arXiv subjects

Xiaodong Wang

Publications and source records attributed to Xiaodong Wang.

At least 217 records · Page 12Linked to original sources

arXiv2017

Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar

In this paper, we consider the problem of joint delay-Doppler estimation of moving targets in a passive radar that makes use of orthogonal frequency-division multiplexing (OFDM) communication signals. A compressed sensing algorithm is proposed to achieve supper-resolution and better accuracy, using both the atomic norm and the $\ell_1$-norm. The atomic norm is used to manifest the signal sparsity in the continuous domain. Unlike previous works which assume the demodulation to be error free, we explicitly introduce the demodulation error signal whose sparsity is imposed by the $\ell_1$-norm. On this basis, the delays and Doppler frequencies are estimated by solving a semidefinite program (SDP) which is convex. We also develop an iterative method for solving this SDP via the alternating direction method of multipliers (ADMM) where each iteration involves closed-form computation. Simulation results are presented to illustrate the high performance of the proposed algorithm.

cs.IT↗

arXiv2016

On the DoF of Two-way $2\times2\times2$ Relay Networks with or without Relay Caching

Two-way relay is potentially an effective approach to spectrum sharing and aggregation by allowing simultaneous bidirectional transmissions between source-destinations pairs. This paper studies the two-way $2\times2\times2$ relay network, a class of four-unicast networks, where there are four source/destination nodes and two relay nodes, with each source sending a message to its destination. We show that without relay caching the total degrees of freedom (DoF) is bounded from above by $8/3$, indicating that bidirectional links do not double the DoF (It is known that the total DoF of one-way $2\times2\times2$ relay network is $2$.). Further, we show that the DoF of $8/3$ is achievable for the two-way $2\times2\times2$ relay network with relay caching. Finally, even though the DoF of this network is no more than $8/3$ for generic channel gains, DoF of $4$ can be achieved for a symmetric configuration of channel gains.

cs.IT↗

arXiv2016

Modeling and analysis of the electromechanical behavior of surface-bonded piezoelectric actuators using finite element method

Piezoelectric actuators have been widely used to form a self-monitoring smart system to do Structural health monitoring (SHM). One of the most fundamental issues in using actuators is to determine the actuation effects being transferred from the actuators to the host structure. This report summaries the state of the art of modeling techniques for piezoelectric actuators and provides a numerical analysis of the static and dynamic electromechanical behavior of piezoelectric actuators surface-bonded to an elastic medium under in-plane mechanical and electric loads using finite element method. Also case study is conducted to study the effect of material properties, bonding layer and loading frequency using static and harmonic analysis of ANSYS. Finally, stresses and displacements are determined, and singularity behavior at the tips of the actuator is proved. The results indicate that material properties, bonding layers and frequency have a significant influence on the stresses transferred to the host structure.

physics.comp-ph↗

arXiv2016

Low-tubal-rank Tensor Completion using Alternating Minimization

The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to recover a third-order tensor by observing a subset of its elements selected uniformly at random. We propose a fast iterative algorithm, called {\em Tubal-Alt-Min}, that is inspired by a similar approach for low-rank matrix completion. The unknown low-tubal-rank tensor is represented as the product of two much smaller tensors with the low-tubal-rank property being automatically incorporated, and Tubal-Alt-Min alternates between estimating those two tensors using tensor least squares minimization. First, we note that tensor least squares minimization is different from its matrix counterpart and nontrivial as the circular convolution operator of the low-tubal-rank tensor model is intertwined with the sub-sampling operator. Second, the theoretical performance guarantee is challenging since Tubal-Alt-Min is iterative and nonconvex in nature. We prove that 1) Tubal-Alt-Min guarantees exponential convergence to the global optima, and 2) for an $n \times n \times k$ tensor with tubal-rank $r \ll n$, the required sampling complexity is $O(nr^2k \log^3 n)$ and the computational complexity is $O(n^2rk^2 \log^2 n)$. Third, on both synthetic data and real-world video data, evaluation results show that compared with tensor-nuclear norm minimization (TNN-ADMM), Tubal-Alt-Min improves the recovery error dramatically (by orders of magnitude). It is estimated that Tubal-Alt-Min converges at an exponential rate $10^{-0.4423 \text{Iter}}$ where $\text{Iter}$ denotes the number of iterations, which is much faster than TNN-ADMM's $10^{-0.0332 \text{Iter}}$, and the running time can be accelerated by more than $5$ times for a $200 \times 200 \times 20$ tensor.

