Complex discriminative learning Bayesian neural equalizer

13 years 8 months ago

Download www.uncini.com

Traditional approaches to channel equalization are based on the inversion of the global (linear or nonlinear) channel response. However, in digital links the complete channel inversion is neither required nor desirable. Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be e ectively recasted as a classi

Mirko Solazzi, Aurelio Uncini, Elio D. Di Claudio,

Real-time Traffic

Classical Neural Equalizers | Complete Channel Inversion | Hardware | ISCAS 1999 | Neural Equalizers |

claim paper

» Discriminative factored prior models for personalized contentbased recommendation

» Sparse Bayesian Learning Based on an Efficient Subset Selection

» Competitive generative models with structure learning for NLP classification tasks

» A recursive method for discriminative mixture learning

» Learning multimodal densities on Discriminative Temporal Interaction Manifold for group ac...

» Combining Fisher Linear Discriminants for Dissimilarity Representations

» Label propagation in complex video sequences using semisupervised learning

» Comparing Decision Support Methodologies for Identifying Asthma Exacerbations

Post Info
More Details (n/a)

Added	03 Aug 2010
Updated	03 Aug 2010
Type	Conference
Year	1999
Where	ISCAS
Authors	Mirko Solazzi, Aurelio Uncini, Elio D. Di Claudio, Raffaele Parisi

Comments (0)

Sciweavers

Complex discriminative learning Bayesian neural equalizer

Classical Neural Equalizers | Complete Channel Inversion | Hardware | ISCAS 1999 | Neural Equalizers |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers