Search arXiv⌕ Search

arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 505 records · Page 28Linked to original sources
arXiv2019

Joint 3D Maneuver and Power Adaptation for Secure UAV Communication with CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, where we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, where we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors.

cs.IT↗
arXiv2019

Slow light,double directional rainbow trapping and releasing at terahertz frequencies

Slow light and rainbow trapping attract many attentions in last twenty years, and in most of the previous works, the researchers achieve the slow light and rainbow trapping with complicate configurations or techniques, for example, metamaterial techniques. In this paper, we propose a simple waveguide consisted with semiconductor and dielectric layers, and we find that the slow-light peaks appear in the dispersion curves. Besides, the cutoff frequency with $v_g = 0$ ($v_g$ is the group velocity) in the slow-light peaks is depending on the thicknesses of the semiconductor and dielectric. Then, we design a tapered, horizontally symmetric structure. By using software COMSOL and finite difference time domain method as well, we achieve double directional rainbow trapping and more importantly, releasing the rainbow in such structure. This approach is promising for optical isolator, optical buffer, optical switch and other optical functional devices in miniaturization optical integrated circuit.

physics.optics↗
arXiv2019

Joint Communication and Computation Optimization for Wireless Powered Mobile Edge Computing with D2D Offloading

This paper studies a wireless powered mobile edge computing (MEC) system with device-to-device (D2D)-enabled task offloading. In this system, a set of distributed multi-antenna energy transmitters (ETs) use collaborative energy beamforming to wirelessly charge multiple users. By using the harvested energy, the actively computing user nodes can offload their computation tasks to nearby idle users (as helper nodes) via D2D communication links for self-sustainable remote computing. We consider the frequency division multiple access (FDMA) protocol, such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands. Furthermore, we focus on a particular time block for task execution, which is divided into three slots for computation task offloading, remote computing, and result downloading, respectively, at different user-helper pairs. Under this setup, we jointly optimize the collaborative energy beamforming at ETs, the communication and computation resource allocation at users and helpers, and the user-helper pairing, so as to maximize the sum computation rate (i.e., the number of task input-bits executed over this block) of the users, subject to individual energy neutrality constraints at both users and helpers. First, we consider the computation rate maximization problem under any given user-helper pairs, for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization. Next, we develop the optimal user-helper pairing scheme based on exhaustive search and a low-complexity scheme based on greedy selection. Numerical results show that the proposed design significantly improves the sum computation rate at users, as compared to benchmark schemes without such joint optimization.

cs.IT↗
arXiv2019

Joint Transmit and Reflective Beamforming Design for IRS-Assisted Multiuser MISO SWIPT Systems

This paper studies an intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system. In this system, a multi-antenna access point (AP) uses transmit beamforming to send both information and energy signals to a set of receivers each for information decoding (ID) or energy harvesting (EH), and a dedicatedly deployed IRS properly controls its reflecting phase shifts to form passive reflection beams for facilitating both ID and EH at receivers. Under this setup, we jointly optimize the (active) information and energy transmit beamforming at the AP together with the (passive) reflective beamforming at the IRS, to maximize the minimum power received at all EH receivers, subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at ID receivers, and the maximum transmit power constraint at the AP. Although the formulated SINR-constrained min-energy maximization problem is highly non-convex, we present an efficient algorithm to obtain a high-quality solution by using the techniques of alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed IRS-assisted SWIPT system with both information and energy signals achieves significant performance gains over benchmark schemes without IRS deployed and/or without dedicated energy signals used.

eess.SP↗
arXiv2019

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts.

cs.LG↗
arXiv2019

Common Throughput Maximization for UAV-Enabled Interference Channel with Wireless Powered Communications

This paper studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel for wireless powered communication networks (WPCNs), in which two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices on the ground and collect information from them. We consider two scenarios when both UAVs cooperate in energy transmission and/or information reception via interference coordination and coordinated multi-point (CoMP), respectively. For both scenarios, the UAVs' controllable mobility is exploited via trajectory design to not only enhance the wireless power transfer (WPT) efficiency in the downlink, but also mitigate the co-channel interference for wireless information transfer (WIT) in the uplink. In particular, the objective is to maximize the uplink common (minimum) throughput of the two IoT-devices over a finite UAV mission period, by jointly optimizing the trajectories of both UAVs and the downlink/uplink wireless resource allocation, subject to the maximum flying speed and collision avoidance constraints at UAVs, as well as the individual energy neutrality constraints at IoT-devices. Under both scenarios of interference coordination and CoMP, we first obtain the optimal solutions to the two common-rate maximization problems in well structures for the special case with sufficiently long UAV mission duration. Next, we obtain high-quality solutions for the practical case with finite UAV mission duration by using the alternating optimization and successive convex approximation (SCA).

cs.IT↗
arXiv2019

Fundamental Rate Limits of UAV-Enabled Multiple Access Channel with Trajectory Optimization

