14,391 research outputs found
Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
Sampling from the lattice Gaussian distribution plays an important role in
various research fields. In this paper, the Markov chain Monte Carlo
(MCMC)-based sampling technique is advanced in several fronts. Firstly, the
spectral gap for the independent Metropolis-Hastings-Klein (MHK) algorithm is
derived, which is then extended to Peikert's algorithm and rejection sampling;
we show that independent MHK exhibits faster convergence. Then, the performance
of bounded distance decoding using MCMC is analyzed, revealing a flexible
trade-off between the decoding radius and complexity. MCMC is further applied
to trapdoor sampling, again offering a trade-off between security and
complexity. Finally, the independent multiple-try Metropolis-Klein (MTMK)
algorithm is proposed to enhance the convergence rate. The proposed algorithms
allow parallel implementation, which is beneficial for practical applications.Comment: submitted to Transaction on Information Theor
On the Geometric Ergodicity of Metropolis-Hastings Algorithms for Lattice Gaussian Sampling
Sampling from the lattice Gaussian distribution is emerging as an important
problem in coding and cryptography. In this paper, the classic
Metropolis-Hastings (MH) algorithm from Markov chain Monte Carlo (MCMC) methods
is adapted for lattice Gaussian sampling. Two MH-based algorithms are proposed,
which overcome the restriction suffered by the default Klein's algorithm. The
first one, referred to as the independent Metropolis-Hastings-Klein (MHK)
algorithm, tries to establish a Markov chain through an independent proposal
distribution. We show that the Markov chain arising from the independent MHK
algorithm is uniformly ergodic, namely, it converges to the stationary
distribution exponentially fast regardless of the initial state. Moreover, the
rate of convergence is explicitly calculated in terms of the theta series,
leading to a predictable mixing time. In order to further exploit the
convergence potential, a symmetric Metropolis-Klein (SMK) algorithm is
proposed. It is proven that the Markov chain induced by the SMK algorithm is
geometrically ergodic, where a reasonable selection of the initial state is
capable to enhance the convergence performance.Comment: Submitted to IEEE Transactions on Information Theor
Analysis and Optimization of Cellular Network with Burst Traffic
In this paper, we analyze the performance of cellular networks and study the
optimal base station (BS) density to reduce the network power consumption. In
contrast to previous works with similar purpose, we consider Poisson traffic
for users' traffic model. In such situation, each BS can be viewed as M/G/1
queuing model. Based on theory of stochastic geometry, we analyze users'
signal-to-interference-plus-noise-ratio (SINR) and obtain the average
transmission time of each packet. While most of the previous works on SINR
analysis in academia considered full buffer traffic, our analysis provides a
basic framework to estimate the performance of cellular networks with burst
traffic. We find that the users' SINR depends on the average transmission
probability of BSs, which is defined by a nonlinear equation. As it is
difficult to obtain the closed-form solution, we solve this nonlinear equation
by bisection method. Besides, we formulate the optimization problem to minimize
the area power consumption. An iteration algorithm is proposed to derive the
local optimal BS density, and the numerical result shows that the proposed
algorithm can converge to the global optimal BS density. At the end, the impact
of BS density on users' SINR and average packet delay will be discussed.Comment: This paper has been withdrawn by the author due to missuse of queue
model in Section Fou
Polar Coding for the Cognitive Interference Channel with Confidential Messages
In this paper, we propose a low-complexity, secrecy capacity achieving polar
coding scheme for the cognitive interference channel with confidential messages
(CICC) under the strong secrecy criterion. Existing polar coding schemes for
interference channels rely on the use of polar codes for the multiple access
channel, the code construction problem of which can be complicated. We show
that the whole secrecy capacity region of the CICC can be achieved by simple
point-to-point polar codes due to the cognitivity, and our proposed scheme
requires the minimum rate of randomness at the encoder
Markov Chain Monte Carlo Algorithms for Lattice Gaussian Sampling
Sampling from a lattice Gaussian distribution is emerging as an important
problem in various areas such as coding and cryptography. The default sampling
algorithm --- Klein's algorithm yields a distribution close to the lattice
Gaussian only if the standard deviation is sufficiently large. In this paper,
we propose the Markov chain Monte Carlo (MCMC) method for lattice Gaussian
sampling when this condition is not satisfied. In particular, we present a
sampling algorithm based on Gibbs sampling, which converges to the target
lattice Gaussian distribution for any value of the standard deviation. To
improve the convergence rate, a more efficient algorithm referred to as
Gibbs-Klein sampling is proposed, which samples block by block using Klein's
algorithm. We show that Gibbs-Klein sampling yields a distribution close to the
target lattice Gaussian, under a less stringent condition than that of the
original Klein algorithm.Comment: 5 pages, 1 figure, IEEE International Symposium on Information
Theory(ISIT) 201
Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks
This paper aims at answering two fundamental questions: how area spectral
efficiency (ASE) behaves with different system parameters; how to design an
energy-efficient network. Based on stochastic geometry, we obtain the
expression and a tight lower-bound for ASE of Poisson distributed networks
considering multi-user MIMO (MU-MIMO) transmission. With the help of the
lower-bound, some interesting results are observed. These results are validated
via numerical results for the original expression. We find that ASE can be
viewed as a concave function with respect to the number of antennas and active
users. For the purpose of maximizing ASE, we demonstrate that the optimal
number of active users is a fixed portion of the number of antennas. With
optimal number of active users, we observe that ASE increases linearly with the
number of antennas. Another work of this paper is joint optimization of the
base station (BS) density, the number of antennas and active users to minimize
the network energy consumption. It is discovered that the optimal combination
of the number of antennas and active users is the solution that maximizes the
energy-efficiency. Besides the optimal algorithm, we propose a suboptimal
algorithm to reduce the computational complexity, which can achieve near
optimal performance.Comment: Submitted to IEEE Transactions on Wireless Communications, Major
Revisio
Poly[[diaquadi-μ-dicyanamido-nickel(II)] bis(pyridinium-4-olate)]
The title compound, {[Ni(C2N3)2(H2O)2]·2C5H5NO}n, is a centrosymmetric two-dimensional coordination polymer with a layer (4,4) network structure. The asymmetric unit is compossed of an NiII atom, which sits on an inversion center, a μ-1,5-bridging dicyanamide anion, a water molecule, and a free 4-hydroxypyridine molecule present in the zwitterionic pyridinium-4-olate form. The NiII atom is coordinated in a slightly distorted N4O2 octahedral geometry by four bridging dicyanamide ligands and two trans water molecules. In the crystal, the two-dimensional networks are linked via N—H⋯O and O—H⋯O hydrogen bonds, forming a three-dimensional network
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