1,390 research outputs found
Distributed coordination of self-organizing mechanisms in communication networks
The fast development of the Self-Organizing Network (SON) technology in
mobile networks renders the problem of coordinating SON functionalities
operating simultaneously critical. SON functionalities can be viewed as control
loops that may need to be coordinated to guarantee conflict free operation, to
enforce stability of the network and to achieve performance gain. This paper
proposes a distributed solution for coordinating SON functionalities. It uses
Rosen's concave games framework in conjunction with convex optimization. The
SON functionalities are modeled as linear Ordinary Differential Equation
(ODE)s. The stability of the system is first evaluated using a basic control
theory approach. The coordination solution consists in finding a linear map
(called coordination matrix) that stabilizes the system of SON functionalities.
It is proven that the solution remains valid in a noisy environment using
Stochastic Approximation. A practical example involving three different SON
functionalities deployed in Base Stations (BSs) of a Long Term Evolution (LTE)
network demonstrates the usefulness of the proposed method.Comment: submitted to IEEE TCNS. arXiv admin note: substantial text overlap
with arXiv:1209.123
Newton based Stochastic Optimization using q-Gaussian Smoothed Functional Algorithms
We present the first q-Gaussian smoothed functional (SF) estimator of the
Hessian and the first Newton-based stochastic optimization algorithm that
estimates both the Hessian and the gradient of the objective function using
q-Gaussian perturbations. Our algorithm requires only two system simulations
(regardless of the parameter dimension) and estimates both the gradient and the
Hessian at each update epoch using these. We also present a proof of
convergence of the proposed algorithm. In a related recent work (Ghoshdastidar
et al., 2013), we presented gradient SF algorithms based on the q-Gaussian
perturbations. Our work extends prior work on smoothed functional algorithms by
generalizing the class of perturbation distributions as most distributions
reported in the literature for which SF algorithms are known to work and turn
out to be special cases of the q-Gaussian distribution. Besides studying the
convergence properties of our algorithm analytically, we also show the results
of several numerical simulations on a model of a queuing network, that
illustrate the significance of the proposed method. In particular, we observe
that our algorithm performs better in most cases, over a wide range of
q-values, in comparison to Newton SF algorithms with the Gaussian (Bhatnagar,
2007) and Cauchy perturbations, as well as the gradient q-Gaussian SF
algorithms (Ghoshdastidar et al., 2013).Comment: This is a longer of version of the paper with the same title accepted
in Automatic
State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch
This paper concerns state estimation problems in a mean field control
setting. In a finite population model, the goal is to estimate the joint
distribution of the population state and the state of a typical individual. The
observation equations are a noisy measurement of the population.
The general results are applied to demand dispatch for regulation of the
power grid, based on randomized local control algorithms. In prior work by the
authors it has been shown that local control can be carefully designed so that
the aggregate of loads behaves as a controllable resource with accuracy
matching or exceeding traditional sources of frequency regulation. The
operational cost is nearly zero in many cases.
