arXiv subjects

Xun Gong

Publications and source records attributed to Xun Gong.

66 records · Page 4Linked to original sources

arXiv2020

Achievable Rates of Opportunistic Cognitive Radio Systems Using Reconfigurable Antennas with Imperfect Sensing and Channel Estimation

We consider an opportunistic cognitive radio (CR) system in which secondary transmitter (SUtx) is equipped with a reconfigurable antenna (RA). Utilizing the beam steering capability of the RA, we regard a design framework for integrated sector-based spectrum sensing and data communication. In this framework, SUtx senses the spectrum and detects the beam corresponding to active primary user's (PU) location. SUtx also sends training symbols (prior to data symbols), to enable channel estimation at secondary receiver (SUrx) and selection of the strongest beam between SUtx-SUrx for data transmission. We establish a lower bound on the achievable rates of SUtx-SUrx link, in the presence of spectrum sensing and channel estimation errors, and errors due to incorrect detection of the beam corresponding to PU's location and incorrect selection of the strongest beam for data transmission. We formulate a novel constrained optimization problem, aiming at maximizing the derived achievable rate lower bound subject to average transmit and interference power constraints. We optimize the durations of spatial spectrum sensing and channel training as well as data symbol transmission power. Our numerical results demonstrate that between optimizing spectrum sensing and channel training durations, the latter is more important for providing higher achievable rates.

eess.SP↗

arXiv2019

Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas

We consider an opportunistic cognitive radio (CR) system consisting of a primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx), where SUtx is equipped with an electrically steerable parasitic array radiator (ESPAR) antenna with beam steering capability for sensing and communication, and there is a limited feedback channel from SUrx to SUtx. Taking a holistic approach, we develop a framework for integrated sector-based spectrum sensing and sector-based data communication. Upon sensing the channel busy, SUtx determines the beam corresponding to PU's orientation. Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected beam corresponding to the strongest channel between SUtx and SUrx. We formulate a constrained optimization problem, where SUtx-SUrx link ergodic capacity is maximized, subject to average transmit power and interference constraints, and the optimization variables are sensing duration, thresholds of channel quantizer at SUrx, and transmit power levels at SUtx. Since this problem is non-convex we develop a suboptimal computationally efficient iterative algorithm to find the solution. Our numerical results quantify the capacity improvement provided by the ESPAR antenna and demonstrate that our CR system yields lower outage and symbol error probabilities, compared with a CR system that its SUtx has an omni-directional antenna.

eess.SP↗

arXiv2019

Integrated Optimization of Power Split, Engine Thermal Management, and Cabin Heating for Hybrid Electric Vehicles

Cabin heating demand and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs), especially in congested traffic conditions. This paper presents an integrated power and thermal management (i-PTM) scheme for the optimization of power split, engine thermal management, and cabin heating of HEVs. A control-oriented model of a power split HEV, including power and thermal loops, is developed and experimentally validated against data collected from a 2017 Toyota Prius HEV. Based on this model, the dynamic programming (DP) technique is adopted to derive a bench-mark for minimal fuel consumption, using 2-dimensional (power split and engine thermal management) and 3-dimensional (power split, engine thermal management, and cabin heating) formulations. Simulation results for a real-world congested driving cycle show that the engine thermal effect and the cabin heating requirement can significantly influence the optimal behavior for the power management, and substantial potential on fuel saving can be achieved by the i-PTM optimization as compared to conventional power and thermal management strategies.

eess.SY↗

arXiv2019

Sequential Optimization of Speed, Thermal Load, and Power Split in Connected HEVs

The emergence of connected and automated vehicles (CAVs) provides an unprecedented opportunity to capitalize on these technologies well beyond their original designed intents. While abundant evidence has been accumulated showing substantial fuel economy improvement benefits achieved through advanced powertrain control, the implications of the CAV operation on power and thermal management have not been fully investigated. In this paper, in order to explore the opportunities for the coordination between the onboard thermal management and the power split control, we present a sequential optimization solution for eco-driving speed trajectory planning, air conditioning (A/C) thermal load planning (eco-cooling), and powertrain control in hybrid electric CAVs to evaluate the individual as well as the collective energy savings through proactive usage of traffic data for vehicle speed prediction. Simulation results over a real-world driving cycle show that compared to a baseline non-CAV, 11.9%, 14.2%, and 18.8% energy savings can be accumulated sequentially through speed, thermal load, and power split optimizations, respectively.

eess.SY↗

arXiv2019

A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture. According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated Recurrent Units (GRU) with the attention mechanism. The former is to capture the local trend features and the latter is to capture the long temporal dependencies. Then, we design a hybrid multimodal deep learning framework (HMDLF) for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.

cs.LG↗

arXiv2018

Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

The energy efficiency of Connected and Automated Vehicles (CAVs) is significantly influenced by surrounding road users. This paper presents the evaluation of energy efficiency of CAVs in a mixed traffic interacted with human controlled vehicles. To simulate the interaction between the CAVs and the cut-in vehicles controlled by human drivers near the intersection, a lane changing model is proposed to emulate the politeness and patience characteristics of the human driver. The proposed lane changing model is then calibrated based on over 100,000 naturalistic lane changing events collected by the University of Michigan Safety Pilot Model Deployment Program. A case study on simulation of the cut-in scenario is carried out to demonstrate the human driver's lane changing sensitivity under different driving trajectories of a frontal CAV and the influence on the energy consumption of the CAV due to the cut-in vehicle is evaluated. The simulation results indicate that the fuel economy of the CAV can be substantially improved if its surrounding cut-in vehicles can be well handled.

