Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Abstract

CDMA is an important and basic part of today's communications technologies. This technology can be analyzed efficiently by reducing the time, computation burden, and cost by characterizing the physical layer with a Markov Model. Waveform level simulation is generally used for simulating different parts of a digital communication system. In this paper, we introduce two different mathematical methods to model digital communication channels. Hidden Markov and Semi Hidden Markov models' applications have been investigated for evaluating the DS-CDMA link performance with different parameters. Hidden Markov Models have been a powerful mathematical tool that can be applied as models of discrete-time series in many fields successfully. A semi-hidden Markov model as a stochastic process is a modification of hidden Markov models with states that are no longer unobservable and less hidden. A principal characteristic of this mathematical model is statistical inertia, which admits the generation, and analysis of observation symbol contains frequent runs.

Optimization of Access Point Positioning on Wi-Fi Networks Using the K-Means Clustering Method

“…It is a multivariate statistical technique that identifies groupings of the data objects based on the inter-object similarities computed by a chosen distance metric. Clustering algorithms can be classified into two categories, i.e., Hierarchical clustering and Partitional clustering [3,11,12] .…”