cs.LG↗

arXiv2016

A simple linear space algorithm for computing a longest common increasing subsequence

This paper reformulates the problem of finding a longest common increasing subsequence of the two given input sequences in a very succinct way. An extremely simple linear space algorithm based on the new formula can find a longest common increasing subsequence of sizes $n$ and $m$ respectively, in time $O(nm)$ using additional $\min\{n,m\}+1$ space.

cs.DS↗

arXiv2016

On the Symmetric $K$-user Interference Channels with Limited Feedback

In this paper, we develop achievability schemes for symmetric $K$-user interference channels with a rate-limited feedback from each receiver to the corresponding transmitter. We study this problem under two different channel models: the linear deterministic model, and the Gaussian model. For the deterministic model, the proposed scheme achieves a symmetric rate that is the minimum of the symmetric capacity with infinite feedback, and the sum of the symmetric capacity without feedback and the symmetric amount of feedback. For the Gaussian interference channel, we use lattice codes to propose a transmission strategy that incorporates the techniques of Han-Kobayashi message splitting, interference decoding, and decode and forward. This strategy achieves a symmetric rate which is within a constant number of bits to the minimum of the symmetric capacity with infinite feedback, and the sum of the symmetric capacity without feedback and the amount of symmetric feedback. This constant is obtained as a function of the number of users, $K$. The symmetric achievable rate is used to characterize the achievable generalized degrees of freedom which exhibits a gradual increase from no feedback to perfect feedback in the presence of feedback links with limited capacity.

cs.IT↗

arXiv2016

The DoF of Two-way Butterfly Networks

This paper studies the two-way butterfly network, a class of two-way four-unicast networks. We first show that bidirectional links do not increase the degrees of freedom for this network thus giving the first example for networks, to the best of our knowledge, where bidirectional links do not increase the degrees of freedom. Further, we see that sufficient caching at the relays or increasing the number of antennas in the relays can double the two-way degrees of freedom for butterfly network.

cs.IT↗

arXiv2016

Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array

By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacityapproaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.

cs.IT↗

arXiv2016

Order-2 Asymptotic Optimality of the Fully Distributed Sequential Hypothesis Test

This work analyzes the asymptotic performances of fully distributed sequential hypothesis testing procedures as the type-I and type-II error rates approach zero, in the context of a sensor network without a fusion center. In particular, the sensor network is defined by an undirected graph, where each sensor can observe samples over time, access the information from the adjacent sensors, and perform the sequential test based on its own decision statistic. Different from most literature, the sampling process and the information exchange process in our framework take place simultaneously (or, at least in comparable time-scales), thus cannot be decoupled from one another. Two message-passing schemes are considered, based on which the distributed sequential probability ratio test (DSPRT) is carried out respectively. The first scheme features the dissemination of the raw samples. Although the sample propagation based DSPRT is shown to yield the asymptotically optimal performance at each sensor, it incurs excessive inter-sensor communication overhead due to the exchange of raw samples with index information. The second scheme adopts the consensus algorithm, where the local decision statistic is exchanged between sensors instead of the raw samples, thus significantly lowering the communication requirement compared to the first scheme. In particular, the decision statistic for DSPRT at each sensor is updated by the weighted average of the decision statistics in the neighbourhood at every message-passing step. We show that, under certain regularity conditions, the consensus algorithm based DSPRT also yields the order-2 asymptotically optimal performance at all sensors.

stat.AP↗

arXiv2016

Does $\ell_p$-minimization outperform $\ell_1$-minimization?

