Moe Z. Win

Massachusetts Institute of Technology, Cambridge, Massachusetts, United States

Are you Moe Z. Win?

Claim your profile

Publications (411)425.62 Total impact


  • No preview · Article · Dec 2016 · IEEE Transactions on Signal Processing

  • No preview · Article · Dec 2016 · IEEE Transactions on Communications

  • No preview · Conference Paper · May 2016

  • No preview · Conference Paper · Mar 2016

  • No preview · Article · Mar 2016 · IEEE Signal Processing Magazine

  • No preview · Article · Jan 2016 · IEEE Transactions on Signal Processing
  • Source
    [Show description] [Hide description]
    DESCRIPTION: Resource including power and bandwidth allocation strategies for wireless cooperative localization networks
    Full-text · Research · Oct 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.
    No preview · Article · Oct 2015
  • Wenhan Dai · Yuan Shen · Moe Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the design of joint inference and control algorithms. In this paper, we develop efficient network navigation algorithms with optimized energy allocation. In particular, we first determine the confidence region for lzocation inference based on Fisher information analysis, and then design robust energy allocation strategies that minimize the position errors of the agents within the confidence region. Based on these strategies, both centralized and distributed energy-efficient network navigation algorithms are developed. Simulation results show that the proposed algorithms significantly reduce the position errors compared to the algorithms with uniform or non-robust power control.
    No preview · Article · Jul 2015 · IEEE Journal on Selected Areas in Communications
  • Youngmin Jeong · Hyundong Shin · Moe Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: The H-transforms are integral transforms that involve Fox's H-functions as kernels. A large variety of integral transforms can be put into particular forms of the H-transform since H-functions subsume most of the known special functions including Meijer's G-functions. In this paper, we embody the H-transform theory into a unifying framework for modeling and analysis in wireless communication. First, we systematize the use of elementary identities and properties of the H-transform by introducing operations on parameter sequences of H-functions. We then put forth H-fading and degree-2 irregular H-fading to model radio propagation under composite, specular, and/or inhomogeneous conditions. The H-fading describes composite effects of multipath fading and shadowing as a single H-variate, including most of typical models such as Rayleigh, Nakagami-m, Weibull, α-μ, N∗ Nakagami-m, (generalized) K-fading, and Weibull/gamma fading as its special cases. As a new class of H-variates (called the degree-ζ irregular H-variate), the degree-2 irregular H-fading characterizes specular and/or inhomogeneous radio propagation in which the multipath component consists of a strong specularly reflected or line-of-sight (LOS) wave as well as unequal-power or correlated in-phase and quadrature scattered waves. This fading includes a variety of typical models such as Rician, Nakagami-q, κ-μ, η-μ, Rician/LOS gamma, and κ-μ/LOS gamma fading as its special cases. Finally, we develop a unifying H-transform analysis for the amount of fading, error probability, channel capacity, and error exponent in wireless communication using the new systematic language of transcendental H-functions. By virtue of two essential operations - called Mellin and convolution operations - involved in the Mellin transform and Mellin convolution of two H-functions, the H-transforms for these performance measures culminate in H-functions. Using the algebraic asymptotic expansions of the H-transform, we further analyze the error probability and capacity at high and low signal-to-noise ratios in a unified fashion.
    No preview · Article · Jul 2015 · IEEE Transactions on Information Theory
  • [Show abstract] [Hide abstract]
    ABSTRACT: Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.
    No preview · Article · Jul 2015 · IEEE Journal on Selected Areas in Communications
  • W. Dai · Y. Shen · M.Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments via spatiotemporal cooperation. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the joint design of inference and control algorithms. In this paper, we develop efficient algorithms for network navigation with power control. In particular, we first determine the confidence region for location inference based on Fisher information analysis, and then design robust power control that minimizes the location inference errors of the agents within the confidence region. Both centralized and distributed algorithms are developed for energy-efficient network navigation. Simulation results show that the proposed algorithms significantly reduce the location inference errors compared to those without power control.
    No preview · Article · Jun 2015
  • W. Dai · Y. Shen · M.Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Wireless network localization (WNL) is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. The localization performance of a node in WNL is determined by the allocation of transmit resources among its neighboring nodes. To achieve the best localization performance, we develop a computational geometry framework for optimal resource allocation in WNL. We first determine an affine map that transforms each resource allocation strategy into a point in 3-D Euclidian space. By exploiting geometric properties of these image points, we prove the sparsity property of the optimal resource allocation vector, i.e., the optimal localization performance can be achieved by allocating resources to only a small subset of neighboring nodes. Moreover, these geometric properties enable the reduction of the search space for optimal solutions, based on which we design efficient resource allocation strategies. Numerical results show that the proposed strategies can achieve significant improvements in both localization performance and computation efficiency. Our approach provides a new methodology for resource allocation in network localization, yielding exact optimal solutions rather than -approximate solutions.
    No preview · Article · Jun 2015
  • Source
    Alberto Rabbachin · Andrea Conti · Moe Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Wireless secrecy is essential for communication confidentiality, health privacy, public safety, information superiority, and economic advantage in the modern information society. Contemporary security systems are based on cryptographic primitives and can be complemented by techniques that exploit the intrinsic properties of a wireless environment. This paper develops a foundation for design and analysis of wireless networks with secrecy provided by intrinsic properties such as node spatial distribution, wireless propagation medium, and aggregate network interference. We further propose strategies that mitigate eavesdropping capabilities, and we quantify their benefits in terms of network secrecy metrics. This research provides insights into the essence of wireless network intrinsic secrecy and offers a new perspective on the role of network interference in communication confidentiality.
