An application of a semi-hidden Markov model in wireless communication systems

Kordnoori, Shaghayegh; Mostafaei, Hamidreza; Behzadi, Mohammad Hassan

doi:10.1007/s40096-019-0279-3

Cited by 5 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A source’s activity pattern is assumed to be governed by a hidden Markov model (HMM). The HMM has been used in many instances to model communication systems [ 37 , 38 , 39 , 40 ]. An HMM imprints a memory on the system that is used to mimic the behavior of a communication system’s channel coding.…”

Section: System Modelmentioning

confidence: 99%

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Ghosh,

Dong,

Haimovich

et al. 2023

Entropy

View full text Add to dashboard Cite

This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources.

show abstract

Section: System Modelmentioning

confidence: 99%

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Ghosh,

Dong,

Haimovich

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…DCM first generates a random number before transmitting one symbol to determine the channel state; it then generates another set of numbers for the input-to-output transition. In the past, DCM approaches were used to analyze and model a wide range of communication channels, such as high-frequency (HF) radio channels [36], indoor PLC channels [23], wireless channels [37], underwater acoustic channels, and, more recently, hybrid PLC-VLC channels [38]. This research combines Markov model concepts with the long burst error characteristics of the transmission medium to gain a deeper understanding of the PLC channel and conduct a thorough analysis of it.…”

Section: Fmms For Discrete Channel Modelingmentioning

confidence: 99%

Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the Fritchman–Markov Model

Iyiola

Familua

Swart

et al. 2023

Sensors

View full text Add to dashboard Cite

Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman–Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system’s statistical error process. The PLC system’s fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman–Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman–Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time.

show abstract

“…The received symbol can be observed, but the state in which an error happens is not observable. HMMs were used for modeling the statistics of burst errors in the communication channel [24][25][26][27][28][29][30] . The advantage of HMM compared to the standard wireless channel simulation is its high advance in simulation time.…”

Section: Figurementioning

confidence: 99%

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

Kordnoori

Mostafaei

Kordnoori

et al. 2021

JTS

Self Cite

View full text Add to dashboard Cite

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.

show abstract

An application of a semi-hidden Markov model in wireless communication systems

Cited by 5 publications

References 21 publications

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels

Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the Fritchman–Markov Model

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

Contact Info

Product

Resources

About