In many application areas we are faced with the following question: Can we recover a sparse vector $x_o \in \mathbb{R}^N$ from its undersampled set of noisy observations $y \in \mathbb{R}^n$, $y=A x_o+w$. The last decade has witnessed a surge of algorithms and theoretical results addressing this question. One of the most popular algorithms is the $\ell_p$-regularized least squares (LPLS) given by the following formulation: \[ \hat{x}(γ,p )\in \arg\min_x \frac{1}{2}\|y - Ax\|_2^2+γ\|x\|_p^p, \] where $p \in [0,1]$. Despite the non-convexity of these problems for $p<1$, they are still appealing because of the following folklores in compressed sensing: (i) $\hat{x}(γ,p )$ is closer to $x_o$ than $\hat{x}(γ,1)$. (ii) If we employ iterative methods that aim to converge to a local minima of LPLS, then under good initialization these algorithms converge to a solution that is closer to $x_o$ than $\hat{x}(γ,1)$. In spite of the existence of plenty of empirical results that support these folklore theorems, the theoretical progress to establish them has been very limited. This paper aims to study the above folklore theorems and establish their scope of validity. Starting with approximate message passing algorithm as a heuristic method for solving LPLS, we study the impact of initialization on the performance of AMP. Then, we employ the replica analysis to show the connection between the solution of AMP and $\hat{x}(γ, p)$ in the asymptotic settings. This enables us to compare the accuracy of $\hat{x}(γ,p)$ for $p \in [0,1]$. In particular, we will characterize the phase transition and noise sensitivity of LPLS for every $0\leq p\leq 1$ accurately. Our results in the noiseless setting confirm that LPLS exhibits the same phase transition for every $0\leq p <1$ and this phase transition is much higher than that of LASSO.

cs.IT↗

arXiv2016

Ergodic measures with multi-zero Lyapunov exponents inside homoclinic classes

We prove that for $C^1$ generic diffeomorphisms, if a homoclinic class $H(P)$ contains two hyperbolic periodic orbits of indices $i$ and $i+k$ respectively and $H(P)$ has no domination of index $j$ for any $j\in\{i+1,\cdots,i+k-1\}$, then there exists a non-hyperbolic ergodic measure whose $(i+l)^{th}$ Lyapunov exponent vanishes for any $l\in\{1,\cdots, k\}$, and whose support is the whole homoclinic class. We also prove that for $C^1$ generic diffeomorphisms, if a homoclinic class $H(P)$ has a dominated splitting of the form $E\oplus F\oplus G$, such that the center bundle $F$ has no finer dominated splitting, and $H(p)$ contains a hyperbolic periodic orbit $Q_1$ of index $\dim(E)$ and a hyperbolic periodic orbit $Q_2$ whose absolute Jacobian along the bundle $F$ is strictly less than $1$, then there exists a non-hyperbolic ergodic measure whose Lyapunov exponents along the center bundle $F$ all vanish and whose support is the whole homoclinic class.

math.DS↗

arXiv2016

Position-aided Large-scale MIMO Channel Estimation for High-Speed Railway Communication Systems

We consider channel estimation for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna arrays. It is known that the throughput of conventional training schemes monotonically decreases with the mobility. Assuming that the moving terminal employs a large linear antenna array, this paper proposes a position-aided channel estimation scheme whereby only a portion of the transmit antennas send pilot symbols and the full channel matrix can be well estimated by using these pilots together with the antenna position information based on the joint spatial-temporal correlation. The relationship between mobility and throughput/DoF is established. Furthermore, the optimal selections of transmit power and time interval partition between the training and data phases as well as the antenna size are presented accordingly. Both analytical and simulation results show that the system throughput with the position-aided channel estimator does not deteriorate appreciably as the mobility increases, which is sharply in contrast with the conventional one.

cs.IT↗

arXiv2016

Brendle's inequality on static manifolds

We generalize Brendle's geometric inequality considered in \cite{B} to static manifolds. The inequality bounds the integral of inverse mean curvature of an embedded mean-convex hypersurface by geometric data of the horizon. As a consequence, we obtain a reverse Penrose inequality on static asymptotically locally hyperbolic manifolds in the spirit of Chruściel and Simon \cite{CS}.

