15,533 research outputs found
Blind Demixing for Low-Latency Communication
In the next generation wireless networks, lowlatency communication is
critical to support emerging diversified applications, e.g., Tactile Internet
and Virtual Reality. In this paper, a novel blind demixing approach is
developed to reduce the channel signaling overhead, thereby supporting
low-latency communication. Specifically, we develop a low-rank approach to
recover the original information only based on a single observed vector without
any channel estimation. Unfortunately, this problem turns out to be a highly
intractable non-convex optimization problem due to the multiple non-convex
rankone constraints. To address the unique challenges, the quotient manifold
geometry of product of complex asymmetric rankone matrices is exploited by
equivalently reformulating original complex asymmetric matrices to the
Hermitian positive semidefinite matrices. We further generalize the geometric
concepts of the complex product manifolds via element-wise extension of the
geometric concepts of the individual manifolds. A scalable Riemannian
trust-region algorithm is then developed to solve the blind demixing problem
efficiently with fast convergence rates and low iteration cost. Numerical
results will demonstrate the algorithmic advantages and admirable performance
of the proposed algorithm compared with the state-of-art methods.Comment: 14 pages, accepted by IEEE Transaction on Wireless Communicatio
Semi-supervised transductive speaker identification
We present an application of transductive semi-supervised learning to the problem of speaker identification. Formulating this problem as one of transduction is the most natural choice in some scenarios, such as when annotating archived speech data. Experiments with the CHAINS corpus show that, using the basic MFCC-encoding of recorded utterances, a well known simple semi-supervised algorithm, label spread, can solve this problem well. With only a small number of labelled utterances, the semi-supervised algorithm drastically outperforms a state of the art supervised support vector machine algorithm. Although we restrict ourselves to the transductive setting in this paper, the results encourage future work on semi-supervised learning for inductive speaker identification
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