tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

Among the LTE-A communication techniques, Device-to-Device (D2D) communication which is defined to directly route data traffic between spatially closely located mobile user equipments (UEs), holds great promise in improving energy efficiency, throughput, delay, as well as spectrum efficiency As a combination of ad-hoc and centralized communication mechanisms, D2D communication enables researchers to merge together the long-term development achievements in previously disjoint domains of ad-hoc networking and centralized networking To help researchers to have a systematic understanding of the emerging D2D communication, we provide in this paper a comprehensive survey of available D2D related research works ranging from technical papers to experimental prototypes to standard activities, and outline some open research problems which deserve further studies

Device-to-Device Communication in LTE-Advanced Networks: A Survey

国際会議参加報告:2014 IEEE International Symposium on Information Theory

We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework to simultaneously provide (1) resiliency against stragglers that may prolong computations; (2) security against Byzantine (or malicious) workers that deliberately modify the computation for their benefit; and (3) (information-theoretic) privacy of the dataset amidst possible collusion of workers. LCC, which leverages the well-known Lagrange polynomial to create computation redundancy in a novel coded form across workers, can be applied to any computation scenario in which the function of interest is an arbitrary multivariate polynomial of the input dataset, hence covering many computations of interest in machine learning. LCC significantly generalizes prior works to go beyond linear computations. It also enables secure and private computing in distributed settings, improving the computation and communication efficiency of the state-of-the-art. Furthermore, we prove the optimality of LCC by showing that it achieves the optimal tradeoff between resiliency, security, and privacy, i.e., in terms of tolerating the maximum number of stragglers and adversaries, and providing data privacy against the maximum number of colluding workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the conventional uncoded implementation of distributed least-squares linear regression by up to $13.43\times$, and also achieves a $2.36\times$-$12.65\times$ speedup over the state-of-the-art straggler mitigation strategies.

Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks

In this paper, we define multicast for an ad hoc network through nodes' mobility as MotionCast and study the delay and capacity tradeoffs for it. Assuming nodes move according to an independently and identically distributed (i.i.d.) pattern and each desires to send packets to k distinctive destinations, we compare the delay and capacity in two transmission protocols: one uses 2-hop relay algorithm without redundancy; the other adopts the scheme of redundant packets transmissions to improve delay while at the expense of the capacity. In addition, we obtain the maximum capacity and the minimum delay under certain constraints. We find that the per-node delay and capacity for the 2-hop algorithm without redundancy are Θ(1/k) and Θ(n log k), respectively; for the 2-hop algorithm with redundancy, they are Ω(1/k√(n log k)) and Θ(√(n log k)), respectively. The capacity of the 2-hop relay algorithm without redundancy is better than the multicast capacity of static networks developed by Li [IEEE/ACM Trans. Netw., vol. 17, no. 3, pp. 950-961, Jun. 2009] as long as k is strictly less than n in an order sense, while when k=Θ(n), mobility does not increase capacity anymore. The ratio between delay and capacity satisfies delay/rate ≥ O(nk log k) for these two protocols, which are both smaller than that of directly extending the fundamental tradeoff for unicast established by Neely and Modiano [IEEE Trans. Inf. Theory, vol. 51, no. 6, pp. 1917-1937, Jun. 2005] to multicast, i.e., delay/rate ≥ O(n k2). More importantly, we have proved that the fundamental delay-capacity tradeoff ratio for multicast is delay/rate ≥ O(n log k), which would guide us to design better routing schemes for multicast.

/pdf/delay-and-capacity-tradeoff-analysis-for-motioncast-4n4vvoy95n.pdf

Delay and capacity tradeoff analysis for motioncast

A secret sharing scheme is a method to store information securely and reliably Particularly, in a threshold secret sharing scheme , a secret is encoded into $n$ shares, such that any set of at least $t_{1}$ shares suffice to decode the secret, and any set of at most $t_{2} shares reveal no information about the secret Assuming that each party holds a share and a user wishes to decode the secret by receiving information from a set of parties; the question we study is how to minimize the amount of communication between the user and the parties We show that the necessary amount of communication, termed “decoding bandwidth”, decreases as the number of parties that participate in decoding increases We prove a tight lower bound on the decoding bandwidth, and construct secret sharing schemes achieving the bound Particularly, we design a scheme that achieves the optimal decoding bandwidth when $d$ parties participate in decoding, universally for all $t_{1} \le d \le n$  The scheme is based on a generalization of Shamir’s secret sharing scheme and preserves its simplicity and efficiency In addition, we consider the setting of secure distributed storage where the proposed communication efficient secret sharing schemes not only improve decoding bandwidth but further improve disk access complexity during decoding

