arXiv subjects

Huaiyu Dai

Publications and source records attributed to Huaiyu Dai.

At least 109 records · Page 6Linked to original sources

arXiv2019

Reinforcement Learning for Interference Avoidance Game in RF-Powered Backscatter Communications

RF-powered backscatter communication is a promising new technology that can be deployed for battery-free applications such as internet of things (IoT) and wireless sensor networks (WSN). However, since this kind of communication is based on the ambient RF signals and battery-free devices, they are vulnerable to interference and jamming. In this paper, we model the interaction between the user and a smart interferer in an ambient backscatter communication network as a game. We design the utility functions of both the user and interferer in which the backscattering time is taken into the account. The convexity of both sub-game optimization problems is proved and the closed-form expression for the equilibrium of the Stackelberg game is obtained. Due to lack of information about the system SNR and transmission strategy of the interferer, the optimal strategy is obtained using the Q-learning algorithm in a dynamic iterative manner. We further introduce hotbooting Q-learning as an effective approach to expedite the convergence of the traditional Q-learning. Simulation results show that our approach can obtain considerable performance improvement in comparison to random and fixed backscattering time transmission strategies and improves the convergence speed of Q-Learning by about 31%.

eess.SP↗

arXiv2019

Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wide range of relevant areas such as computer vision, health care, and financial market analysis. To accommodate the large volume of data, there is a surge of interest in the design of distributed machine learning, among which stochastic gradient descent (SGD) is one of the mostly adopted methods. Nonetheless, distributed machine learning methods may be vulnerable to Byzantine attack, in which the adversary can deliberately share falsified information to disrupt the intended machine learning procedures. Therefore, two asynchronous Byzantine tolerant SGD algorithms are proposed in this work, in which the honest collaborative workers are assumed to store the model parameters derived from their own local data and use them as the ground truth. The proposed algorithms can deal with an arbitrary number of Byzantine attackers and are provably convergent. Simulation results based on a real-world dataset are presented to verify the theoretical results and demonstrate the effectiveness of the proposed algorithms.

cs.LG↗

arXiv2019

Energy Efficiency of RSMA and NOMA in Cellular-Connected mmWave UAV Networks

Cellular-connected unmanned aerial vehicles (UAVs) are recently getting significant attention due to various practical use cases, e.g., surveillance, data gathering, purchase delivery, among other applications. Since UAVs are low power nodes, energy and spectral efficient communication is of paramount importance. To that end, multiple access (MA) schemes can play an important role in achieving high energy efficiency and spectral efficiency. In this work, we introduce rate-splitting MA (RSMA) and non-orthogonal MA (NOMA) schemes in a cellular-connected UAV network. In particular, we investigate the energy efficiency of the RSMA and NOMA schemes in a millimeter wave (mmWave) downlink transmission scenario. Furthermore, we optimize precoding vectors of both the schemes by explicitly taking into account the 3GPP antenna propagation patterns. The numerical results for this realistic transmission scheme indicate that RSMA is superior to NOMA in terms of overall energy efficiency.

eess.SP↗

arXiv2019

SLNR Based Precoding for One-Bit Quantized Massive MIMO in mmWave Communications

Massive multiple-input multiple-output (MIMO) is a key technology for 5G wireless communications with a promise of significant capacity increase. The use of low-resolution data converters is crucial for massive MIMO to make the overall transmission as cost- and energy-efficient as possible. In this work, we consider a downlink millimeter-wave (mmWave) transmission scenario, where multiple users are served simultaneously by massive MIMO with one-bit digital-to-analog (D/A) converters. In particular, we propose a novel precoder design based on signal-to-leakage-plus-noise ratio (SLNR), which minimizes energy leakage into undesired users while taking into account impairments due to nonlinear one-bit quantization. We show that well-known regularized zero-forcing (RZF) precoder is a particular version of the proposed SLNR-based precoder, which is obtained when quantization impairments are totally ignored. Numerical results underscore significant performance improvements along with the proposed SLNR-based precoder as compared to either RZF or zero-forcing (ZF) precoders.

cs.IT↗

arXiv2019

Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

cs.NI↗

arXiv2019

Smart Information Spreading for Opinion Maximization in Social Networks

The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized learning algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.

