arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 523 records · Page 29Linked to original sources

arXiv2019

Optimal Resource Allocation for Wireless Powered Mobile Edge Computing with Dynamic Task Arrivals

This paper considers a wireless powered multiuser mobile edge computing (MEC) system, where a multi-antenna access point (AP) employs the radio-frequency (RF) signal based wireless power transfer (WPT) to charge a number of distributed users, and each user utilizes the harvested energy to execute computation tasks via local computing and task offloading. We consider the frequency division multiple access (FDMA) protocol to support simultaneous task offloading from multiple users to the AP. Different from previous works that considered one-shot optimization with static task models, we study the joint computation and wireless resource allocation optimization with dynamic task arrivals over a finite time horizon consisting of multiple slots. Under this setup, our objective is to minimize the system energy consumption including the AP's transmission energy and the MEC server's computing energy over the whole horizon, by jointly optimizing the transmit energy beamforming at the AP, and the local computing and task offloading strategies at the users over different time slots. To characterize the fundamental performance limit of such systems, we focus on the offline optimization by assuming the task and channel information are known a-priori at the AP. In this case, the energy minimization problem corresponds to a convex optimization problem. Leveraging the Lagrange duality method, we obtain the optimal solution to this problem in a well structure. It is shown that in order to maximize the system energy efficiency, the optimal number of task input-bits at each user and the AP are monotonically increasing over time, and the offloading strategies at different users depend on both the wireless channel conditions and the task load at the AP. Numerical results demonstrate the benefit of the proposed joint-WPT-MEC design over alternative benchmark schemes without such joint design.

cs.IT↗

arXiv2019

Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior

It is becoming increasingly common to see large collections of network data objects -- that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic tools already standard for scalar and vector data. In this paper, our focus is on averages of unlabeled, undirected networks with edge weights. Specifically, we (i) characterize a certain notion of the space of all such networks, (ii) describe key topological and geometric properties of this space relevant to doing probability and statistics thereupon, and (iii) use these properties to establish the asymptotic behavior of a generalized notion of an empirical mean under sampling from a distribution supported on this space. Our results rely on a combination of tools from geometry, probability theory, and statistical shape analysis. In particular, the lack of vertex labeling necessitates working with a quotient space modding out permutations of labels. This results in a nontrivial geometry for the space of unlabeled networks, which in turn is found to have important implications on the types of probabilistic and statistical results that may be obtained and the techniques needed to obtain them.

math.ST↗

arXiv2019

Most memory efficient distributed super points detection on core networks

The super point, a host which communicates with lots of others, is a kind of special hosts gotten great focus. Mining super point at the edge of a network is the foundation of many network research fields. In this paper, we proposed the most memory efficient super points detection scheme. This scheme contains a super points reconstruction algorithm called short estimator and a super points filter algorithm called long estimator. Short estimator gives a super points candidate list using thousands of bytes memory and long estimator improves the accuracy of detection result using millions of bytes memory. Combining short estimator and long estimator, our scheme acquires the highest accuracy using the smallest memory than other algorithms. There is no data conflict and floating operation in our scheme. This ensures that our scheme is suitable for parallel running and we deploy our scheme on a common GPU to accelerate processing speed. We also describe how to extend our algorithm to sliding time. Experiments on several real-world core network traffics show that our algorithm acquires the highest accuracy with only consuming littler than one-fifth memory of other algorithms.

cs.DC↗

arXiv2019

GPU based Real-time Super Hosts Detection at Distributed Edge Routers

The super host is a special host on the network which contacts with many other hosts during a certain time window. They play important roles in network researches such as scanners detection, resource allocation, spam filtering and so on. How to find super hosts in real time is the foundation of these applications. In this paper, a novel algorithm, denoted as CBAA, is proposed to solve this problem at edge routers. CBAA divides network traffic into different parts. A cube of bits array is devised to store hosts' linking information of different traffic parts when scanning packets. At the end of each time window, CBAA restores super hosts very fast because there are only a fraction of super hosts in each traffic part. CBAA is also a parallel algorithm. It's easy to deploy CBAA in GPU to deal with high-speed network traffic in real time. Experiments on a real-world core network prove the advantage of our algorithm.

cs.NI↗

arXiv2018

A Generic Receiver Architecture for MIMO Wireless Power Transfer with Non-Linear Energy Harvesting

This letter investigates a multiple-input multiple-output (MIMO) wireless power transfer (WPT) system under practical non-liner energy harvesting (EH) models. We propose a new generic energy receiver (ER) architecture consisting of $N$ receive antennas and $L$ rectifiers, for which one power splitter is inserted after each antenna to adaptively split the received radio frequency (RF) signals among the $L$ rectifiers for efficient non-linear RF-to-direct current (DC) conversion. With the proposed architecture, we maximize the total harvested DC power at the ER, by jointly optimizing the transmit energy beamforming at the energy transmitter (ET) and the power splitting ratios at the ER. Numerical results show that our proposed design by exploiting the nonlinearity of EH significantly improves the harvested DC power at the ER, as compared to two conventional designs.

