29,653 research outputs found
Intersection Information based on Common Randomness
The introduction of the partial information decomposition generated a flurry
of proposals for defining an intersection information that quantifies how much
of "the same information" two or more random variables specify about a target
random variable. As of yet, none is wholly satisfactory. A palatable measure of
intersection information would provide a principled way to quantify slippery
concepts, such as synergy. Here, we introduce an intersection information
measure based on the G\'acs-K\"orner common random variable that is the first
to satisfy the coveted target monotonicity property. Our measure is imperfect,
too, and we suggest directions for improvement.Comment: 19 pages, 5 figure
Communication Complexity of Permutation-Invariant Functions
Motivated by the quest for a broader understanding of communication
complexity of simple functions, we introduce the class of
"permutation-invariant" functions. A partial function is permutation-invariant if for every bijection
and every , it is the case that . Most of the commonly studied functions
in communication complexity are permutation-invariant. For such functions, we
present a simple complexity measure (computable in time polynomial in given
an implicit description of ) that describes their communication complexity
up to polynomial factors and up to an additive error that is logarithmic in the
input size. This gives a coarse taxonomy of the communication complexity of
simple functions. Our work highlights the role of the well-known lower bounds
of functions such as 'Set-Disjointness' and 'Indexing', while complementing
them with the relatively lesser-known upper bounds for 'Gap-Inner-Product'
(from the sketching literature) and 'Sparse-Gap-Inner-Product' (from the recent
work of Canonne et al. [ITCS 2015]). We also present consequences to the study
of communication complexity with imperfectly shared randomness where we show
that for total permutation-invariant functions, imperfectly shared randomness
results in only a polynomial blow-up in communication complexity after an
additive overhead
Non-locality and Communication Complexity
Quantum information processing is the emerging field that defines and
realizes computing devices that make use of quantum mechanical principles, like
the superposition principle, entanglement, and interference. In this review we
study the information counterpart of computing. The abstract form of the
distributed computing setting is called communication complexity. It studies
the amount of information, in terms of bits or in our case qubits, that two
spatially separated computing devices need to exchange in order to perform some
computational task. Surprisingly, quantum mechanics can be used to obtain
dramatic advantages for such tasks.
We review the area of quantum communication complexity, and show how it
connects the foundational physics questions regarding non-locality with those
of communication complexity studied in theoretical computer science. The first
examples exhibiting the advantage of the use of qubits in distributed
information-processing tasks were based on non-locality tests. However, by now
the field has produced strong and interesting quantum protocols and algorithms
of its own that demonstrate that entanglement, although it cannot be used to
replace communication, can be used to reduce the communication exponentially.
In turn, these new advances yield a new outlook on the foundations of physics,
and could even yield new proposals for experiments that test the foundations of
physics.Comment: Survey paper, 63 pages LaTeX. A reformatted version will appear in
Reviews of Modern Physic
Feedback Enhances Simultaneous Wireless Information and Energy Transmission in Multiple Access Channels
In this report, the fundamental limits of simultaneous information and energy
transmission in the two-user Gaussian multiple access channel (G-MAC) with and
without feedback are fully characterized. More specifically, all the achievable
information and energy transmission rates (in bits per channel use and
energy-units per channel use, respectively) are identified. Furthermore, the
fundamental limits on the individual and sum- rates given a minimum energy rate
ensured at an energy harvester are also characterized. In the case without
feedback, an achievability scheme based on power-splitting and successive
interference cancellation is shown to be optimal. Alternatively, in the case
with feedback (G-MAC-F), a simple yet optimal achievability scheme based on
power-splitting and Ozarow's capacity achieving scheme is presented. Finally,
the energy transmission enhancement induced by the use of feedback is
quantified. Feedback can at most double the energy transmission rate at high
SNRs when the information transmission sum-rate is kept fixed at the
sum-capacity of the G-MAC, but it has no effect at very low SNRs.Comment: INRIA REPORT N{\deg}8804, accepted for publication in IEEE
transactions on Information Theory, March, 201
Passive network tomography for erroneous networks: A network coding approach
Passive network tomography uses end-to-end observations of network
communication to characterize the network, for instance to estimate the network
topology and to localize random or adversarial glitches. Under the setting of
linear network coding this work provides a comprehensive study of passive
network tomography in the presence of network (random or adversarial) glitches.
To be concrete, this work is developed along two directions: 1. Tomographic
upper and lower bounds (i.e., the most adverse conditions in each problem
setting under which network tomography is possible, and corresponding schemes
(computationally efficient, if possible) that achieve this performance) are
presented for random linear network coding (RLNC). We consider RLNC designed
with common randomness, i.e., the receiver knows the random code-books all
nodes. (To justify this, we show an upper bound for the problem of topology
estimation in networks using RLNC without common randomness.) In this setting
we present the first set of algorithms that characterize the network topology
exactly. Our algorithm for topology estimation with random network errors has
time complexity that is polynomial in network parameters. For the problem of
network error localization given the topology information, we present the first
computationally tractable algorithm to localize random errors, and prove it is
computationally intractable to localize adversarial errors. 2. New network
coding schemes are designed that improve the tomographic performance of RLNC
while maintaining the desirable low-complexity, throughput-optimal, distributed
linear network coding properties of RLNC. In particular, we design network
codes based on Reed-Solomon codes so that a maximal number of adversarial
errors can be localized in a computationally efficient manner even without the
information of network topology.Comment: 40 pages, under submission for IEEE Trans. on Information Theor
Optimal Error Rates for Interactive Coding II: Efficiency and List Decoding
We study coding schemes for error correction in interactive communications.
Such interactive coding schemes simulate any -round interactive protocol
using rounds over an adversarial channel that corrupts up to
transmissions. Important performance measures for a coding scheme are its
maximum tolerable error rate , communication complexity , and
computational complexity.
We give the first coding scheme for the standard setting which performs
optimally in all three measures: Our randomized non-adaptive coding scheme has
a near-linear computational complexity and tolerates any error rate with a linear communication complexity. This improves over
prior results which each performed well in two of these measures.
We also give results for other settings of interest, namely, the first
computationally and communication efficient schemes that tolerate adaptively, if only one party is required to
decode, and if list decoding is allowed. These are the
optimal tolerable error rates for the respective settings. These coding schemes
also have near linear computational and communication complexity.
These results are obtained via two techniques: We give a general black-box
reduction which reduces unique decoding, in various settings, to list decoding.
We also show how to boost the computational and communication efficiency of any
list decoder to become near linear.Comment: preliminary versio
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