cs.SI↗

arXiv2018

Enhancing Physical Layer Security for NOMA Transmission in mmWave Drone Networks

Physical layer security (PLS) is critically important for emerging wireless communication networks to maintain the confidentiality of the information of legitimate users. In this paper, we investigate enhancing PLS in an unmanned aerial vehicle (UAV) based communication network where a UAV acting as an aerial base station (BS) provides coverage in a densely packed user area (such as a stadium or a concert area). In particular, non-orthogonal multiple access (NOMA) together with highly-directional multi-antenna transmission techniques in mmWave frequency bands are utilized for improving spectral efficiency. In order to achieve PLS against potential eavesdropper attacks, we introduce a protected zone around the user region. However, limited resource availability refrain protected zone being extended to cover the entire eavesdropper region. Hence, we propose an approach to optimize the protected zone shape (for fixed area) at each UAV-BS hovering altitude. The associated secrecy performance is evaluated considering the secrecy outage and sum secrecy rates. Numerical results reveal the importance of protected zone shape optimization at each altitude to maximize NOMA secrecy rates.

cs.IT↗

arXiv2018

A Two-Stage Auction Mechanism for Cloud Resource Allocation

With the recent growth in the size of cloud computing business, handling the interactions between customers and cloud providers has become more challenging. Auction theory has been proposed to model these interactions due to its simplicity and a good match with real-world scenarios. In this paper, we consider cloud of clouds networks (CCNs) with different types of servers along with customers with heterogeneous demands. For each CCN, a CCN manager is designated to handle the cloud resources. A comprehensive framework is introduced in which the process of resource gathering and allocation is addressed via two stages, where the first stage models the interactions between customers and CCN managers, and the second stage examines the interactions between CCN managers and private cloud providers (CPs). For the first stage, an options-based sequential auction (OBSA) is adapted to the examined market, which is capable of providing truthfulness as the dominant strategy and resolving the entrance time problem. An analytical foundation for OBSAs is presented and multiple performance metrics are derived. For the second stage, two parallel markets are assumed: flat-price and auction-based market. A theoretical framework for market analysis is provided and the bidding behavior of CCN managers is described.

cs.DC↗

arXiv2018

Infection Analysis on Irregular Networks through Graph Signal Processing

In a networked system, functionality can be seriously endangered when nodes are infected, due to internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes' states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure design. This analysis is challenging due to irregular network structure, heterogeneous epidemic spreading, and noisy observations. This paper treats a network snapshot as a graph signal, and develops effective approaches for infection analysis based on graph signal processing. For the macro (network-level) analysis aiming to distinguish an epidemic from random failures, 1) multiple detection metrics are defined based on the graph Fourier transform (GFT) and neighborhood characteristics of the graph signal; 2) a new class of graph wavelets, distance-based graph wavelets (DBGWs), are developed; and 3) a machine learning-based framework is designed employing either the GFT spectrum or the graph wavelet coefficients as features for infection analysis. DBGWs also enable the micro (node-level) infection analysis, through which the performance of epidemic countermeasures can be improved. Extensive simulations are conducted to demonstrate the effectiveness of all the proposed algorithms in various network settings.

cs.SI↗

arXiv2018

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: RAN, core network, and caching. We also present a general overview of 5G cellular networks composed of software defined network (SDN), network function virtualization (NFV), caching, and mobile edge computing (MEC) capable of meeting latency and other 5G requirements.

cs.NI↗

arXiv2018

Age-Optimal Trajectory Planning for UAV-Assisted Data Collection

Unmanned aerial vehicle (UAV)-aided data collection is a new and promising application in many practical scenarios. In this work, we study the age-optimal trajectory planning problem in UAV-enabled wireless sensor networks, where a UAV is dispatched to collect data from the ground sensor nodes (SNs). The age of information (AoI) collected from each SN is characterized by the data uploading time and the time elapsed since the UAV leaves this SN. We attempt to design two age-optimal trajectories, referred to as the Max-AoI-optimal and Ave-AoI-optimal trajectories, respectively. The Max-AoI-optimal trajectory planning is to minimize the age of the `oldest' sensed information among the SNs. The Ave-AoI-optimal trajectory planning is to minimize the average AoI of all the SNs. Then, we show that each age-optimal flight trajectory corresponds to a shortest Hamiltonian path in the wireless sensor network where the distance between any two SNs represents the amount of inter-visit time. The dynamic programming (DP) method and genetic algorithm (GA) are adopted to find the two different age-optimal trajectories. Simulation results validate the effectiveness of the proposed methods, and show how the UAV's trajectory is affected by the two AoI metrics.

cs.IT↗

arXiv2018

A Computation Offloading Incentive Mechanism with Delay and Cost Constraints under 5G Satellite-ground IoV architecture

