Design of Communication Systems using Deep Learning: A Variational Inference Perspective

04/18/2019
by   Vishnu Raj, et al.
0

An approach to design end to end communication system using deep learning leveraging the generative modeling capabilities of autoencoders is presented. The system models are designed using Deep Neural Networks (DNNs) and the objective function for optimizing these models are derived using variational inference. Through experimental validation, the proposed method is shown to produce better models consistently in terms of error rate performance as well as constellation packing density as compared to previous works.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/06/2018

Unbiased Implicit Variational Inference

We develop unbiased implicit variational inference (UIVI), a method that...
research
02/07/2018

Semi-Amortized Variational Autoencoders

Amortized variational inference (AVI) replaces instance-specific local i...
research
05/22/2017

Detection Algorithms for Communication Systems Using Deep Learning

The design and analysis of communication systems typically rely on the d...
research
09/21/2023

Bayesian sparsification for deep neural networks with Bayesian model reduction

Deep learning's immense capabilities are often constrained by the comple...
research
11/22/2016

Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...
research
05/24/2017

Proximity Variational Inference

Variational inference is a powerful approach for approximate posterior i...
research
06/13/2023

Variational Positive-incentive Noise: How Noise Benefits Models

A large number of works aim to alleviate the impact of noise due to an u...

Please sign up or login with your details

Forgot password? Click here to reset