    Full-text · Article · Jun 2015
  • Wenhan Dai · Yuan Shen · Moe Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Device-to-device (D2D) communication in cellular networks is a promising concept that permits cooperation among mobile devices not only to increase data throughput but also to enhance localization services. In those networks, the allocation of transmitting power plays a critical role in determining network lifetime and localization accuracy. Meanwhile, it is a challenging task for implementation in cooperative D2D networks, since each device has only imperfect estimates of local network parameters in distributed settings. In this paper, we establish an optimization framework for robust power allocation in cooperative wireless network localization, and develop distributed power allocation strategies. In particular, we decompose the power allocation problem into infrastructure and cooperation phases, show the sparsity property of the optimal power allocation, and develop efficient power allocation strategies. Simulation results show that these strategies can achieve significant performance improvement in localization accuracy compared to the uniform strategies.
    No preview · Article · Jun 2015 · IEEE Journal on Selected Areas in Communications
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we develop a cooperative IMU/radio-location-based navigation system, where each node tracks the location not only based on its own measurements, but also via collaboration with neighbor nodes. The key problem is to design a nonlinear filter to fuse IMU and radiolocation information. We apply the Rao-Blackwellization method by using a particle filter and parallel Kalman filters for the estimation of orientation and other states (i.e., position, velocity, etc.), respectively. The proposed method significantly outperforms the extended Kalman filter (EKF) in the set of simulations here.
    Preview · Article · Jun 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: Communications in the ether are highly susceptible to eavesdropping due to the broadcast nature of the wireless medium. To improve communication confidentiality in wireless environments, research efforts have been made to complement cryptography with physical layer security. A recent view of the role of interference, especially in multi-tier wireless networks, suggested that interference engineering can increase the level of communication confidentiality. The design of interference engineering strategies (IESs) requires a thorough characterization of concurrent effects of wireless emissions on legitimate and eavesdropping receivers. This article advocates IESs for achieving a new level of communication confidentiality in multi-tier wireless networks (namely multi-tier network secrecy) with different degrees of coordination among the tiers. Insights on how IES benefits wireless network secrecy are provided, guiding the design of such strategies for a new level of communication confidentiality.
    No preview · Article · Jun 2015 · IEEE Communications Magazine
  • [Show abstract] [Hide abstract]
    ABSTRACT: Location awareness is becoming essential for emerging wireless applications where most network activities require the location information of network nodes, e.g., routing between nodes in ad-hoc sensor networks, positioning vehicles on the road, or tracking targets in underwater acoustic sensor networks. In particular, cooperation among nodes is highly beneficial for the localization accuracy and coverage in harsh environments. In this paper, we study least square (LS) cooperative localization in the presence of arbitrary non-line-of-sight (NLOS) ranging bias. To develop the network position error bound (PEB), we first derive the Fisher information matrix (FIM) for a general NLOS bias model and show that a Gaussian bias due to NLOS effects is the worst case that produces the extremal FIM, whereas a constant bias or equivalently full line-of-sight is the best situation leading to the largest FIM in a sense of Löwner partial ordering. We then analyze the asymptotic performance, such as uniform convergence, consistency, and efficiency, of LS cooperative localization to quantify the deviations of localization accuracy for LS, squared-range LS, and squared-range weighted LS solutions from the fundamental limit (i.e., Cramér–Rao lower bound) due to their practical tractability. We also propose a simple distributed algorithm for LS cooperative localization by integrating squared-range relaxation into Gaussian variational message passing on the localization network. To account for stochastic natures of node locations and populations, we further characterize the network PEB for Gilbert's disk localization network, where anchors and/or agents are randomly distributed in the network according to point processes.
    No preview · Article · Apr 2015 · IEEE Transactions on Vehicular Technology
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sensor radar networks enable important new applications based on accurate localization. They rely on the quality of range measurements, which serve as observations for inferring a target location. In harsh propagation environments (e.g., indoors), such observations can be nonrepresentative of the target due to noise, multipath, clutter, and non-line-of-sight conditions leading to target misdetection, false-alarm events, and inaccurate localization. These conditions can be mitigated by selecting and processing a subset of representative observations. We introduce blind techniques for the selection of representative observations gathered by sensor radars operating in harsh environments. A methodology for the design and analysis of sensor radar networks is developed, taking into account the aforementioned impairments and observation selection. Results are obtained for noncoherent ultra-wideband sensor radars in a typical indoor environment (with obstructions, multipath, and clutter) to enable a clear understanding of how observation selection improves the localization accuracy.
    No preview · Article · Apr 2015 · IEEE Transactions on Vehicular Technology
  • Source
    Kaiqing Zhang · Hong Hu · Wenhan Dai · Yuan Shen · Moe Z. Win
    [Show abstract] [Hide abstract]
    ABSTRACT: Increasing sources of sensor measurements and prior knowledge have become available for indoor localization on smartphones. How to effectively utilize these sources for enhancing localization accuracy is an important yet challenging problem. In this paper, we present an area state-aided localization algorithm that exploits various sources of information. Specifically, we introduce the concept of area state to indicate the area where the user is on an indoor map. The position of the user is then estimated using inertial measurement unit (IMU) measurements with the aid of area states. The area states are in turn updated based on the position estimates. To avoid accumulated errors of IMU measurements, our algorithm uses WiFi received signal strength indicator (RSSI) to indicate the vicinity of the user to the routers. The experiment results show that our system can achieve satisfactory localization accuracy in a typical indoor environment.
    Preview · Article · Mar 2015

