Searcharxiv⌕ Search

arXiv subjects

Xiaodong Wang

Publications and source records attributed to Xiaodong Wang.

At least 181 records · Page 10Linked to original sources

Interference Removal for Radar/Communication Co-existence: the Random Scattering Case

In this paper we consider an un-cooperative spectrum sharing scenario, wherein a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of magnitude of the transmitted powers in play, we focus on the issue of interference mitigation at the communication receiver. We explicitly account for the reverberation produced by the (typically high-power) radar transmitter whose signal hits scattering centers (whether targets or clutter) producing interference onto the communication receiver, which is assumed to operate in an un-synchronized and un-coordinated scenario. We first show that receiver design amounts to solving a non-convex problem of joint interference removal and data demodulation: next, we introduce two algorithms, both exploiting sparsity of a proper representation of the interference and of the vector containing the errors of the data block. The first algorithm is basically a relaxed constrained Atomic Norm minimization, while the latter relies on a two-stage processing structure and is based on alternating minimization. The merits of these algorithms are demonstrated through extensive simulations: interestingly, the two-stage alternating minimization algorithm turns out to achieve satisfactory performance with moderate computational complexity.

eess.SY↗

Place recognition: An Overview of Vision Perspective

Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of wisdom accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place recognition literature. Since condition invariant and viewpoint invariant features are essential factors to long-term robust visual place recognition system, We start with traditional image description methodology developed in the past, which exploit techniques from image retrieval field. Recently, the rapid advances of related fields such as object detection and image classification have inspired a new technique to improve visual place recognition system, i.e., convolutional neural networks (CNNs). Thus we then introduce recent progress of visual place recognition system based on CNNs to automatically learn better image representations for places. Eventually, we close with discussions and future work of place recognition.

cs.CV↗

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. Nevertheless, by considering a MEC system consisting of multiple mobile users with stochastic task arrivals and wireless channels in this paper, the design of computation offloading policies is challenging to minimize the long-term average computation cost in terms of power consumption and buffering delay. A deep reinforcement learning (DRL) based decentralized dynamic computation offloading strategy is investigated to build a scalable MEC system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn efficient computation offloading policies independently at each mobile user. Thus, powers of both local execution and task offloading can be adaptively allocated by the learned policies from each user's local observation of the MEC system. Numerical results are illustrated to demonstrate that efficient policies can be learned at each user, and performance of the proposed DDPG based decentralized strategy outperforms the conventional deep Q-network (DQN) based discrete power control strategy and some other greedy strategies with reduced computation cost. Besides, the power-delay tradeoff is also analyzed for both the DDPG based and DQN based strategies.

cs.LG↗

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of our models, describe high performance optimizations targeting existing systems, point out their limitations and make suggestions for the future general-purpose/accelerated inference hardware. Also, we highlight the need for better co-design of algorithms, numerics and computing platforms to address the challenges of workloads often run in data centers.

cs.LG↗

Quickest Detection of Time-Varying False Data Injection Attacks in Dynamic Linear Regression Models

Motivated by the sequential detection of false data injection attacks (FDIAs) in a dynamic smart grid, we consider a more general problem of sequentially detecting time-varying FDIAs in dynamic linear regression models. The unknown time-varying parameter vector in the linear regression model and the FDIAs impose a significant challenge for designing a computationally efficient detector. We first propose two Cumulative-Sum-type algorithms to address this challenge. One is called generalized Cumulative-Sum (GCUSUM) algorithm, and the other one is called relaxed generalized Cumulative-Sum (RGCUSUM) algorithm, which is a modified version of the GCUSUM. It can be shown that the computational complexity of the proposed RGCUSUM algorithm scales linearly with the number of observations. Next, considering Lordon's setup, for any given constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed RGCUSUM algorithm is derived, which provides a guideline for the design of the proposed RGCUSUM algorithm to achieve the prescribed performance requirement. In addition, for any given threshold employed in the proposed RGCUSUM algorithm, an upper bound on the expected detection delay is also provided. The performance of the proposed RGCUSUM algorithm is also numerically studied in the context of an IEEE standard power system under FDIAs.