The information exchange between grid and load is minimal, but it is assumed
in the overall control architecture that the aggregate power consumption of
loads is available to the grid operator. It is shown that the Kalman filter can
be constructed to reduce these communication requirements,Comment: To appear, IEEE Trans. Auto. Control. Preliminary version appeared in
the 54rd IEEE Conference on Decision and Control, 201
Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation
In this paper, we consider the distributive queue-aware power and subband
allocation design for a delay-optimal OFDMA uplink system with one base
station, users and independent subbands. Each mobile has an uplink
queue with heterogeneous packet arrivals and delay requirements. We model the
problem as an infinite horizon average reward Markov Decision Problem (MDP)
where the control actions are functions of the instantaneous Channel State
Information (CSI) as well as the joint Queue State Information (QSI). To
address the distributive requirement and the issue of exponential memory
requirement and computational complexity, we approximate the subband allocation
Q-factor by the sum of the per-user subband allocation Q-factor and derive a
distributive online stochastic learning algorithm to estimate the per-user
Q-factor and the Lagrange multipliers (LM) simultaneously and determine the
control actions using an auction mechanism. We show that under the proposed
auction mechanism, the distributive online learning converges almost surely
(with probability 1). For illustration, we apply the proposed distributive
stochastic learning framework to an application example with exponential packet
size distribution. We show that the delay-optimal power control has the {\em
multi-level water-filling} structure where the CSI determines the instantaneous
power allocation and the QSI determines the water-level. The proposed algorithm
has linear signaling overhead and computational complexity ,
which is desirable from an implementation perspective.Comment: To appear in Transactions on Signal Processin
Submodular Stochastic Probing on Matroids
In a stochastic probing problem we are given a universe , where each
element is active independently with probability , and only a
probe of e can tell us whether it is active or not. On this universe we execute
a process that one by one probes elements --- if a probed element is active,
then we have to include it in the solution, which we gradually construct.
Throughout the process we need to obey inner constraints on the set of elements
taken into the solution, and outer constraints on the set of all probed
elements. This abstract model was presented by Gupta and Nagarajan (IPCO '13),
and provides a unified view of a number of problems. Thus far, all the results
falling under this general framework pertain mainly to the case in which we are
maximizing a linear objective function of the successfully probed elements. In
this paper we generalize the stochastic probing problem by considering a
monotone submodular objective function. We give a -approximation algorithm for the case in which we are given
matroids as inner constraints and matroids as outer constraints.
Additionally, we obtain an improved -approximation
algorithm for linear objective functions
Forecast Design in Monetary Capital Stock Measurement
We design a procedure for measuring the United States capital stock of money implied by the Divisia monetary aggregate service flow, in a manner consistent with the present-value model of economic capital stock. We permit non-martingale expectations and time varying discount rates. Based on Barnettâs (1991) definition of the economic stock of money, we compute the U.S. economic stock of money by discounting to present value the flow of expected expenditure on the services of monetary assets, where expenditure on monetary services is evaluated at the user costs of the monetary components. As a theoretically consistent measure of money stock, our economic stock of money nests Rotemberg, Driscoll, and Poterbaâs (1995) currency equivalent index as a special case, under the assumption of martingale expectations. To compute the economic stock of money without imposing martingale expectations, we define a procedure for producing the necessary forecasts based on an asymmetric vector autoregressive model and a Bayesian vector autoregressive model. In application of this proposed procedure, Barnett, Chae, and Keating (2005) find the resulting capital-stock growth-rate index to be surprisingly robust to the modeling of expectations. Similarly the primary conclusions of this supporting paper regard robustness. We believe that further experiments with other forecasting models would further confirm our robustness conclusion. Different forecasting models can produce substantial differences in forecasts into the distant future. But since the distant future is heavily discounted in our stock formula, and since alternative forecasting formulas rarely produce dramatic differences in short term forecasts, we believe that our robustness result obviates prior concerns about the dependency of theoretical monetary capital stock computations upon forecasts of future expected flows. Even the simple martingale forecast, which has no unknown parameters and is easily computed with current period data, produces a discounted stock measure that is adequate for most purposes. Determining an easily measured extended index that can remove the small bias that we identify under the martingale forecast remains a subject for our future research. At the time that Milton Friedman (1969) was at the University of Chicago, the âChicago Schoolâ view on the monetary transmission mechanism was based upon the wealth effect, called the âreal balance effectâ or âPigou (1943) effect,â of open market operations. Our research identifies very large errors in the wealth effects computed from the conventional simple sum monetary aggregates and makes substantial progress in the direction of accurate measurement of monetary-policy wealth effects.Monetary aggregation, Divisia money aggregate, economic stock of money, user cost of money, currency equivalent index, Bayesian vector autoregression, asymmetric vector autoregression.
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