eess.SP↗

arXiv2018

Secure UAV Communication with Cooperative Jamming and Trajectory Control

This paper presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends artificial noise (AN) to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully-controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location {\emph{a-priori}}. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation (SCA).

cs.IT↗

arXiv2018

Task Offloading and Replication for Vehicular Cloud Computing: A Multi-Armed Bandit Approach

Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a roadside unit, to complete task requests using the vehicle-as-a-resource framework. In this paper, we investigate timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by several server vehicles). A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel Contextual-Combinatorial Multi-Armed Bandit (CC-MAB) learning framework. DATE-V is `contextual' because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is `combinatorial' because it aims to replicate the received task and send the task replications to multiple server vehicles to guarantee the service timeliness. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.

cs.DC↗

arXiv2018

VATE: a trade-off between memory and preserving time for high accuracy cardinalities estimation under sliding time window

Host cardinality is one of the important attributes in the field of network research. The cardinality estimation under sliding time window has become a research hotspot in recent years because of its high accuracy and small delay. This kind of algorithms preserve the time information of sliding time window by introducing more powerful counters. The more counters used in these algorithms, the higher the estimation accuracy of these algorithms. However, the available number of sliding counters is limited due to their large memory footprint or long state-maintenance time. To solve this problem, a new sliding counter, asynchronous timestamp (AT), is designed in this paper which has the advantages of less memory consumption and low state-maintenance time. AT can replace counters in existing algorithms. On the same device, more AT can be used to achieve higher accuracy. Based on AT, this paper designs a new multi-hosts cardinalities estimation algorithm VATE. VATE is also a parallel algorithm that can be deployed on GPU. With the parallel processing capability of GPU, VATE can estimate cardinalities of hosts in a 40 Gb/s high-speed network in real time at the time granularity of 1 second.

cs.NI↗

arXiv2018

Optimal 1D Trajectory Design for UAV-Enabled Multiuser Wireless Power Transfer

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV's one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first work to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Towards this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the original UAV-speed-constrained trajectory optimization problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The obtained optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly (SHF) structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Numerical results show that our proposed optimal solution significantly outperforms the benchmark schemes in prior works under different scenarios.

cs.IT↗

arXiv2018

Cardinalities estimation under sliding time window by sharing HyperLogLog Counter

Cardinalities estimation is an important research topic in network management and security. How to solve this problem under sliding time window is a hot topic. HyperLogLog is a memory efficient algorithm work under a fixed time window. A sliding version of HyperLogLog can work under sliding time window by replacing every counter of HyperLogLog with a list of feature possible maxim (LFPM). But LFPM is a dynamic structure whose size is variable at running time. This paper proposes a novel counter for HyperLogLog which consumes smaller size of memory than that of LFPM. Our counter is called bit distance recorder BDR, because it maintains the distance of every left most "1" bit position. The size of BDR is fixed. Based on BDR, we design a multi hosts' cardinalities estimation algorithm under sliding time window, virtual bit distance recorder VBDR. VBDR allocate a virtual vector of BDR for every host and every physical BDR is shared by several hosts to improve the memory usage. After a small modifcation, we propose another two parallel versions of VBDR which can run on GPU to handle high speed traffic. One of these parallel VBDR is fast in IP pair scanning and the other one is memory efficient. BDR is also suitable for other cardinality estimation algorithms such as PCSA, LogLog.

cs.NI↗

arXiv2018

Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN), in which a UAV is dispatched as a mobile access point (AP) to serve a set of ground users periodically. The UAV employs the radio frequency (RF) wireless power transfer (WPT) to charge the users in the downlink, and the users use the harvested RF energy to send independent information to the UAV in the uplink. Unlike the conventional WPCN with fixed APs, the UAV-enabled WPCN can exploit the mobility of the UAV via trajectory design, jointly with the wireless resource allocation optimization, to maximize the system throughput. In particular, we aim to maximize the uplink common (minimum) throughput among all ground users over a finite UAV's flight period, subject to its maximum speed constraint and the users' energy neutrality constraints. The resulted problem is non-convex and thus difficult to be solved optimally. To tackle this challenge, we first consider an ideal case without the UAV's maximum speed constraint, and obtain the optimal solution to the relaxed problem. The optimal solution shows that the UAV should successively hover above a finite number of ground locations for downlink WPT, as well as above each of the ground users for uplink communication. Next, we consider the general problem with the UAV's maximum speed constraint. Based on the above multi-location-hovering solution, we first propose an efficient successive hover-and-fly trajectory design, jointly with the downlink and uplink wireless resource allocation, and then propose a locally optimal solution by applying the techniques of alternating optimization and successive convex programming (SCP). Numerical results show that the proposed UAV-enabled WPCN achieves significant throughput gains over the conventional WPCN with fixed-location AP.