This paper studies an unmanned aerial vehicle (UAV)-enabled multiple access channel (MAC), in which multiple ground users transmit individual messages to a mobile UAV in the sky. We consider a linear topology scenario, where these users locate in a straight line and the UAV flies at a fixed altitude above the line connecting them. Under this setup, we jointly optimize the one-dimensional (1D) UAV trajectory and wireless resource allocation to reveal the fundamental rate limits of the UAV-enabled MAC, under the users' individual maximum power constraints and the UAV's maximum flight speed constraints. First, we consider the capacity-achieving non-orthogonal multiple access (NOMA) transmission with successive interference cancellation (SIC) at the UAV receiver. In this case, we characterize the capacity region by maximizing the average sum-rate of users subject to rate profile constraints. To optimally solve this highly non-convex problem, we transform the original speed-constrained trajectory optimization problem into a speed-free problem that is optimally solvable via the Lagrange dual decomposition. It is rigorously proved that the optimal 1D trajectory solution follows the successive hover-and-fly (SHF) structure. Next, we consider two orthogonal multiple access (OMA) transmission schemes, i.e., frequency-division multiple access (FDMA) and time-division multiple access (TDMA). We maximize the achievable rate regions in the two cases by jointly optimizing the 1D trajectory design and wireless resource (frequency/time) allocation. It is shown that the optimal trajectory solutions still follow the SHF structure but with different hovering locations. Finally, numerical results show that the proposed optimal trajectory designs achieve considerable rate gains over other benchmark schemes, and the capacity region achieved by NOMA significantly outperforms the rate regions by FDMA and TDMA.

cs.IT↗
arXiv2019

Ultra-subwavelength focusing and giant magnetic-field enhancement in a low-loss one-way waveguide based on remanence

The subwavelength focusing based on surface plasmon polaritons (SPP) has been widely explored in tapered metallic structures. However, the efficiency of energy localization is relatively weak, largely due to high propagation loss and strong back reflection. Here, we propose a straight-tapered 3-dimensional (3D) one-way surface magnetoplasmon (SMP) waveguide with the ending surface of $\sim 10^{-4}λ_0 \times 10^{-4}λ_0$ to achieve energy focusing in the microwave regime. Due to low propagation loss of SMP, we achieve huge magnetic field enhancement in such an ultra-subwavelength area, by five orders of magnitude. Instead of using an external static magnetic field, our proposed SMP waveguide relies on remanence, which is very convenient for operating practical 3D applications. These results show promising applications in magnetic-field enhancing or quenching fluorescence, luminescence or nonlinearity of 2D materials, novel scanning near-field microwave microscopy and energy storage.

physics.optics↗
arXiv2019

Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.

cs.CL↗
arXiv2019

Secrecy Transmission in Large-Scale UAV-Enabled Wireless Networks

This paper considers the secrecy transmission in a large-scale unmanned aerial vehicle (UAV)-enabled wireless network, in which a set of UAVs in the sky transmit confidential information to their respective legitimate receivers on the ground, in the presence of another set of randomly distributed suspicious ground eavesdroppers. We assume that the horizontal locations of legitimate receivers and eavesdroppers are distributed as two independent homogeneous Possion point processes (PPPs), and each of the UAVs is positioned exactly above its corresponding legitimate receiver for efficient secrecy communication. Furthermore, we consider an elevation-angle-dependent line-of-sight (LoS)/non-LoS (NLoS) path-loss model for air-to-ground (A2G) wireless channels and employ the wiretap code for secrecy transmission. Under such setups, we first characterize the secrecy communication performance (in terms of the connection probability, secrecy outage probability, and secrecy transmission capacity) in mathematically tractable forms, and accordingly optimize the system configurations (i.e., the wiretap code rates and UAV positioning altitude) to maximize the secrecy transmission capacity, subject to a maximum secrecy outage probability constraint. Next, we propose to use the secrecy guard zone technique for further secrecy protection, and analyze the correspondingly achieved secrecy communication performance. Finally, we present numerical results to validate the theoretical analysis. It is shown that the employment of secrecy guard zone significantly improves the secrecy transmission capacity of this network, and the desirable guard zone radius generally decreases monotonically as the UAVs' and/or the eavesdroppers' densities increase.

cs.IT↗
arXiv2019

Cognitive UAV Communication via Joint Maneuver and Power Control

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV's mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV's 3D placement and power control to maximize the SR's achievable rate, subject to the UAV's altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Next, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR's average achievable rate over this period by jointly optimizing the UAV's 3D trajectory and power control, subject to the additional constraints on UAV's maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR's achievable rate in both scenarios.

cs.IT↗
arXiv2019

READ: a three-communicating-stage distributed super points detections algorithm

A super point is a host that interacts with a far larger number of counterparts in the network over a period of time. Super point detection plays an important role in network research and application. With the increase of network scale, distributed super point detection has become a hot research topic. Compared with single-node super point detection algorithm, the difficulty of super point detection in multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication distributed super point detection algorithm: Rough Estimator based Asynchronous Distributed super point detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super point. At the same time, the Linear Estimator (LE) is used to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. In this paper, we proof that the accuracy of READ in distributed environment is no less than that in the single node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has higher accuracy in distributed environment, but also has less than 5% of communication burden compared with existing algorithms.

