Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

08/04/2014
by   Yu Liu, et al.
0

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/27/2012

Sparse Stochastic Inference for Latent Dirichlet allocation

We present a hybrid algorithm for Bayesian topic models that combines th...
research
08/02/2016

Blocking Collapsed Gibbs Sampler for Latent Dirichlet Allocation Models

The latent Dirichlet allocation (LDA) model is a widely-used latent vari...
research
04/23/2020

Extending the Lora modulation to add further parallel channels and improve the LoRaWAN network performance

In this paper we present a new modulation, called DLoRa, similar in prin...
research
02/19/2013

Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification

In this letter, we derive the optimal discriminant functions for modulat...
research
06/27/2016

Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video

This paper proposes a novel dynamic Hierarchical Dirichlet Process topic...
research
07/12/2018

Sequential Sampling for Optimal Bayesian Classification of Sequencing Count Data

High throughput technologies have become the practice of choice for comp...
research
12/24/2010

Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

We present a novel modulation level classification (MLC) method based on...

Please sign up or login with your details

Forgot password? Click here to reset