destination. We propose a
hypergraph model of the wideband multipath fading relay channel, and show that
its min-cut is achieved by a non-coherent peaky frequency binning scheme. The
so-obtained lower bound on the capacity of the wideband multipath fading relay
channel turns out to coincide with the block-Markov lower bound on the capacity
of the wideband frequency-division Gaussian (FD-AWGN) relay channel. In certain
cases, this achievable rate also meets the cut-set upper-bound, and thus
reaches the capacity of the non-coherent wideband multipath fading relay
channel.Comment: 8 pages, 4 figures, longer version (including proof) of the paper in
Proc. of IEEE ISIT 201
Optimal relay location and power allocation for low SNR broadcast relay channels
We consider the broadcast relay channel (BRC), where a single source
transmits to multiple destinations with the help of a relay, in the limit of a
large bandwidth. We address the problem of optimal relay positioning and power
allocations at source and relay, to maximize the multicast rate from source to
all destinations. To solve such a network planning problem, we develop a
three-faceted approach based on an underlying information theoretic model,
computational geometric aspects, and network optimization tools. Firstly,
assuming superposition coding and frequency division between the source and the
relay, the information theoretic framework yields a hypergraph model of the
wideband BRC, which captures the dependency of achievable rate-tuples on the
network topology. As the relay position varies, so does the set of hyperarcs
constituting the hypergraph, rendering the combinatorial nature of optimization
problem. We show that the convex hull C of all nodes in the 2-D plane can be
divided into disjoint regions corresponding to distinct hyperarcs sets. These
sets are obtained by superimposing all k-th order Voronoi tessellation of C. We
propose an easy and efficient algorithm to compute all hyperarc sets, and prove
they are polynomially bounded. Using the switched hypergraph approach, we model
the original problem as a continuous yet non-convex network optimization
program. Ultimately, availing on the techniques of geometric programming and
-norm surrogate approximation, we derive a good convex approximation. We
provide a detailed characterization of the problem for collinearly located
destinations, and then give a generalization for arbitrarily located
destinations. Finally, we show strong gains for the optimal relay positioning
compared to seemingly interesting positions.Comment: In Proceedings of INFOCOM 201
Privacy Against Statistical Inference
We propose a general statistical inference framework to capture the privacy
threat incurred by a user that releases data to a passive but curious
adversary, given utility constraints. We show that applying this general
framework to the setting where the adversary uses the self-information cost
function naturally leads to a non-asymptotic information-theoretic approach for
characterizing the best achievable privacy subject to utility constraints.
Based on these results we introduce two privacy metrics, namely average
information leakage and maximum information leakage. We prove that under both
metrics the resulting design problem of finding the optimal mapping from the
user's data to a privacy-preserving output can be cast as a modified
rate-distortion problem which, in turn, can be formulated as a convex program.
Finally, we compare our framework with differential privacy.Comment: Allerton 2012, 8 page
When Network Coding and Dirty Paper Coding meet in a Cooperative Ad Hoc Network
We develop and analyze new cooperative strategies for ad hoc networks that
are more spectrally efficient than classical DF cooperative protocols. Using
analog network coding, our strategies preserve the practical half-duplex
assumption but relax the orthogonality constraint. The introduction of
interference due to non-orthogonality is mitigated thanks to precoding, in
particular Dirty Paper coding. Combined with smart power allocation, our
cooperation strategies allow to save time and lead to more efficient use of
bandwidth and to improved network throughput with respect to classical RDF/PDF.Comment: 7 pages, 7 figure
From the Information Bottleneck to the Privacy Funnel
We focus on the privacy-utility trade-off encountered by users who wish to
disclose some information to an analyst, that is correlated with their private
data, in the hope of receiving some utility. We rely on a general privacy
statistical inference framework, under which data is transformed before it is
disclosed, according to a probabilistic privacy mapping. We show that when the
log-loss is introduced in this framework in both the privacy metric and the
distortion metric, the privacy leakage and the utility constraint can be
reduced to the mutual information between private data and disclosed data, and
between non-private data and disclosed data respectively. We justify the
relevance and generality of the privacy metric under the log-loss by proving
that the inference threat under any bounded cost function can be upper-bounded
by an explicit function of the mutual information between private data and
disclosed data. We then show that the privacy-utility tradeoff under the
log-loss can be cast as the non-convex Privacy Funnel optimization, and we
leverage its connection to the Information Bottleneck, to provide a greedy
algorithm that is locally optimal. We evaluate its performance on the US census
dataset
Asymptotic Capacity and Optimal Precoding Strategy of Multi-Level Precode & Forward in Correlated Channels
We analyze a multi-level MIMO relaying system where a multiple-antenna
transmitter sends data to a multipleantenna receiver through several relay
levels, also equipped with multiple antennas. Assuming correlated fading in
each hop, each relay receives a faded version of the signal transmitted by the
previous level, performs precoding on the received signal and retransmits it to
the next level. Using free probability theory and assuming that the noise power
at the relay levels - but not at the receiver - is negligible, a closed-form
expression of the end-to-end asymptotic instantaneous mutual information is
derived as the number of antennas in all levels grow large with the same rate.
This asymptotic expression is shown to be independent from the channel
realizations, to only depend on the channel statistics and to also serve as the
asymptotic value of the end-to-end average mutual information. We also provide
the optimal singular vectors of the precoding matrices that maximize the
asymptotic mutual information : the optimal transmit directions represented by
the singular vectors of the precoding matrices are aligned on the eigenvectors
of the channel correlation matrices, therefore they can be determined only
using the known statistics of the channel matrices and do not depend on a
particular channel realization.Comment: 5 pages, 3 figures, to be published in proceedings of IEEE
Information Theory Workshop 200
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