eess.SY↗

arXiv2017

Three-Stream Convolutional Networks for Video-based Person Re-Identification

This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks. To this end, we study a number of methods for video-based person re-identification and make the following findings: 1) Max-pooling only focuses on the maximum value of a receptive field, wasting a lot of information. 2) Networks with different streams even including the one with the worst performance work better than networks with same streams, where each one has the best performance alone. 3) A full connection layer at the end of convolutional networks is not necessary. Based on these studies, we propose a new convolutional architecture termed Three-Stream Convolutional Networks (TSCN). It first uses different streams to learn different aspects of feature maps for attentive spatio-temporal fusion of video, and then merges them together to study some union features. To further utilize the feature maps, two architectures are designed by using the strategies of multi-scale and upsampling. Comparative experiments on iLIDS-VID, PRID-2011 and MARS datasets illustrate that the proposed architectures are significantly better for feature extraction than the state-of-the-art models.

cs.CV↗

arXiv2014

Quantifying the Information Leakage in Timing Side Channels in Deterministic Work-Conserving Schedulers

When multiple job processes are served by a single scheduler, the queueing delays of one process are often affected by the others, resulting in a timing side channel that leaks the arrival pattern of one process to the others. In this work, we study such a timing side channel between a regular user and a malicious attacker. Utilizing Shannon's mutual information as a measure of information leakage between the user and attacker, we analyze privacy-preserving behaviors of common work-conserving schedulers. We find that the attacker can always learn perfectly the user's arrival process in a longest-queue-first (LQF) scheduler. When the user's job arrival rate is very low (near zero), first-come-first-serve (FCFS) and round robin schedulers both completely reveal the user's arrival pattern. The near-complete information leakage in the low-rate traffic region is proven to be reduced by half in a work-conserving version of TDMA (WC-TDMA) scheduler, which turns out to be privacy-optimal in the class of deterministic-working-conserving (det-WC) schedulers, according to a universal lower bound on information leakage we derive for all det-WC schedulers.

cs.IT↗

arXiv2013

Invisible Flow Watermarks for Channels with Dependent Substitution, Deletion, and Bursty Insertion Errors

Flow watermarks efficiently link packet flows in a network in order to thwart various attacks such as stepping stones. We study the problem of designing good flow watermarks. Earlier flow watermarking schemes mostly considered substitution errors, neglecting the effects of packet insertions and deletions that commonly happen within a network. More recent schemes consider packet deletions but often at the expense of the watermark visibility. We present an invisible flow watermarking scheme capable of enduring a large number of packet losses and insertions. To maintain invisibility, our scheme uses quantization index modulation (QIM) to embed the watermark into inter-packet delays, as opposed to time intervals including many packets. As the watermark is injected within individual packets, packet losses and insertions may lead to watermark desynchronization and substitution errors. To address this issue, we add a layer of error-correction coding to our scheme. Experimental results on both synthetic and real network traces demonstrate that our scheme is robust to network jitter, packet drops and splits, while remaining invisible to an attacker.

cs.CR↗

arXiv2013

An Information Theoretic Study of Timing Side Channels in Two-user Schedulers

Timing side channels in two-user schedulers are studied. When two users share a scheduler, one user may learn the other user's behavior from patterns of service timings. We measure the information leakage of the resulting timing side channel in schedulers serving a legitimate user and a malicious attacker, using a privacy metric defined as the Shannon equivocation of the user's job density. We show that the commonly used first-come-first-serve (FCFS) scheduler provides no privacy as the attacker is able to to learn the user's job pattern completely. Furthermore, we introduce an scheduling policy, accumulate-and-serve scheduler, which services jobs from the user and attacker in batches after buffering them. The information leakage in this scheduler is mitigated at the price of service delays, and the maximum privacy is achievable when large delays are added.

cs.IT↗

arXiv2012

CensorSpoofer: Asymmetric Communication with IP Spoofing for Censorship-Resistant Web Browsing

A key challenge in censorship-resistant web browsing is being able to direct legitimate users to redirection proxies while preventing censors, posing as insiders, from discovering their addresses and blocking them. We propose a new framework for censorship-resistant web browsing called {\it CensorSpoofer} that addresses this challenge by exploiting the asymmetric nature of web browsing traffic and making use of IP spoofing. CensorSpoofer de-couples the upstream and downstream channels, using a low-bandwidth indirect channel for delivering outbound requests (URLs) and a high-bandwidth direct channel for downloading web content. The upstream channel hides the request contents using steganographic encoding within email or instant messages, whereas the downstream channel uses IP address spoofing so that the real address of the proxies is not revealed either to legitimate users or censors. We built a proof-of-concept prototype that uses encrypted VoIP for this downstream channel and demonstrated the feasibility of using the CensorSpoofer framework in a realistic environment.

cs.CR↗

arXiv2011

Website Detection Using Remote Traffic Analysis

Recent work in traffic analysis has shown that traffic patterns leaked through side channels can be used to recover important semantic information. For instance, attackers can find out which website, or which page on a website, a user is accessing simply by monitoring the packet size distribution. We show that traffic analysis is even a greater threat to privacy than previously thought by introducing a new attack that can be carried out remotely. In particular, we show that, to perform traffic analysis, adversaries do not need to directly observe the traffic patterns. Instead, they can gain sufficient information by sending probes from a far-off vantage point that exploits a queuing side channel in routers. To demonstrate the threat of such remote traffic analysis, we study a remote website detection attack that works against home broadband users. Because the remotely observed traffic patterns are more noisy than those obtained using previous schemes based on direct local traffic monitoring, we take a dynamic time warping (DTW) based approach to detecting fingerprints from the same website. As a new twist on website fingerprinting, we consider a website detection attack, where the attacker aims to find out whether a user browses a particular web site, and its privacy implications. We show experimentally that, although the success of the attack is highly variable, depending on the target site, for some sites very low error rates. We also show how such website detection can be used to deanonymize message board users.

cs.CR↗