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- Title
Channel Identification Machines.
- Authors
Lazar, Aurel A.; Slutskiy, Yevgeniy B.
- Abstract
We present a formal methodology for identifying a channel in a system consisting of a communication channel in cascade with an asynchronous sampler. The channel is modeled as a multidimensional filter, while models of asynchronous samplers are taken from neuroscience and communications and include integrate-and-fire neurons, asynchronous sigma/delta modulators and general oscillators in cascade with zero-crossing detectors. We devise channel identification algorithms that recover a projection of the filter(s) onto a space of input signals loss-free for both scalar and vector-valued test signals. The test signals are modeled as elements of a reproducing kernel Hilbert space (RKHS) with a Dirichlet kernel. Under appropriate limiting conditions on the bandwidth and the order of the test signal space, the filter projection converges to the impulse response of the filter. We show that our results hold for a wide class of RKHSs, including the space of finite-energy band limited signals. We also extend our channel identification results to noisy circuits.
- Subjects
BLIND channel identification (Telecommunications); NEUROSCIENCES; HILBERT space; ELECTRONIC modulators; ELECTRIC oscillators; KERNEL functions; ENERGY bands
- Publication
Computational Intelligence & Neuroscience, 2012, p1
- ISSN
1687-5265
- Publication type
Article
- DOI
10.1155/2012/209590