cs.IT↗

Multi-Sensor Sequential Change Detection with Unknown Change Propagation Pattern

The problem of detecting changes with multiple sensors has received significant attention in the literature. In many practical applications such as critical infrastructure monitoring and modeling of disease spread, a useful change propagation model is one where change eventually happens at all sensors, but where not all sensors witness change at the same time instant. While prior work considered the case of known change propagation dynamics, this paper studies a more general setting of unknown change propagation pattern (trajectory). A Bayesian formulation of the problem in both centralized and decentralized settings is studied with the goal of detecting the first time instant at which any sensor witnesses a change. Using the dynamic programming (DP) framework, the optimal solution structure is derived and in the rare change regime, several more practical change detection algorithms are proposed. Under certain conditions, the first-order asymptotic optimality of a proposed algorithm called multichart test is shown as the false alarm probability vanishes. To further reduce the computational complexity, change detection algorithms are proposed based on online estimation of the unknown change propagation pattern. Numerical studies illustrate that the proposed detection techniques offer near-optimal performance. Further, in the decentralized setting, it is shown that if an event-triggered sampling scheme called level-crossing sampling with hysteresis (LCSH) is used for sampling and transmission of local statistics, the detection performance can be significantly improved using the same amount of communication resources compared to the conventional uniform-in-time sampling (US) scheme.

cs.IT↗

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection problem as a partially observable Markov decision process (POMDP) problem and propose a universal robust online detection algorithm using the framework of model-free reinforcement learning (RL) for POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based algorithm in timely and accurate detection of cyber-attacks targeting the smart grid.

cs.LG↗

Measures of intermediate entropies for star vector fields

We prove that all star vector fields, including Lorenz attractors and multisingular hyperbolic vector fields, admit the intermediate entropy property. To be precise, if $X$ is a star vector field with $h_{top}(X)>0$, then for any $h\in [0,h_{top}(X))$, there exists an ergodic invariant measure $μ$ of $X$ such that $h_μ(X)=h$. Moreover, we show that the topological entropy is lower semi-continuous for star vector fields.

math.DS↗

Efficient File Delivery for Coded Prefetching in Shared Cache Networks with Multiple Requests Per User

We consider a centralized caching network, where a server serves several groups of users, each having a common shared homogeneous fixed-size cache and requesting arbitrary multiple files. An existing coded prefetching scheme is employed where each file is broken into multiple fragments and each cache stores multiple coded packets each formed by XORing fragments from different files. For such a system, we propose an efficient file delivery scheme with explicit constructions by the server to meet the arbitrary multi-requests of all user-groups. Specifically, the stored coded packets of each cache are classified into four types based on the composition of the file fragments encoded. A delivery strategy is developed, which separately delivers part of each packet type first and then combinatorially delivers the remaining different packet types in the last stage. The rate as well as the worst rate of the proposed delivery scheme are analyzed. We show that our caching model and delivery scheme can incorporate some existing coded caching schemes as special cases. Moreover, for the special case of uniform requests and uncoded prefetching, we make a comparison with existing results, and show that our approach can achieve a lower delivery rate. We also provide numerical results on the delivery rate for the proposed scheme.

cs.IT↗

LEOS-assisted Inter-GEOS Communication via Distributed-storage Coding

We consider a space communication network consisting of Geosynchronous Earth Orbit satellites (GEOSs) and Low Earth Orbit satellites (LEOSs). In case of no direct communication link between two GEOSs, the data exchange between them is through relay by the LEOSs. In particular, the source GEOS sends coded data to multiple LEOSs based on the distributed storage framework. The destination GEOS then retrieves certain amount of data from each LEOS for data reconstruction. For the GEOS-LEOS downlink, a regenerating-code-based transmission scheme is optimized to guarantee data reconstructability, where the transmission power allocation to the LEOSs is proposed to minimize the total transmission energy. We also consider the power allocation to minimize the total transmission time given the total transmission energy. For the LEOS-GEOS uplink, a flexible partial-downloading coding transmission scheme is proposed to guarantee data reconstructability, where the joint uploaded-data size and power allocations are proposed to minimize the total transmission energy or the total transmission time. Extensive simulation results are presented to evaluate the proposed algorithms and show that regenerating code can achieve lower transmission energy and shorter transmission time for data regeneration than conventional maximum-distance separable (MDS) code.

cs.IT↗

A Simple and Space Efficient Segment Tree Implementation

The segment tree is an extremely versatile data structure. In this paper, a new heap based implementation of segment trees is proposed. In such an implementation of segment tree, the structural information associated with the tree nodes can be removed completely. Some primary computational geometry problems such as stabbing counting queries, measure of union of intervals, and maximum clique size of Intervals are used to demonstrate the efficiency of the new heap based segment tree implementation. Each interval in a set $S=\{I_1 ,I_2 ,\cdots,I_n\}$ of $n$ intervals can be insert into or delete from the heap based segment tree in $O(\log n)$ time. All the primary computational geometry problems can be solved efficiently.