Publication Stats

21k Citations
425.62 Total Impact Points

Institutions

  • 2003-2015
    • Massachusetts Institute of Technology
      • Laboratory for Information and Decision Systems
      Cambridge, Massachusetts, United States
  • 2001-2013
    • University of Bologna
      • "Guglielmo Marconi" Department of Electrical, Electronic and Information Engineering DEI
      Bolonia, Emilia-Romagna, Italy
    • University of Rome Tor Vergata
      • Dipartimento di Ingegneria Elettronica
      Roma, Latium, Italy
  • 2009-2011
    • The Chinese University of Hong Kong
      • Department of Information Engineering
      Hong Kong, Hong Kong
  • 2008-2011
    • Kyung Hee University
      • Electronics and Radio Engineering Division
      Sŏul, Seoul, South Korea
    • University of Ferrara
      • Department of Engineering
      Ferrare, Emilia-Romagna, Italy
  • 2007
    • Aristotle University of Thessaloniki
      Saloníki, Central Macedonia, Greece
    • University of Oulu
      • Centre for Wireless Communications (CWC)
      Uleoborg, Oulu, Finland
  • 2006
    • University of Florence
      Florens, Tuscany, Italy
  • 2004-2006
    • Mitsubishi Electric Research Laboratories
      Cambridge, Massachusetts, United States
    • University of Texas at Dallas
      • Department of Electrical Engineering
      Richardson, Texas, United States
  • 1998-2005
    • AT&T Labs
      Austin, Texas, United States
  • 2000-2004
    • Indian Institute of Technology Delhi
      • Department of Electrical Engineering
      New Delhi, NCT, India
  • 2002-2003
    • University of Alberta
      • Department of Electrical and Computer Engineering
      Edmonton, Alberta, Canada
    • The American University of Rome
      Roma, Latium, Italy
    • Indian Institute of Technology Ropar
      • Department of Electrical Engineering
      Rūpar, Punjab, India
  • 1997-2003
    • University of Southern California
      • Department of Electrical Engineering
      Los Angeles, California, United States
    • University of California, Los Angeles
      • Department of Electrical Engineering
      Los Angeles, California, United States
  • 1990-1995
    • California Institute of Technology
      • Jet Propulsion Laboratory
      Pasadena, California, United States