The 5G Internet of Vehicles has become a new paradigm alongside the growing popularity and variety of computation-intensive applications with high requirements for computational resources and analysis capabilities. Existing network architectures and resource management mechanisms may not sufficiently guarantee satisfactory Quality of Experience and network efficiency, mainly suffering from coverage limitation of Road Side Units, insufficient resources, and unsatisfactory computational capabilities of onboard equipment, frequently changing network topology, and ineffective resource management schemes. To meet the demands of such applications, in this article, we first propose a novel architecture by integrating the satellite network with 5G cloud-enabled Internet of Vehicles to efficiently support seamless coverage and global resource management. A incentive mechanism based joint optimization problem of opportunistic computation offloading under delay and cost constraints is established under the aforementioned framework, in which a vehicular user can either significantly reduce the application completion time by offloading workloads to several nearby vehicles through opportunistic vehicle-to-vehicle channels while effectively controlling the cost or protect its own profit by providing compensated computing service. As the optimization problem is non-convex and NP-hard, simulated annealing based on the Markov Chain Monte Carlo as well as the metropolis algorithm is applied to solve the optimization problem, which can efficaciously obtain both high-quality and cost-effective approximations of global optimal solutions. The effectiveness of the proposed mechanism is corroborated through simulation results.

cs.NI↗

arXiv2016

Relay Selection for Cooperative NOMA

This letter studies the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA). In particular, a two-stage RS strategy is proposed, and analytical results are developed to demonstrate that this two-stage strategy can achieve the minimal outage probability among all possible RS schemes, and realize the maximal diversity gain. The provided simulation results show that cooperative NOMA with this two-stage RS scheme outperforms that with the conventional max-min approach, and can also yield a significant performance gain over orthogonal multiple access.

cs.IT↗

arXiv2015

Virtual-MIMO-Boosted Information Propagation on Highways

In vehicular communications, traffic-related information should be spread over the network as quickly as possible to maintain a safer transportation system. This motivates us to develop more efficient information propagation schemes. In this paper, we propose a novel virtual-MIMO-enabled information dissemination scheme, in which the vehicles opportunistically form virtual antenna arrays to boost the transmission range and therefore accelerate information propagation along the highway. We model the information propagation process as a renewal reward process and investigate in detail the \emph{Information Propagation Speed} (IPS) of the proposed scheme. The corresponding closed-form IPS is derived, which shows that the IPS increases cubically with the vehicle density but will ultimately converge to a constant upper bound. Moreover, increased mobility also facilitates the information spreading by offering more communication opportunities. However, the limited network density essentially determines the bottleneck in information spreading. Extensive simulations are carried out to verify our analysis. We also show that the proposed scheme exhibits a significant IPS gain over its conventional counterpart.

cs.IT↗

arXiv2014

Mobile Conductance in Sparse Networks and Mobility-Connectivity Tradeoff

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.

cs.NI↗

arXiv2014

Gossip-based Information Spreading in Mobile Networks

Mobile networks receive increasing research interest recently due to their increasingly wide applications in various areas; mobile ad hoc networks (MANET) and Vehicular ad hoc networks (VANET) are two prominent examples. Mobility introduces challenges as well as opportunities: it is known to improve the network throughput as shown in [1]. In this paper, we analyze the effect of mobility on the information spreading based on gossip algorithms. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we explore the mobile conductances of several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.

cs.SI↗

arXiv2014

On the Capacity Region of Cognitive Multiple Access over White Space Channels

Opportunistically sharing the white spaces, or the temporarily unoccupied spectrum licensed to the primary user (PU), is a practical way to improve the spectrum utilization. In this paper, we consider the fundamental problem of rate regions achievable for multiple secondary users (SUs) which send their information to a common receiver over such a white space channel. In particular, the PU activities are treated as on/off side information, which can be obtained causally or non-causally by the SUs. The system is then modeled as a multi-switch channel and its achievable rate regions are characterized in some scenarios. Explicit forms of outer and inner bounds of the rate regions are derived by assuming additional side information, and they are shown to be tight in some special cases. An optimal rate and power allocation scheme that maximizes the sum rate is also proposed. The numerical results reveal the impacts of side information, channel correlation and PU activity on the achievable rates, and also verify the effectiveness of our rate and power allocation scheme. Our work may shed some light on the fundamental limit and design tradeoffs in practical cognitive radio systems.

cs.IT↗

arXiv2014

Mobile Conductance and Gossip-based Information Spreading in Mobile Networks

In this paper, we propose a general analytical framework for information spreading in mobile networks based on a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. We derive a general result for the information spreading time in mobile networks in terms of this new metric, and instantiate it through several popular mobility models. Large scale network simulation is conducted to verify our analysis.

cs.SI↗