59,148 research outputs found
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
Multi-center clinical trials: Randomization and ancillary statistics
The purpose of this paper is to investigate and develop methods for analysis
of multi-center randomized clinical trials which only rely on the randomization
process as a basis of inference. Our motivation is prompted by the fact that
most current statistical procedures used in the analysis of randomized
multi-center studies are model based. The randomization feature of the trials
is usually ignored. An important characteristic of model based analysis is that
it is straightforward to model covariates. Nevertheless, in nearly all model
based analyses, the effects due to different centers and, in general, the
design of the clinical trials are ignored. An alternative to a model based
analysis is to have analyses guided by the design of the trial. Our development
of design based methods allows the incorporation of centers as well as other
features of the trial design. The methods make use of conditioning on the
ancillary statistics in the sample space generated by the randomization
process. We have investigated the power of the methods and have found that, in
the presence of center variation, there is a significant increase in power. The
methods have been extended to group sequential trials with similar increases in
power.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS151 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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