142,192 research outputs found
Non-cooperative Feedback Rate Control Game for Channel State Information in Wireless Networks
It has been well recognized that channel state information (CSI) feedback is
of great importance for dowlink transmissions of closed-loop wireless networks.
However, the existing work typically researched the CSI feedback problem for
each individual mobile station (MS), and thus, cannot efficiently model the
interactions among self-interested mobile users in the network level. To this
end, in this paper, we propose an alternative approach to investigate the CSI
feedback rate control problem in the analytical setting of a game theoretic
framework, in which a multiple-antenna base station (BS) communicates with a
number of co-channel MSs through linear precoder. Specifically, we first
present a non-cooperative feedback-rate control game (NFC), in which each MS
selects the feedback rate to maximize its performance in a distributed way. To
improve efficiency from a social optimum point of view, we then introduce
pricing, called the non-cooperative feedback-rate control game with price
(NFCP). The game utility is defined as the performance gain by CSI feedback
minus the price as a linear function of the CSI feedback rate. The existence of
the Nash equilibrium of such games is investigated, and two types of feedback
protocols (FDMA and CSMA) are studied. Simulation results show that by
adjusting the pricing factor, the distributed NFCP game results in close
optimal performance compared with that of the centralized scheme.Comment: 26 pages, 10 figures; IEEE Journal on Selected Areas in
Communications, special issue on Game Theory in Wireless Communications, 201
Self-selection patterns in Mexico-U.S. migration: the role of migration networks
This paper examines the role of migration networks in determining self-selection
patterns of Mexico-U.S. migration. We first present a simple theoretical framework
showing how such networks impact on migration incentives at different education
levels and, consequently, how they are likely to affect the expected skill composition
of migration. Using survey data from Mexico, we then show that the probability of
migration is increasing with education in communities with low migrant networks,
but decreasing with education in communities with high migrant networks. This is
consistent with positive self-selection of migrants being driven by high migration
costs, as advocated by Chiquiar and Hanson (2005), and with negative self-selection
of migrants being driven by lower returns to education in the U.S. than in Mexico, as
advocated by Borjas (1987)
Dynamics of Oscillators Coupled by a Medium with Adaptive Impact
In this article we study the dynamics of coupled oscillators. We use
mechanical metronomes that are placed over a rigid base. The base moves by a
motor in a one-dimensional direction and the movements of the base follow some
functions of the phases of the metronomes (in other words, it is controlled to
move according to a provided function). Because of the motor and the feedback,
the phases of the metronomes affect the movements of the base while on the
other hand, when the base moves, it affects the phases of the metronomes in
return.
For a simple function for the base movement (such as in which is the velocity of the base,
is a multiplier, is a proportion and and
are phases of the metronomes), we show the effects on the dynamics of the
oscillators. Then we study how this function changes in time when its
parameters adapt by a feedback. By numerical simulations and experimental
tests, we show that the dynamic of the set of oscillators and the base tends to
evolve towards a certain region. This region is close to a transition in
dynamics of the oscillators; where more frequencies start to appear in the
frequency spectra of the phases of the metronomes
Machine learning in spectral domain
Deep neural networks are usually trained in the space of the nodes, by
adjusting the weights of existing links via suitable optimization protocols. We
here propose a radically new approach which anchors the learning process to
reciprocal space. Specifically, the training acts on the spectral domain and
seeks to modify the eigenvectors and eigenvalues of transfer operators in
direct space. The proposed method is ductile and can be tailored to return
either linear or non linear classifiers. The performance are competitive with
standard schemes, while allowing for a significant reduction of the learning
parameter space. Spectral learning restricted to eigenvalues could be also
employed for pre-training of the deep neural network, in conjunction with
conventional machine-learning schemes. Further, it is surmised that the nested
indentation of eigenvectors that defines the core idea of spectral learning
could help understanding why deep networks work as well as they do
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