101,742 research outputs found
Deep Reinforcement Learning for Resource Allocation in V2V Communications
In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications
Smoothing Riemannian Metrics with Bounded Ricci Curvatures in Dimension Four, II
This note is a continuation of the author's paper \cite{Li}. We prove that if
the metric of a 4-manifold has bounded Ricci curvature and the curvature
has no local concentration everywhere, then it can be smoothed to a metric with
bounded sectional curvature. Here we don't assume the bound for local Sobolev
constant of and hence this smoothing result can be applied to the
collapsing case.Comment: 17 page
Inference on locally ordered breaks in multiple regressions
We consider issues related to inference about locally ordered breaks in a system of equations, as originally proposed by Qu and Perron (2007 Qu, Z., Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica 75:459–502.[Crossref], [Web of Science ®], [Google Scholar]). These apply when break dates in different equations within the system are not separated by a positive fraction of the sample size. This allows constructing joint confidence intervals of all such locally ordered break dates. We extend the results of Qu and Perron (2007 Qu, Z., Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica 75:459–502.[Crossref], [Web of Science ®], [Google Scholar]) in several directions. First, we allow the covariates to be any mix of trends and stationary or integrated regressors. Second, we allow for breaks in the variance-covariance matrix of the errors. Third, we allow for multiple locally ordered breaks, each occurring in a different equation within a subset of equations in the system. Via some simulation experiments, we show first that the limit distributions derived provide good approximations to the finite sample distributions. Second, we show that forming confidence intervals in such a joint fashion allows more precision (tighter intervals) compared to the standard approach of forming confidence intervals using the method of Bai and Perron (1998 Bai, J., Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica 66:47–78.[Crossref], [Web of Science ®], [Google Scholar]) applied to a single equation. Simulations also indicate that using the locally ordered break confidence intervals yields better coverage rates than using the framework for globally distinct breaks when the break dates are separated by roughly 10% of the total sample size
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