396 research outputs found
Optimality of Treating Interference as Noise: A Combinatorial Perspective
For single-antenna Gaussian interference channels, we re-formulate the
problem of determining the Generalized Degrees of Freedom (GDoF) region
achievable by treating interference as Gaussian noise (TIN) derived in [3] from
a combinatorial perspective. We show that the TIN power control problem can be
cast into an assignment problem, such that the globally optimal power
allocation variables can be obtained by well-known polynomial time algorithms.
Furthermore, the expression of the TIN-Achievable GDoF region (TINA region) can
be substantially simplified with the aid of maximum weighted matchings. We also
provide conditions under which the TINA region is a convex polytope that relax
those in [3]. For these new conditions, together with a channel connectivity
(i.e., interference topology) condition, we show TIN optimality for a new class
of interference networks that is not included, nor includes, the class found in
[3].
Building on the above insights, we consider the problem of joint link
scheduling and power control in wireless networks, which has been widely
studied as a basic physical layer mechanism for device-to-device (D2D)
communications. Inspired by the relaxed TIN channel strength condition as well
as the assignment-based power allocation, we propose a low-complexity
GDoF-based distributed link scheduling and power control mechanism (ITLinQ+)
that improves upon the ITLinQ scheme proposed in [4] and further improves over
the heuristic approach known as FlashLinQ. It is demonstrated by simulation
that ITLinQ+ provides significant average network throughput gains over both
ITLinQ and FlashLinQ, and yet still maintains the same level of implementation
complexity. More notably, the energy efficiency of the newly proposed ITLinQ+
is substantially larger than that of ITLinQ and FlashLinQ, which is desirable
for D2D networks formed by battery-powered devices.Comment: A short version has been presented at IEEE International Symposium on
Information Theory (ISIT 2015), Hong Kon
Privacy-Constrained Remote Source Coding
We consider the problem of revealing/sharing data in an efficient and secure
way via a compact representation. The representation should ensure reliable
reconstruction of the desired features/attributes while still preserve privacy
of the secret parts of the data. The problem is formulated as a remote lossy
source coding with a privacy constraint where the remote source consists of
public and secret parts. Inner and outer bounds for the optimal tradeoff region
of compression rate, distortion, and privacy leakage rate are given and shown
to coincide for some special cases. When specializing the distortion measure to
a logarithmic loss function, the resulting rate-distortion-leakage tradeoff for
the case of identical side information forms an optimization problem which
corresponds to the "secure" version of the so-called information bottleneck.Comment: 10 pages, 1 figure, to be presented at ISIT 201
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