57 research outputs found
Optimal association of mobile users to multi-access edge computing resources
Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity
Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
Topological signal processing over simplicial complexes
The goal of this paper is to establish the fundamental tools to analyze signals defined over a topological space, i.e. a set of points along with a set of neighborhood relations. This setup does not require the definition of a metric and then it is especially useful to deal with signals defined over non-metric spaces. We focus on signals defined over simplicial complexes. Graph Signal Processing (GSP) represents a special case of Topological Signal Processing (TSP), referring to the situation where the signals are associated only with the vertices of a graph. Even though the theory can be applied to signals of any order, we focus on signals defined over the edges of a graph and show how building a simplicial complex of order two, i.e. including triangles, yields benefits in the analysis of edge signals. After reviewing the basic principles of algebraic topology, we derive a sampling theory for signals of any order and emphasize the interplay between signals of different order. Then we propose a method to infer the topology of a simplicial complex from data. We conclude with applications to real edge signals and to the analysis of discrete vector fields to illustrate the benefits of the proposed methodologies
Graph topology inference based on sparsifying transform learning
Graph-based representations play a key role in machine learning. The
fundamental step in these representations is the association of a graph
structure to a dataset. In this paper, we propose a method that aims at finding
a block sparse representation of the graph signal leading to a modular graph
whose Laplacian matrix admits the found dictionary as its eigenvectors. The
role of sparsity here is to induce a band-limited representation or,
equivalently, a modular structure of the graph. The proposed strategy is
composed of two optimization steps: i) learning an orthonormal sparsifying
transform from the data; ii) recovering the Laplacian, and then topology, from
the transform. The first step is achieved through an iterative algorithm whose
alternating intermediate solutions are expressed in closed form. The second
step recovers the Laplacian matrix from the sparsifying transform through a
convex optimization method. Numerical results corroborate the effectiveness of
the proposed methods over both synthetic data and real brain data, used for
inferring the brain functionality network through experiments conducted over
patients affected by epilepsy.Comment: Submitted to IEEE Transactions on Signal Processing, March 201
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Migrating computational intensive tasks from mobile devices to more
resourceful cloud servers is a promising technique to increase the
computational capacity of mobile devices while saving their battery energy. In
this paper, we consider a MIMO multicell system where multiple mobile users
(MUs) ask for computation offloading to a common cloud server. We formulate the
offloading problem as the joint optimization of the radio resources-the
transmit precoding matrices of the MUs-and the computational resources-the CPU
cycles/second assigned by the cloud to each MU-in order to minimize the overall
users' energy consumption, while meeting latency constraints. The resulting
optimization problem is nonconvex (in the objective function and constraints).
Nevertheless, in the single-user case, we are able to express the global
optimal solution in closed form. In the more challenging multiuser scenario, we
propose an iterative algorithm, based on a novel successive convex
approximation technique, converging to a local optimal solution of the original
nonconvex problem. Then, we reformulate the algorithm in a distributed and
parallel implementation across the radio access points, requiring only a
limited coordination/signaling with the cloud. Numerical results show that the
proposed schemes outperform disjoint optimization algorithms.Comment: Paper submitted to IEEE Trans. on Signal and Information Processing
over Network
Distributed signal processing and optimization based on in-network subspace projections
We study distributed optimization and processing of subspace-constrained signals in multi-agent networks with sparse connectivity. We introduce the first optimization framework based on distributed subspace projections, aimed at minimizing a network cost function depending on the specific processing task, while imposing subspace constraints on the final solution. The proposed method hinges on (sub)gradient optimization techniques while leveraging distributed projections as a mechanism to enforce subspace constraints in a cooperative and distributed fashion. Asymptotic convergence rates to optimal solutions of the problem are established under different assumptions (e.g., nondifferentiability, nonconvexity, etc.) on the objective function. We also introduce an extension of the framework that works with constant step-sizes, thus enabling faster convergence to optimal solutions of the optimization problem. Our algorithmic framework is very flexible and can be customized to a variety of problems in distributed signal processing. Finally, numerical tests on synthetic and realistic data illustrate how the proposed methods compare favorably to existing distributed algorithms
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