cs.DC↗
arXiv2019

A stochastic collocation approach for parabolic PDEs with random domain deformations

This work considers the problem of numerically approximating statistical moments of a Quantity of Interest (QoI) that depends on the solution of a linear parabolic partial differential equation. The geometry is assumed to be random and is parameterized by $N$ random variables. The parabolic problem is remapped to a fixed deterministic domain with random coefficients and shown to admit an extension on a well defined region embedded in the complex hyperplane. A Stochastic collocation method with an isotropic Smolyak sparse grid is used to compute the statistical moments of the QoI. In addition, convergence rates for the stochastic moments are derived and compared to numerical experiments.

math.NA↗
arXiv2019

3D Trajectory Optimization for Secure UAV Communication with CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of distributed ground nodes (GNs) that can cooperate in signal detection, in the presence of several colluding suspicious eavesdroppers. Different from prior works considering the two-dimensional (2D) horizontal trajectory design in the non-CoMP scenario, this paper additionally exploits the UAV's vertical trajectory (or altitude) control for further improving the secrecy communication performance with CoMP. In particular, we jointly optimize the three dimensional (3D) trajectory and transmit power allocation of the UAV to maximize the average secrecy rate at GNs over a particular flight period, subject to the UAV's maximum flight speed and maximum transmit power constraints. To solve the non-convex optimization problem, we propose an alternating-optimization-based approach, which optimizes the transmit power allocation and trajectory design in an alternating manner, by convex optimization and successive convex approximation (SCA), respectively. Numerical results show that in the scenario with CoMP reception, our proposed 3D trajectory optimization significantly outperforms the conventional 2D horizontal trajectory design, by exploiting the additional degree of freedom in vertical trajectory.

eess.SP↗
arXiv2019

Cognitive Wireless Power Transfer in the Presence of Reactive Primary Communication User

This paper studies a cognitive or secondary multi-antenna wireless power transfer (WPT) system over a multi-carrier channel, which shares the same spectrum with a primary wireless information transfer (WIT) system that employs adaptive water-filling power allocation. By controlling the transmit energy beamforming over sub-carriers (SCs), the secondary energy transmitter (S-ET) can directly charge the secondary energy receiver (S-ER), even purposely interfere with the primary WIT system, such that the primary information transmitter (P-IT) can reactively adjust its power allocation (based on water-filling) to facilitate the S-ER's energy harvesting. We investigate how the secondary WPT system can exploit the primary WIT system's reactive power allocation, for improving the wireless energy harvesting performance. In particular, our objective is to maximize the total energy received at the S-ER from both the S-ET and the P-IT, by optimizing the S-ET's energy beamforming over SCs, subject to its maximum transmit power constraint, and the maximum interference power constraint imposed at the primary information receiver (P-IR) to protect the primary WIT. Although the formulated problem is non-convex and difficult to be optimally solved in general, we propose an efficient algorithm to obtain a high-quality solution by employing the Lagrange dual method together with a one-dimensional search. We also present two benchmark energy beamforming designs based on the zero-forcing (ZF) and maximum-ratio-transmission (MRT) principles, respectively, as well as the conventional design without considering the primary WIT system's reaction. Numerical results show that our proposed design leads to significantly improved energy harvesting performance at the S-ER, as compared to these benchmark schemes.

cs.IT↗
arXiv2019

Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning

Shared edge computing platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to mobile users are providing ultra-low latency and location-awareness to a rich portfolio of services. Though ubiquitous edge service provisioning, i.e., deploying the application at all possible edge sites, is always preferable, it is impractical due to often limited operational budget of ASPs. In this case, an ASP has to cautiously decide where to deploy the edge service and how much budget it is willing to use. A central issue here is that the service demand received by each edge site, which is the key factor of deploying benefit, is unknown to ASPs a priori. What's more complicated is that this demand pattern varies temporally and spatially across geographically distributed edge sites. In this paper, we investigate an edge resource rental problem where the ASP learns service demand patterns for individual edge sites while renting computation resource at these sites to host its applications for edge service provisioning. An online algorithm, called Context-aware Online Edge Resource Rental (COERR), is proposed based on the framework of Contextual Combinatorial Multi-armed Bandit (CC-MAB). COERR observes side-information (context) to learn the demand patterns of edge sites and decides rental decisions (including where to rent the computation resource and how much to rent) to maximize ASP's utility given a limited budget. COERR provides a provable performance achieving sublinear regret compared to an Oracle algorithm that knows exactly the expected service demand of edge sites. Experiments are carried out on a real-world dataset and the results show that COERR significantly outperforms other benchmarks.

cs.GT↗
arXiv2019

Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.

cs.IT↗