122,661 research outputs found
Delay-Optimal Buffer-Aware Probabilistic Scheduling with Adaptive Transmission
Cross-layer scheduling is a promising way to improve Quality of Service (QoS)
given a power constraint. In this paper, we investigate the system with random
data arrival and adaptive transmission. Probabilistic scheduling strategies
aware of the buffer state are applied to generalize conventional deterministic
scheduling. Based on this, the average delay and power consumption are analysed
by Markov reward process. The optimal delay-power tradeoff curve is the Pareto
frontier of the feasible delay-power region. It is proved that the optimal
delay-power tradeoff is piecewise-linear, whose vertices are obtained by
deterministic strategies. Moreover, the corresponding strategies of the optimal
tradeoff curve are threshold-based, hence can be obtained by a proposed
effective algorithm. On the other hand, we formulate a linear programming to
minimize the average delay given a fixed power constraint. By varying the power
constraint, the optimal delay-power tradeoff curve can also be obtained. It is
demonstrated that the algorithm result and the optimization result match each
other, and are further validated by Monte-Carlo simulation.Comment: 6 pages, 4 figures, accepted by IEEE ICCC 201
Information-Disturbance Tradeoff in Quantum State Discrimination
When discriminating between two pure quantum states, there exists a
quantitative tradeoff between the information retrieved by the measurement and
the disturbance caused on the unknown state. We derive the optimal tradeoff and
provide the corresponding quantum measurement. Such an optimal measurement
smoothly interpolates between the two limiting cases of maximal information
extraction and no measurement at all.Comment: 5 pages, 2 (low-quality) figures. Eq. (20) corrected. Final published
versio
Explicit Space-Time Codes Achieving The Diversity-Multiplexing Gain Tradeoff
A recent result of Zheng and Tse states that over a quasi-static channel,
there exists a fundamental tradeoff, referred to as the diversity-multiplexing
gain (D-MG) tradeoff, between the spatial multiplexing gain and the diversity
gain that can be simultaneously achieved by a space-time (ST) block code. This
tradeoff is precisely known in the case of i.i.d. Rayleigh-fading, for T>=
n_t+n_r-1 where T is the number of time slots over which coding takes place and
n_t,n_r are the number of transmit and receive antennas respectively. For T <
n_t+n_r-1, only upper and lower bounds on the D-MG tradeoff are available.
In this paper, we present a complete solution to the problem of explicitly
constructing D-MG optimal ST codes, i.e., codes that achieve the D-MG tradeoff
for any number of receive antennas. We do this by showing that for the square
minimum-delay case when T=n_t=n, cyclic-division-algebra (CDA) based ST codes
having the non-vanishing determinant property are D-MG optimal. While
constructions of such codes were previously known for restricted values of n,
we provide here a construction for such codes that is valid for all n.
For the rectangular, T > n_t case, we present two general techniques for
building D-MG-optimal rectangular ST codes from their square counterparts. A
byproduct of our results establishes that the D-MG tradeoff for all T>= n_t is
the same as that previously known to hold for T >= n_t + n_r -1.Comment: Revised submission to IEEE Transactions on Information Theor
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data
release mechanisms to attain the information-theoretically optimal tradeoff
between minimizing distortion of useful data and concealing specific sensitive
information. Our approach employs adversarially-trained neural networks to
implement randomized mechanisms and to perform a variational approximation of
mutual information privacy. We validate our Privacy-Preserving Adversarial
Networks (PPAN) framework via proof-of-concept experiments on discrete and
continuous synthetic data, as well as the MNIST handwritten digits dataset. For
synthetic data, our model-agnostic PPAN approach achieves tradeoff points very
close to the optimal tradeoffs that are analytically-derived from model
knowledge. In experiments with the MNIST data, we visually demonstrate a
learned tradeoff between minimizing the pixel-level distortion versus
concealing the written digit.Comment: 16 page
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