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Andrea Goldsmith

Bio: Andrea Goldsmith is an academic researcher from Princeton University. The author has contributed to research in topics: Communication channel & Fading. The author has an hindex of 97, co-authored 793 publications receiving 61845 citations. Previous affiliations of Andrea Goldsmith include California Institute of Technology & Harvard University.


Papers
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TL;DR: This work considers the discrete, time-varying broadcast channel with memory under the assumption that the channel states belong to a set of finite cardinality and defines the physically degraded finite-state broadcast channel for which the capacity region is derived.
Abstract: We consider the discrete, time-varying broadcast channel with memory under the assumption that the channel states belong to a set of finite cardinality. We first define the physically degraded finite-state broadcast channel for which we derive the capacity region. We then define the stochastically degraded finite-state broadcast channel and derive the capacity region for this scenario as well. In both scenarios we consider the non-indecomposable finite-state channel as well as the indecomposable one.
Proceedings ArticleDOI
09 May 2022
TL;DR: An alternative framework for the statistical characterization and performance evaluation of the FTR fading model is determined, based on the fact that the F TR fading distribution can be described as an underlying Rician Shadowed (RS) distribution with continuously varying parameter K r (ratio of specular to diffuse components).
Abstract: We present a composite wireless fading model encompassing multipath fading and shadowing based on fluctuating two-ray (FTR) fading and inverse gamma (IG) shadowing. We first determine an alternative framework for the statistical characterization and performance evaluation of the FTR fading model, which is based on the fact that the FTR fading distribution can be described as an underlying Rician Shadowed (RS) distribution with continuously varying parameter $K_{T}$ (ratio of specular to diffuse components). We demonstrate that this new formulation permits to obtain a closed-form expression of the generalized moment generating function (GMGF) of the FTR model, from which the PDF and CDF of the composite IG/FTR model can be obtained in closed-form. The exact and asymptotic outage probability of the IG/FTR model are analyzed and verified by Monte Carlo simulations.
Proceedings ArticleDOI
01 Dec 2011
TL;DR: The connection between this version of the hats problem and hypercube graph theory is explored, and the proposed sufficient condition on the joint distribution of the hat colors guarantees the optimality of a binary sum coding strategy.
Abstract: We propose a large class of decentralized control problems with non-classical information structure for which a coding strategy is optimal. This class is a generalized version of the hats problem with statistically dependent hat colors where implicit communication via action is allowed. We propose a sufficient condition on the joint distribution of the hat colors which guarantees the optimality of a binary sum coding strategy. We explore the connection between this version of the hats problem and hypercube graph theory, and use that to show that verifying our proposed sufficient condition is computationally tractable.
Posted Content
TL;DR: In this paper, the authors considered a parameter-server setting in which the worker is constrained to communicate information to the server using only $R$ bits per dimension, and the resulting polynomial complexity source coding schemes were used to design distributed optimization algorithms with convergence rates matching the minimax optimal lower bounds for Smooth and Strongly-Convex objectives with access to an Exact Gradient oracle.
Abstract: The communication cost of distributed optimization algorithms is a major bottleneck in their scalability. This work considers a parameter-server setting in which the worker is constrained to communicate information to the server using only $R$ bits per dimension. We show that $\mathbf{democratic}$ $\mathbf{embeddings}$ from random matrix theory are significantly useful for designing efficient and optimal vector quantizers that respect this bit budget. The resulting polynomial complexity source coding schemes are used to design distributed optimization algorithms with convergence rates matching the minimax optimal lower bounds for (i) Smooth and Strongly-Convex objectives with access to an Exact Gradient oracle, as well as (ii) General Convex and Non-Smooth objectives with access to a Noisy Subgradient oracle. We further propose a relaxation of this coding scheme which is nearly minimax optimal. Numerical simulations validate our theoretical claims.
Proceedings ArticleDOI
11 Aug 2014
TL;DR: It is found that the mappings required for the optimal strategies and the way they are used vary significantly with the channel cost of transmission.
Abstract: The optimal strategy for dynamic joint source-channel coding with feedback was recently shown to be a simple mapping between the source symbols and channel inputs, where the mapping only depends on the decoder's posterior belief about the source. In this work, we derive the optimal joint source-channel coding strategies for two specific channels - binary erasure channels and Z-channels. It is found that the mappings required for the optimal strategies and the way they are used vary significantly with the channel cost of transmission.

Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Book
01 Jan 2005

9,038 citations

Journal ArticleDOI
TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
Abstract: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is /spl Theta/(W/spl radic/An) bit-meters per second. Thus even under optimal circumstances, the throughput is only /spl Theta/(W//spl radic/n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.

9,008 citations

Proceedings Article
01 Jan 2005
TL;DR: This book aims to provide a chronology of key events and individuals involved in the development of microelectronics technology over the past 50 years and some of the individuals involved have been identified and named.
Abstract: Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

7,826 citations

Journal ArticleDOI
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Abstract: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)

6,967 citations