On Delay-Sensitive Healthcare Data Analytics at the Network Edge: A Deep Learning Approach

  • Al-Sakib Khan Pathan
  • Gacanin H.
  • Zubair Md. Fadlullah

As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next ...

OFDM-Autoencoder for End-to-End Learning of Communications Systems

  • Cammerer S.
  • Doerner S.
  • Felix A.
  • Hoydis J.
  • Ten Brink S.

We extend the idea of end-to-end learning of communications systems through deep neural network (NN)- based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out ...

Vehicle to Infrastructure Communications Design in Urban Hyperfractals

  • Jacquet P.
  • Popescu D.

This study exploits our innovative model called "hyperfractal" that captures the self-similarity of the urban vehicular networks as well as incorporating road-side infrastructure with its own self-similarity. The goal is to provide understanding of achievable trade-offs in operating the road-side units under the constraint of minimum routing path delay while ...

Detection of Ambient Backscatter Signals from Multiple-Antenna Tags

Ambient backscatter has been introduced as an potential technology that is effective to address communication and energy problems for battery-free devices. Most existing studies about ambient backscatter are based on the assumption of single antenna at each tag. In this paper, we show that multiple-antenna tags are practically useful and ...

Coffee exports and industrialization in Brazil

  • Barros F.
  • Ferreira A.
  • Marcondes R.
  • Prioste R.

© 2018 Informa UK Limited, trading as Taylor & Francis Group We investigate the dynamic relationship between coffee exports and machinery imports in Brazil from 1869 to 1939. Our tests reveal cointegration and bidirectional causality in the temporal sense. This evidence suggests that foreign exchange real revenues from coffee exports ...

Performance of Balanced Fairness in Computer Clusters: A Recursive Approach

  • Bonald T.
  • Comte C.
  • Mathieu F.

Understanding the performance of computer clusters is crucial for proper dimensioning. One of the main challenge is to take into account the complex interactions between computers that process jobs in parallel. In particular, a job can generally not be processed by any computer of the cluster due to various constraints ...

Handling Delay in 5G Ethernet Mobile Fronthaul Network

  • Chen D.
  • Raimena Veisllari
  • Steinar Bjørnstad

In this paper we discuss how to handle delay in 5G Ethernet mobile fronthaul networks. The reasons for the delay requirements, addressing both service and protocol specific requirements, are discussed together with prime carrier needs of scalability of networks and easy migration to new network solutions. In light of these ...

Dev-for-Operations and Multi-sided Platform for Next Generation Platform as a Service

This paper presents two new challenges for the Telco ecosystem transformation in the era of cloud-native microservice-based architectures. (1) Development-for-Operations (Dev-for-Operations) impacts not only the overall workflow for deploying a Platform as a Service (PaaS) in an open foundry environment, but also the Telco business as well as operational models ...

Limited Sharing Multi-Party Computation for Massive matrix Operations

In this paper, we introduce limited-sharing multi party computation; in which there is a network of workers (processors) and also a set of input nodes that have access to some massive matrices as private inputs. These input nodes aim to offload computation of a polynomial function of the matrices to ...

Multi-version Coding with Side Information

  • Ali R.
  • Cadambe V.
  • Llorca J.
  • Tulino A.

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. In applications of storage systems to modern key-value stores, the stored data is highly dynamic due to frequent updates from the system write clients. The multi-version coding problem has been formulated to study the cost of storing dynamic data in asynchronous ...

Cost of Path Loss and Local Cooperation in Capacity Scaling of Extended Wireless Networks

  • Du J.
  • Muriel Medard
  • Shlomo Shamai (Shitz)

Given a large wireless network consisting of randomly deployed nodes, where each of the nodes wants to transmit to a random destination node within the network at some equal rate, how fast can the sum rate grow as the number of nodes scales up at fixed density? This question is ...

Sparse NOMA: A Closed-Form Characterization

  • Benjamin M. Zaidel
  • Shental O.
  • Shlomo Shamai (Shitz)

Understanding fundamental limits of the various technologies suggested for future 5G and beyond cellular systems is crucial for developing efficient state-of-the-art designs. A leading technology of major interest is non-orthogonal multiple-access (NOMA). In this paper, we derive an explicit rigorous closed-form analytical expression for the optimum spectral efficiency in the ...

A Compressed Sensing Approach for Distribution Matching

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary source via position modulation (PM). We then present a simple and exact matcher ...

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

In this paper, we introduce limited-sharing multiparty computation; in which there is a network of workers (processors) and a set of sources, each having access to a massive matrix as a private input. These sources aim to offload the task of computing a polynomial function of the matrices to the ...

Compressive Phase Retrieval of Structured Signals

Compressive phase retrieval is the problem of recovering a structured vector $bf{x} in mathds{C}^n$ from its phaseless linear measurements. A compression algorithm aims to represent structured signals with as few bits as possible. As a result of extensive research devoted to compression algorithms, in many signal classes, compression algorithms are ...

Genome-Wide Association Studies: Information Theoretic Limits of Reliable Learning

In the problems of Genome-Wide Association Study (GWAS), the objective is to associate subsequences of individual's genomes to the observable characteristics called phenotypes. The genome containing the biological information of an individual can be represented by a sequence of length <strong>G</strong>. Many observable characteristics of the individuals can be related ...

Erasure Coding for Decentralized Coded Caching

  • Maddahali M.
  • Mohajer S.
  • Reisizadeh H.

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. Coded caching can significantly decrease the communication load in peak hours of the network. The gain of caching is maximized in a centralized setting, where the cache content of users are opportunistically designed. In the absence of a centralized placement, users' ...

Compressive imaging via one-shot measurements

  • Jalali S.
  • Yuan X.

: One-shot measurement systems combine multiple frames in a signal such as a video into a single frame of the same dimensions. Each element (pixel) in the measured frame is a linear combination of the corresponding elements (pixels) in the combined frames. Such systems are a crucial part of various ...

Compressed Coded Distributed Computing

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, especially for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed ...

Straggler Mitigation in Distributed Matrix Multiplication: Fundamental Limits and Optimal Coding

Consider massive matrix multiplication, a problem that underlies many data analytic applications, in a large-scale distributed system comprising a group of workers. We target the stragglers' delay performance bottleneck, which is due to the unpredictable latency in waiting for slowest nodes (or stragglers) to finish their tasks. We propose a ...