cs.DS↗

Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection (FDI) and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.

cs.IT↗

An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and Quickest Change Detection

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this paper, we propose an enhanced maximum power point tracking (MPPT) method utilizing the state estimation by the sequential Monte Carlo (SMC) filtering which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance (I-C) MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP (GMPP) to refine the estimation by SMC. Moreover, a quick irrandiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change under different noise levels.

eess.SP↗

Consensus-based Distributed Quickest Detection of Attacks with Unknown Parameters

Sequential attack detection in a distributed estimation system is considered, where each sensor successively produces one-bit quantized samples of a desired deterministic scalar parameter corrupted by additive noise. The unknown parameters in the pre-attack and post-attack models, namely the desired parameter to be estimated and the injected malicious data at the attacked sensors pose a significant challenge for designing a computationally efficient scheme for each sensor to detect the occurrence of attacks by only using local communication with neighboring sensors. The generalized Cumulative Sum (GCUSUM) algorithm is considered, which replaces the unknown parameters with their maximum likelihood estimates in the CUSUM test statistic. For the problem under consideration, a sufficient condition is provided under which the expected false alarm period of the GCUSUM can be guaranteed to be larger than any given value. Next, we consider the distributed implementation of the GCUSUM. We first propose an alternative test statistic which is asymptotically equivalent to that of GCUSUM. Then based on the proposed alternative test statistic and running consensus algorithms, we propose a distributed approximate GCUSUM algorithm which significantly reduce the prohibitively high computational complexity of the centralized GCUSUM. Numerical results show that the distributed approximate GCUSUM algorithm can provide a performance that is comparable to the centralized GCUSUM.

cs.IT↗

Super-Resolution mmWave Channel Estimation using Atomic Norm Minimization

We propose super-resolution MIMO channel estimators for millimeter-wave (mmWave) systems that employ hybrid analog and digital beamforming and generalized spatial modulation, respectively. Exploiting the inherent sparsity of mmWave channels, the channel estimation problem is formulated as an atomic norm minimization that enhances sparsity in the continuous angles of departure and arrival. Both pilot-assisted and data-aided channel estimators are developed, with the former one formulated as a convex problem and the latter as a non-convex problem. To solve these formulated channel estimation problems, we develop a computationally efficient conjugate gradient descent method based on non-convex factorization which restricts the search space to low-rank matrices. Simulation results are presented to illustrate the superior channel estimation performance of the proposed algorithms for both types of mmWave systems compared to the existing compressed-sensing-based estimators with finely quantized angle grids.

cs.IT↗

Joint Transportation and Charging Scheduling in Public Vehicle Systems - A Game Theoretic Approach

Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guarantee and the cost-effective charging strategy under the real-time electricity pricing. In this paper, we study the joint transportation and charging scheduling for PV systems to balance the transportation and charging demands, ensuring the long-term operation. We adopt a cake cutting game model to capture the interactions among PV groups, the cloud and smart grids. The cloud announces strategies to coordinate the allocation of transportation and energy resources among PV groups. All the PV groups try to maximize their joint transportation and charging utilities. We propose an algorithm to obtain the unique normalized Nash equilibrium point for this problem. Simulations are performed to confirm the effects of our scheme under the real taxi and power grid data sets of New York City. Our results show that our scheme achieves almost the same transportation performance compared with a heuristic scheme, namely, transportation with greedy charging; however, the average energy price of the proposed scheme is 10.86% lower than the latter one.

eess.SY↗

An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems

As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with minimum number of vehicles and lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, we propose an efficient path planning strategy that focuses on a limited potential search area for each vehicle by filtering out the requests that violate passenger service quality level, so that the global search is reduced to local search. We analyze the performance of the proposed solution such as reduction ratio of computational complexity. Simulations based on the Manhattan taxi data set show that, the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.

cs.AI↗

Quickest Attack Detection in Smart Grid Based on Sequential Monte Carlo Filtering

Quick and accurate detection of cyber attack is key to the normal operation of the smart grid system. In this paper, a joint state estimation and sequential attack detection method for a given bus with grid frequency drift is proposed that utilizes the commonly monitored output voltage. In particular, based on a non-linear state-space model derived from the three-phase sinusoidal voltage equations, we employ the sequential Monte Carlo (SMC) filtering to estimate the system state. The output of the SMC filter is fed into a CUSUM test to detect the attack in a fastest way. Moreover, an adaptive sampling strategy is proposed to reduce the rate of taking measurements and communicating with the controller. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and efficient detection of various types of attacks in power systems.

eess.SP↗