cs.IT↗

arXiv2018

Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing

This paper proposes a novel user cooperation approach in both computation and communication for mobile edge computing (MEC) systems to improve the energy efficiency for latency-constrained computation. We consider a basic three-node MEC system consisting of a user node, a helper node, and an access point (AP) node attached with an MEC server, in which the user has latency-constrained and computation-intensive tasks to be executed. We consider two different computation offloading models, namely the partial and binary offloading, respectively. Under this setup, we focus on a particular finite time block and develop an efficient four-slot transmission protocol to enable the joint computation and communication cooperation. Besides the local task computing over the whole block, the user can offload some computation tasks to the helper in the first slot, and the helper cooperatively computes these tasks in the remaining time; while in the second and third slots, the helper works as a cooperative relay to help the user offload some other tasks to the AP for remote execution in the fourth slot. For both cases with partial and binary offloading, we jointly optimize the computation and communication resources allocation at both the user and the helper (i.e., the time and transmit power allocations for offloading, and the CPU frequencies for computing), so as to minimize their total energy consumption while satisfying the user's computation latency constraint. Although the two problems are non-convex in general, we propose efficient algorithms to solve them optimally. Numerical results show that the proposed joint computation and communication cooperation approach significantly improves the computation capacity and energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design.

cs.IT↗

arXiv2018

Spatio-temporal Edge Service Placement: A Bandit Learning Approach

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.

cs.NI↗

arXiv2018

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhaul

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users in various applications (e.g., in emergence situations). This paper considers a UAV-enabled wireless network, in which multiple UAVs are deployed as aerial base stations (BSs) to serve users distributed on the ground. Different from prior works that ignore UAV's backhaul connections, we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls. We also consider that the air-to-ground (A2G) access links from UAVs to users and the air-to-air (A2A) backhaul links among UAVs are operated over orthogonal frequency bands. Under this setup, we aim to maximize the common (or minimum) throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs, by optimizing the UAVs' deployment locations, jointly with the bandwidth and power allocation of both the access and backhaul links. However, the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally. To tackle this issue, we use the techniques of alternating optimization and successive convex programming (SCP) to obtain a locally optimal solution. Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.

cs.IT↗

arXiv2018

Dynamic Edge Caching with Popularity Drifting

Caching at the network edge devices such as wireless caching stations (WCS) is a key technology in the 5G network. The spatial-temporal diversity of content popularity requires different content to be cached in different WCSs and periodically updated to adapt to temporal changes. In this paper, we study how the popularity drifting speed affects the number of required broadcast transmissions by the MBS and then design coded transmission schemes by leveraging the broadcast advantage under the index coding framework. The key idea is that files already cached in WCSs, which although may be currently unpopular, can serve as side information to facilitate coded broadcast transmission for cache updating. Our algorithm extends existing index coding-based schemes from a single-request scenario to a multiple-request scenario via a "dynamic coloring" approach. Simulation results indicate that a significant bandwidth saving can be achieved by adopting our scheme.

cs.IT↗

arXiv2018

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS's decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a {\em mixed Boolean convex problem} that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution, and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB based solution, and the convex relaxation based solution achieves a suboptimal performance but with lower implementation complexity.

cs.IT↗

arXiv2018

Wireless Powered User Cooperative Computation in Mobile Edge Computing Systems

This paper studies a wireless powered mobile edge computing (MEC) system, where a dedicated energy transmitter (ET) uses the radio-frequency (RF) signal enabled wireless power transfer (WPT) to charge wireless devices for sustainable computation. In such a system, we present a new user cooperation approach to improve the computation performance of active devices, in which surrounding idle devices are enabled as helpers to use their opportunistically harvested wireless energy from the ET to help remotely execute active users' computation tasks. In particular, we consider a basic scenario with one user (with computation tasks to execute) and multiple helpers, in which the user can partition the computation tasks into various parts for local execution and computation offloading to helpers, respectively. Both the user and helpers are subject to the so-called energy neutrality constraints, such that their energy consumption does not exceed the respective energy harvested from the ET. Under this setup and considering a frequency division multiple access (FDMA) based computation offloading protocol, we maximize the computation rate (i.e., the number of computation bits over a particular time block) of the user, by jointly optimizing the transmit energy beamforming at the ET, as well as the communication and computation resource allocations at both the user and helpers. By leveraging the Lagrange duality method, we present the optimal solution to this problem in a semi-closed form. Numerical results show that the proposed wireless powered user cooperative computation design significantly improves the computation rate at the user, as compared to conventional schemes without such cooperation.

cs.IT↗

arXiv2018

A tensor model for nematic phases of bent-core molecules based on molecular theory

We construct a tensor model for nematic phases of bent-core molecules from molecular theory. The form of free energy is determined by molecular symmetry, which includes the couplings and derivatives of a vector and two second-order tensors, with the coefficients determined by molecular parameters. We use the model to study the nematic phases resulted from the hard-core potential. Unlike most macroscopic models, we are able to obtain the phase diagram about the molecular parameters, but not merely some phenomenological coefficients. The tensor model is applicable to other molecules with the same symmetry, which we demonstrate by studying the phase diagram of star molecules.

cond-mat.soft↗