5,656 research outputs found
Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning
To efficiently exploit the massive amounts of raw data that are increasingly
being generated in mobile edge networks, federated learning (FL) has emerged as
a promising distributed learning technique. By collaboratively training a
shared learning model on edge devices, raw data transmission and storage are
replaced by the exchange of the local computed parameters/gradients in FL,
which thus helps address latency and privacy issues. However, the number of
resource blocks when using traditional orthogonal transmission strategies for
FL linearly scales with the number of participating devices, which conflicts
with the scarcity of communication resources. To tackle this issue,
over-the-air computation (AirComp) has emerged recently which leverages the
inherent superposition property of wireless channels to perform one-shot model
aggregation. However, the aggregation accuracy in AirComp suffers from the
unfavorable wireless propagation environment. In this paper, we consider the
use of intelligent reflecting surfaces (IRSs) to mitigate this problem and
improve FL performance with AirComp. Specifically, a performance-oriented
design scheme that directly minimizes the optimality gap of the loss function
is proposed to accelerate the convergence of AirComp-based FL. We first analyze
the convergence behavior of the FL procedure with the absence of channel fading
and noise. Based on the obtained optimality gap which characterizes the impact
of channel fading and noise in different communication rounds on the ultimate
performance of FL, we propose both online and offline approaches to tackle the
resulting design problem. Simulation results demonstrate that such a
performance-oriented design strategy can achieve higher test accuracy than the
conventional isolated mean square error (MSE) minimization approach in FL.Comment: This work has been submitted to the IEEE for possible publicatio
A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks
Centrality is widely recognized as one of the most critical measures to
provide insight in the structure and function of complex networks. While
various centrality measures have been proposed for single-layer networks, a
general framework for studying centrality in multilayer networks (i.e.,
multicentrality) is still lacking. In this study, a tensor-based framework is
introduced to study eigenvector multicentrality, which enables the
quantification of the impact of interlayer influence on multicentrality,
providing a systematic way to describe how multicentrality propagates across
different layers. This framework can leverage prior knowledge about the
interplay among layers to better characterize multicentrality for varying
scenarios. Two interesting cases are presented to illustrate how to model
multilayer influence by choosing appropriate functions of interlayer influence
and design algorithms to calculate eigenvector multicentrality. This framework
is applied to analyze several empirical multilayer networks, and the results
corroborate that it can quantify the influence among layers and multicentrality
of nodes effectively.Comment: 57 pages, 10 figure
Optimal realisations of two-dimensional, totally split-decomposable metrics
A realization of a metric on a finite set is a weighted graph whose vertex set contains such that the shortest-path distance between elements of considered as vertices in is equal to . Such a realization is called optimal if the sum of its edge weights is minimal over all such realizations. Optimal realizations always exist, although it is NP-hard to compute them in general, and they have applications in areas such as phylogenetics, electrical networks and internet tomography. A. Dress (1984) showed that the optimal realizations of a metric are closely related to a certain polytopal complex that can be canonically associated to called its tight-span. Moreover, he conjectured that the (weighted) graph consisting of the zero- and one-dimensional faces of the tight-span of must always contain an optimal realization as a homeomorphic subgraph. In this paper, we prove that this conjecture does indeed hold for a certain class of metrics, namely the class of totally-decomposable metrics whose tight-span has dimension two. As a corollary, it follows that the minimum Manhattan network problem is a special case of finding optimal realizations of two-dimensional totally-decomposable metrics
A low-cost time-hopping impulse radio system for high data rate transmission
We present an efficient, low-cost implementation of time-hopping impulse
radio that fulfills the spectral mask mandated by the FCC and is suitable for
high-data-rate, short-range communications. Key features are: (i) all-baseband
implementation that obviates the need for passband components, (ii) symbol-rate
(not chip rate) sampling, A/D conversion, and digital signal processing, (iii)
fast acquisition due to novel search algorithms, (iv) spectral shaping that can
be adapted to accommodate different spectrum regulations and interference
environments. Computer simulations show that this system can provide 110Mbit/s
at 7-10m distance, as well as higher data rates at shorter distances under FCC
emissions limits. Due to the spreading concept of time-hopping impulse radio,
the system can sustain multiple simultaneous users, and can suppress narrowband
interference effectively.Comment: To appear in EURASIP Journal on Applied Signal Processing (Special
Issue on UWB - State of the Art
A General Robust Linear Transceiver Design for Multi-Hop Amplify-and-Forward MIMO Relaying Systems
In this paper, linear transceiver design for multi-hop amplify-and-forward
(AF) multiple-input multiple-out (MIMO) relaying systems with Gaussian
distributed channel estimation errors is investigated. Commonly used
transceiver design criteria including weighted mean-square-error (MSE)
minimization, capacity maximization, worst-MSE/MAX-MSE minimization and
weighted sum-rate maximization, are considered and unified into a single
matrix-variate optimization problem. A general robust design algorithm is
proposed to solve the unified problem. Specifically, by exploiting majorization
theory and properties of matrix-variate functions, the optimal structure of the
robust transceiver is derived when either the covariance matrix of channel
estimation errors seen from the transmitter side or the corresponding
covariance matrix seen from the receiver side is proportional to an identity
matrix. Based on the optimal structure, the original transceiver design
problems are reduced to much simpler problems with only scalar variables whose
solutions are readily obtained by iterative water-filling algorithm. A number
of existing transceiver design algorithms are found to be special cases of the
proposed solution. The differences between our work and the existing related
work are also discussed in detail. The performance advantages of the proposed
robust designs are demonstrated by simulation results.Comment: 30 pages, 7 figures, Accepted by IEEE Transactions on Signal
Processin
- …