487 research outputs found
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
We propose a novel decomposition framework for the distributed optimization
of general nonconvex sum-utility functions arising naturally in the system
design of wireless multiuser interfering systems. Our main contributions are:
i) the development of the first class of (inexact) Jacobi best-response
algorithms with provable convergence, where all the users simultaneously and
iteratively solve a suitably convexified version of the original sum-utility
optimization problem; ii) the derivation of a general dynamic pricing mechanism
that provides a unified view of existing pricing schemes that are based,
instead, on heuristics; and iii) a framework that can be easily particularized
to well-known applications, giving rise to very efficient practical (Jacobi or
Gauss-Seidel) algorithms that outperform existing adhoc methods proposed for
very specific problems. Interestingly, our framework contains as special cases
well-known gradient algorithms for nonconvex sum-utility problems, and many
blockcoordinate descent schemes for convex functions.Comment: submitted to IEEE Transactions on Signal Processin
Autonomous Spectrum Balancing for Digital Subscriber Lines
The main performance bottleneck of modern Digital Subscriber Line (DSL) networks is the crosstalk among different lines (users). By deploying Dynamic Spectrum Management (DSM) techniques and reducing excess crosstalks among users, a network operator can dramatically increase the data rates and service reach of broadband access. However, current DSM algorithms suffer from either substantial suboptimality in typical deployment scenarios or prohibitively high complexity due to centralized computation. This paper develops, analyzes, and simulates a new suite of DSM algorithms for DSL interference channel models called Autonomous Spectrum Balancing (ASB), for both synchronous and asynchronous transmission cases. In the synchronous case, the transmissions over different tones are orthogonal to each other. In the asynchronous case, the transmissions on different tones are coupled together due to intercarrier- interference. In both cases, ASB utilizes the concept of a 'reference line', which mimics a typical victim line in the interference channel. The basic procedure in ASB algorithms is simple: each user optimizes the weighted sum of the achievable rates on its own line and the reference line while assuming the interferences from other users as noise. Users then iterate until the target rate constraints are met. Good choices of reference line parameters are already available in industry standards, and the ASB algorithm makes the intuitions completely rigorous and theoretically sound. ASB is the first set of algorithms that is fully autonomous, has low complexity, and yet achieves near-optimal performance. It effectively solves the nonconvex and coupled optimization problem of DSL spectrum management, and overcomes the bottleneck of all previous DSM algorithms
FAST Copper for Broadband Access
FAST Copper is a multi-year, U.S. NSF funded project that started in 2004, and is jointly pursued by the research groups of Mung Chiang at Princeton University, John Cioffi at Stanford University, and Alexander Fraser at Fraser Research Lab, and in collaboration with several industrial partners including AT&T. The goal of the FAST Copper Project is to provide ubiquitous, 100 Mbps, fiber/DSL broadband access to everyone in the US with a phone line. This goal will be achieved through two threads of research: dynamic and joint optimization of resources in Frequency, Amplitude, Space, and Time (thus the name 'FAST') to overcome the attenuation and crosstalk bottlenecks, and the integration of communication, networking, computation, modeling, and distributed information management and control for the multi-user twisted pair network
Joint Distributed Access Point Selection and Power Allocation in Cognitive Radio Networks
Spectrum management has been identified as a crucial step towards enabling
the technology of the cognitive radio network (CRN). Most of the current works
dealing with spectrum management in the CRN focus on a single task of the
problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or
spectrum mobility. In this work, we argue that for certain network
configurations, jointly performing several tasks of the spectrum management
improves the spectrum efficiency. Specifically, we study the uplink resource
management problem in a CRN where there exist multiple cognitive users (CUs)
and access points (APs), with each AP operates on a set of non-overlapping
channels. The CUs, in order to maximize their uplink transmission rates, have
to associate to a suitable AP (spectrum decision), and to share the channels
belong to this AP with other CUs (spectrum sharing). These tasks are clearly
interdependent, and the problem of how they should be carried out efficiently
and distributedly is still open in the literature.
In this work we formulate this joint spectrum decision and spectrum sharing
problem into a non-cooperative game, in which the feasible strategy of a player
contains a discrete variable and a continuous vector. The structure of the game
is hence very different from most non-cooperative spectrum management game
proposed in the literature. We provide characterization of the Nash Equilibrium
(NE) of this game, and present a set of novel algorithms that allow the CUs to
distributively and efficiently select the suitable AP and share the channels
with other CUs. Finally, we study the properties of the proposed algorithms as
well as their performance via extensive simulations.Comment: Accepted by Infocom 2011; Infocom 2011, The 30th IEEE International
Conference on Computer Communication
Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks
We study the downlink linear precoder design problem in a multi-cell dense
heterogeneous network (HetNet). The problem is formulated as a general
sum-utility maximization (SUM) problem, which includes as special cases many
practical precoder design problems such as multi-cell coordinated linear
precoding, full and partial per-cell coordinated multi-point transmission,
zero-forcing precoding and joint BS clustering and beamforming/precoding. The
SUM problem is difficult due to its non-convexity and the tight coupling of the
users' precoders. In this paper we propose a novel convex approximation
technique to approximate the original problem by a series of convex
subproblems, each of which decomposes across all the cells. The convexity of
the subproblems allows for efficient computation, while their decomposability
leads to distributed implementation. {Our approach hinges upon the
identification of certain key convexity properties of the sum-utility
objective, which allows us to transform the problem into a form that can be
solved using a popular algorithmic framework called BSUM (Block Successive
Upper-Bound Minimization).} Simulation experiments show that the proposed
framework is effective for solving interference management problems in large
HetNet.Comment: Accepted by IEEE Transactions on Wireless Communicatio
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