math.DG↗

arXiv2016

Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection

This work investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints. That is, in a sensor network, the fusion center sequentially acquires samples by selecting one "most informative" sensor at each time until a reliable decision can be made. In particular, the sensor selection is carried out in the online fashion since it depends on all the previous samples at each time. Our goal is to develop the sequential test (i.e., stopping rule and decision function) and sensor selection strategy that minimize the expected sample size subject to the constraints on the error probabilities and sensor usages. To this end, we first recast the usage-constrained formulation into a Bayesian optimal stopping problem with different sampling costs for the usage-contrained sensors. The Bayesian problem is then studied under both finite- and infinite-horizon setups, based on which, the optimal solution to the original usage-constrained problem can be readily established. Moreover, by capitalizing on the structures of the optimal solution, a lower bound is obtained for the optimal expected sample size. In addition, we also propose algorithms to approximately evaluate the parameters in the optimal sequential test so that the sensor usage and error probability constraints are satisfied. Finally, numerical experiments are provided to illustrate the theoretical findings, and compare with the existing methods.

stat.AP↗

arXiv2015

Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database a priori for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a tensor and use tensor algebraic methods for an adaptive tubal-sampling of this fingerprint space. In particular using a recently proposed tensor algebraic framework in [1] we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by 71% for the high SNR case and 55% for the low SNR case.

cs.IT↗

arXiv2015

An LS-Decomposition Approach for Robust Data Recovery in Wireless Sensor Networks

Wireless sensor networks are widely adopted in military, civilian and commercial applications, which fuels an exponential explosion of sensory data. However, a major challenge to deploy effective sensing systems is the presence of {\em massive missing entries, measurement noise, and anomaly readings}. Existing works assume that sensory data matrices have low-rank structures. This does not hold in reality due to anomaly readings, causing serious performance degradation. In this paper, we introduce an {\em LS-Decomposition} approach for robust sensory data recovery, which decomposes a corrupted data matrix as the superposition of a low-rank matrix and a sparse anomaly matrix. First, we prove that LS-Decomposition solves a convex program with bounded approximation error. Second, using data sets from the IntelLab, GreenOrbs, and NBDC-CTD projects, we find that sensory data matrices contain anomaly readings. Third, we propose an accelerated proximal gradient algorithm and prove that it approximates the optimal solution with convergence rate $O(1/k^2)$ ($k$ is the number of iterations). Evaluations on real data sets show that our scheme achieves recovery error $\leq 5\%$ for sampling rate $\geq 50\%$ and almost exact recovery for sampling rate $\geq 60\%$, while state-of-the-art methods have error $10\% \sim 15\%$ at sampling rate $90\%$.

cs.IT↗

arXiv2015

A Practical O(R\log\log n+n) time Algorithm for Computing the Longest Common Subsequence

In this paper, we revisit the much studied LCS problem for two given sequences. Based on the algorithm of Iliopoulos and Rahman for solving the LCS problem, we have suggested 3 new improved algorithms. We first reformulate the problem in a very succinct form. The problem LCS is abstracted to an abstract data type DS on an ordered positive integer set with a special operation Update(S,x). For the two input sequences X and Y of equal length n, the first improved algorithm uses a van Emde Boas tree for DS and its time and space complexities are O(R\log\log n+n) and O(R), where R is the number of matched pairs of the two input sequences. The second algorithm uses a balanced binary search tree for DS and its time and space complexities are O(R\log L+n) and O(R), where L is the length of the longest common subsequence of X and Y. The third algorithm uses an ordered vector for DS and its time and space complexities are O(nL) and O(R).

cs.DS↗

arXiv2015

Hyperbolicity versus non-hyperbolic ergodic measures inside homoclinic classes

We prove that, for $C^1$-generic diffeomorphisms, if a homoclinic class is not hyperbolic, then there is a non-hyperbolic ergodic measure supported on it. This proves a conjecture by Díaz and Gorodetski [28]. We also discuss the conjectured existence of periodic points with different stable dimension in the class.

math.DS↗