/pdf/communication-efficient-secret-sharing-4o0m86qrud.pdf

Communication Efficient Secret Sharing

There has been recent interest within the networking research community to understand how performance scales in cognitive networks with overlapping n primary nodes and m secondary nodes. Two important metrics, i.e., throughput and delay, are studied in this paper. We first propose a simple and extendable decision model, i.e., the hybrid protocol model, for the secondary nodes to exploit spatial gap among primary transmissions for frequency reuse. Then a framework for general cognitive networks is established based on the hybrid protocol model to analyze the occurrence of transmission opportunities for secondary nodes. We show that in the case that the transmission range of the secondary network is smaller than that of the primary network in order, as long as the primary network operates in a generalized round-robin TDMA fashion, the hybrid protocol model suffice to guide the secondary network to achieve the same throughput and delay scaling as a standalone network, without harming the transmissions of the primary network. Our approach is general in the sense that we only make a few weak assumptions on both networks, and therefore obtains a wide variety of results. We show secondary networks can obtain the same order of throughput and delay as standalone networks when primary networks are classic static networks, networks with random walk mobility, hybrid networks, multicast networks, hierarchically cooperative networks or clustered networks. Our work presents a relatively complete picture of the performance scaling of cognitive networks and provides fundamental insight on the design of them.

/pdf/throughput-and-delay-scaling-of-general-cognitive-networks-3bry9gg5r0.pdf

Throughput and delay scaling of general cognitive networks

There has been recent interest within the networking research community to understand how performance scales in cognitive networks with overlapping n primary nodes and m secondary nodes. Two important metrics, i.e., throughput and delay, are studied in this paper. We first propose a simple and extendable decision model, i.e., the hybrid protocol model, for the secondary nodes to exploit spatial gap among primary transmissions for frequency reuse. Then, a framework for general cognitive networks is established based on the hybrid protocol model to analyze the occurrence of transmission opportunities for secondary nodes. We show that if the primary network operates in a generalized TDMA fashion, or employs a routing scheme such that traffic flows choose relays independently, then the hybrid protocol model suffices to guide the secondary network to achieve the same throughput and delay scaling as a standalone network without harming the performance of the primary network, as long as the secondary transmission range is smaller than the primary range in order. Our approach is general in the sense that we only make a few weak assumptions on both networks, and therefore it obtains a wide variety of results. We show secondary networks can obtain the same order of throughput and delay as standalone networks when primary networks are classic static networks, networks with random walk mobility, hybrid networks, multicast networks, CSMA networks, networks with general mobility, or clustered networks. Our work presents a relatively complete picture of the performance scaling of cognitive networks and provides fundamental insight on the design of them.

Capacity scaling of general cognitive networks

We study the throughput capacity of mobile wireless ad hoc networks with infrastructure support. Mobility and infrastructure support independently have been shown to be effective ways to improve capacity, but few work has analyzed the impact of their combination. In our work we consider an ad hoc network with n users and k base stations. All base stations are wired to each other with bandwidth c(n). We adopt a general mobility model where users move with arbitrary patterns within a bounded distance around their home-points, and let the area of the network scales as f^2(n). We show that for different parameters, mobility can be divided into strong, weak and trivial regimes. The per-node capacity is Θ(1/f(n))+Θ(min(k^2 c/n, k/n)) under strong mobility, and is Θ(min(k^2 c/n, k/n)) in the two latter cases. We also discuss optimal communication schemes and system parameters in each regime. Our study provides fundamental insight on the understanding and design of wireless ad hoc network.

/pdf/capacity-scaling-in-mobile-wireless-ad-hoc-network-with-5eyqgeh95h.pdf

Wentao Huang

Papers

Delay and capacity tradeoff analysis for motioncast

Communication Efficient Secret Sharing

Throughput and delay scaling of general cognitive networks

Capacity scaling of general cognitive networks

Capacity Scaling in Mobile Wireless Ad Hoc Network with Infrastructure Support