90 research outputs found
A Decentralized Method for Joint Admission Control and Beamforming in Coordinated Multicell Downlink
In cellular networks, admission control and beamforming optimization are
intertwined problems. While beamforming optimization aims at satisfying users'
quality-of-service (QoS) requirements or improving the QoS levels, admission
control looks at how a subset of users should be selected so that the
beamforming optimization problem can yield a reasonable solution in terms of
the QoS levels provided. However, in order to simplify the design, the two
problems are usually seen as separate problems. This paper considers joint
admission control and beamforming (JACoB) under a coordinated multicell MISO
downlink scenario. We formulate JACoB as a user number maximization problem,
where selected users are guaranteed to receive the QoS levels they requested.
The formulated problem is combinatorial and hard, and we derive a convex
approximation to the problem. A merit of our convex approximation formulation
is that it can be easily decomposed for per-base-station decentralized
optimization, namely, via block coordinate decent. The efficacy of the proposed
decentralized method is demonstrated by simulation results.Comment: 2012 IEEE Asilomar Conference on Signals, Systems, and Computer
Resilient Distributed Optimization Algorithms for Resource Allocation
Distributed algorithms provide flexibility over centralized algorithms for
resource allocation problems, e.g., cyber-physical systems. However, the
distributed nature of these algorithms often makes the systems susceptible to
man-in-the-middle attacks, especially when messages are transmitted between
price-taking agents and a central coordinator. We propose a resilient strategy
for distributed algorithms under the framework of primal-dual distributed
optimization. We formulate a robust optimization model that accounts for
Byzantine attacks on the communication channels between agents and coordinator.
We propose a resilient primal-dual algorithm using state-of-the-art robust
statistics methods. The proposed algorithm is shown to converge to a
neighborhood of the robust optimization model, where the neighborhood's radius
is proportional to the fraction of attacked channels.Comment: 15 pages, 1 figure, accepted to CDC 201
Spectral partitioning of time-varying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to
partition an unobserved network from the observation of a (dynamical) graph
signal defined on the network. We consider a scenario where our observed graph
signals are obtained by filtering white noise input, and the underlying network
is different for every observation. In this fashion, the filtered graph signals
can be interpreted as defined on a time-varying network. We model each of the
underlying network realizations as generated by an independent draw from a
latent stochastic blockmodel (SBM). To infer the partition of the latent SBM,
we propose a simple spectral algorithm for which we provide a theoretical
analysis and establish consistency guarantees for the recovery. We illustrate
our results using numerical experiments on synthetic and real data,
highlighting the efficacy of our approach.Comment: 5 pages, 2 figure
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation
This paper considers a joint multi-graph inference and clustering problem for
simultaneous inference of node centrality and association of graph signals with
their graphs. We study a mixture model of filtered low pass graph signals with
possibly non-white and low-rank excitation. While the mixture model is
motivated from practical scenarios, it presents significant challenges to prior
graph learning methods. As a remedy, we consider an inference problem focusing
on the node centrality of graphs. We design an expectation-maximization (EM)
algorithm with a unique low-rank plus sparse prior derived from low pass signal
property. We propose a novel online EM algorithm for inference from streaming
data. As an example, we extend the online algorithm to detect if the signals
are generated from an abnormal graph. We show that the proposed algorithms
converge to a stationary point of the maximum-a-posterior (MAP) problem.
Numerical experiments support our analysis
Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis
This paper treats a blind detection problem to identify the central nodes in
a graph from filtered graph signals. Unlike prior works which impose strong
restrictions on the data model, we only require the underlying graph filter to
satisfy a low pass property with a generic low-rank excitation model. We treat
two cases depending on the low pass graph filter's strength. When the graph
filter is strong low pass, i.e., it has a frequency response that drops sharply
at the high frequencies, we show that the principal component analysis (PCA)
method detects central nodes with high accuracy. For general low pass graph
filter, we show that the graph signals can be described by a structured factor
model featuring the product between a low-rank plus sparse factor and an
unstructured factor. We propose a two-stage decomposition algorithm to learn
the structured factor model via a judicious combination of the non-negative
matrix factorization and robust PCA algorithms. We analyze the identifiability
conditions for the model which lead to accurate central nodes detection.
Numerical experiments on synthetic and real data are provided to support our
findings. We demonstrate significant performance gains over prior works
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