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The IEEE Global Conference on Signal and Information Processing (GlobalSIP) is a flagship conference of the IEEE Signal Processing Society. GlobalSIP'15 will be held in Orlando, Florida, USA, December 14-16, 2015. The conference will focus on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference will feature world-class speakers, tutorials, exhibits, and sessions consisting of poster or oral presentations. Outstanding papers will be selected for Best Paper Awards or Best Student Paper Awards; a paper is eligible for a best student paper award if the first author of the paper is a student. IEEE Signal Processing Society and National Science Foundation will provide travel grants to eligible students.

This paper studies wideband hybrid precoder for downlink space-division multiple-access and orthogonal frequency-division multiple-access (SDMA-OFDMA) massive multi-input multi-output (MIMO) systems. We first derive an iterative algorithm to alternatingly optimize the phase-shifter based wideband analog precoder and low-dimensional digital

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Recent studies have demonstrated the effectiveness of proactive resource allocation under the assumption of perfect prediction of the user’s future data rate. In this paper, imperfect rate prediction merely based on the context information including large-scale channel gains of users and statistical information of system available resources is considered. Under the outagebased quality of service constraint, we propose a proactive power allocation policy aimed at minimizing the total transmit energy consumption.

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This slides relate to the GlobalSIP 2015 paper:
"Mobile GPU Accelerated Digital Predistortion on a Software-defined Mobile Transmitter" by Kaipeng Li, et. al.

The full paper can be found in IEEE Xplore or http://kl33.blogs.rice.edu/research/

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We present a geometry-inspired characterization of
target response for active sonar that exploits similarity between
intra-class features to distinguish between different targets
against environmental objects such as a rock. Key innovation is to
represent feature manifolds as a set of ellipsoids, each of which
geometrically encompasses a unique physical characteristic of
the target’s response. We have demonstrated over experimental
field data that for a given target class, these feature ellipsoids

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