Abstract
Network systems are widely used in today's digital communication systems and applications. Network delay is a very important factor affecting the efficiency and reliability of the system, and researchers often use a model to predict and understand the causes and patterns of delay. For our model, we propose beginning with the Hidden Markov Model (HMM) of the transmission network, which is used to analyze the network's operational situation and to predict the approximate state of the system into the future using the HMM prediction algorithm. Then, employing Latent Dirichlet Allocation (LDA), we put forward the Delay Factor Model (DFM) value for the delay. In the DFM, we need to map the delay's interval to an integer, designated as DII (Delay Interval Integer), and the factors returned define the hidden states of the HMM. From the view of the DFM, DII is generated from a factor and the previous DII randomly. We use the Gibbs Sampling approach to obtain an estimation of the DFM's parameters. By defining the HMM and DFM, we can forecast future delay with high accuracy, and the result can be shown to follow the peaks and troughs of the real operation of the network system's delay patterns.
Highlights
Digital communication network systems can be found in many varied applications, from simple to highly complex
We propose in this paper, we began by using Latent Semantic Analysis (LSA), which was originally developed as a technique for language analysis
EXPERIMENTS AND RESULTS The Delay Factor Model (DFM) is more complicated than the Latent Dirichlet Allocation (LDA); (1) the distribution of a Word is associated with the Topic and the previous Word; (2) DFM contains the Beta distribution, which should be computed during the Gibbs Sampling
Summary
Digital communication network systems can be found in many varied applications, from simple to highly complex. Our model of the digital transmission network is based on the data from delay patterns in real world operation, but from this, we want to predict the state of all network situations in the future with useful accuracy. This situation can be resolved using HMM, returning the approximate prediction or information via the HMM function. The model we propose, the Delay Factor Model (DFM), is based on LDA
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