322,953 research outputs found
DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems
LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive
solution for video delivery to very large groups in crowded venues. However,
deployment and management of eMBMS systems is challenging, due to the lack of
realtime feedback from the User Equipment (UEs). Therefore, we present the
Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo
leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs.
These instructions indicate the reporting rates as a function of the observed
Quality of Service (QoS). This simple feedback mechanism collects very limited
QoS reports from the UEs. The reports are used for network optimization,
thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and
evaluate its performance analytically and via extensive simulations.
Specifically, we show that DyMo infers the optimal eMBMS settings with
extremely low overhead, while meeting strict QoS requirements under different
UE mobility patterns and presence of network component failures. For instance,
DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1%
percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5
to 10 reports per second regardless of the number of UEs
A simple GMM estimator for the semi-parametric mixed proportional hazard model
Ridder and Woutersen (2003) have shown that under a weak condition on the baseline hazard, there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspeci�ed distribution of the unobserved heterogeneity. We extend the Linear Rank Estimator (LRE) of Tsiatis (1990) and Robins and Tsiatis
(1991) to this class of models. The optimal LRE is a two-step estimator. We propose a simple one-step estimator that is close to optimal if there is no unobserved heterogeneity. The e¢ ciency gain associated with the optimal LRE increases with the degree of unobserved heterogeneity.
The CMA Evolution Strategy: A Tutorial
This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands
for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized,
method for real-parameter (continuous domain) optimization of non-linear,
non-convex functions. We try to motivate and derive the algorithm from
intuitive concepts and from requirements of non-linear, non-convex search in
continuous domain.Comment: ArXiv e-prints, arXiv:1604.xxxx
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