Wireless channel modeling and performance analysis based on Markov chain

Liu, Xiaoyang; Liu, Chao; Liu, Wanping; Zeng, Xiaoping

doi:10.1109/ccdc.2017.7978890

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Stridh et al [27] modeled the MIMO channel at 5.8 GHz, where the experimental results show that the established model is feasible. Liu et al [28] gave the model of the wireless channel from the perspective of a Markov chain, and the experimental results show that although the model can meet the practical requirements under various parameter settings.…”

Section: Introductionmentioning

confidence: 99%

WiFi-Based Indoor Positioning and Communication: Empirical Model and Theoretical Analysis

Han

Wang

Huang

et al. 2022

Wireless Communications and Mobile Computing

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GPS (Global Positioning System) has been developed more mature and perfect in outdoor positioning. However, because GPS signals cannot be effectively transmitted in a complex indoor environment, positioning and navigation cannot be performed in typical situations such as indoor emergency occurrence location determination, shopping mall guides, and intelligent service robot fixed-point services. With the deepening of related researches, technologies based on ultrasound, WiFi, Bluetooth, radio frequency identification, infrared, etc. have been used to achieve indoor target positioning. The traditional WiFi-based indoor positioning technology uses the RSS value and CSI value of the WiFi signal to build a fingerprint database for positioning. The algorithm is complex and highly cost, and the function is relatively single. In this paper, for the first time, we propose a new algorithm for integrating indoor target positioning and communication based on WiFi signals. Using RSS value of WiFi signal for target location and using CSI value for indoor wireless channel modeling to get the system transmission function to achieve communication purpose, the experimental results show that the positioning algorithm can achieve positioning within the ideal accuracy range, and the communication algorithm part can accurately recover the system function of the wireless multipath channel and finally achieve the goal of integration of positioning and communication based on the WiFi signal level.

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Section: Introductionmentioning

confidence: 99%

WiFi-Based Indoor Positioning and Communication: Empirical Model and Theoretical Analysis

Han

Wang

Huang

et al. 2022

Wireless Communications and Mobile Computing

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“…The establishment of Markov chain from the branch of probability theory by A. Markov was developed in 1913 by applying in poetry (Hayes, 2013). Later, so many applications of Markov chain (Von Hilgers and Langville, 2006) appeared such as forecasting variables of economics such as poverty, inflation, employment (Waller et al, 2019), supply chain management (Yin et al, 2010), optimization (Cao, 2015a(Cao, , 2015bCao, 2009), molecular reaction (Salehi et al, 2015), passphrase cracking (Sparell and Simovits, 2016), image segmentation (Boudaren and Monfrini and W Pieczynski, 2012) and execution examination of wireless communications (Liu et al, 2017), to say a few. The study on classification of Markov chain as homogeneous and non-homogeneous is also experienced (Cao, 2015a(Cao, , 2015bWan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In a set of same kind of images by applying Markov chain in posterior probabilities obtained from Bayesian inferences, they established the potentiality of the proposed system in image registration (Karabulut et al, 2017). Mostly difficult technological problems are solved by neural networks, Liu et al (2017) proposed Markov chain and fuzzy Markov chain methodology in addition to neural networks to reduce the error between actual values and predicted values. The proposed methodology was implemented on nano-composite material, and it improved the forecasting process compared with executing artificial neural network alone (Liu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

State space classification of Markov password – an alphanumeric password authentication scheme for secure communication in cloud computing

Vaithyasubramanian

Sundararajan

2021

IJPCC

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Purpose Purpose of this study is to classify the states of Markov Chain for the implementation of Markov Password for effective security. Password confirmation is more often required in all authentication process, as the usage of computing facilities and electronic devices have developed hugely to access networks. Over the years with the increase in numerous Web developments and internet applications, each platform needs ID and password validation for individual users. Design/methodology/approach In the technological development of cloud computing, in recent times, it is facing security issues. Data theft, data security, denial of service, patch management, encryption management, key management, storage security and authentication are some of the issues and challenges in cloud computing. Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password. Markov passwords – a rule-based password formation are created or generated by using Markov chain. Representation of Markov password formation can be done by state space diagram or transition probability matrix. State space classification of Markov chain is one of the basic and significant properties. The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper. Analysis on strength of obvious password of length eight has shown range of 7%–9% although the converted Markov password has shown more than 82%. As an effective methodology, this password authentication can be implemented in cloud portal and password login validation process. Findings The objective of this paper is to classify the states of Markov chain to support the practice of this type of password in the direction of effective authentication for secure communication in cloud computing. Conversion of some sample obvious password into Markov password and comparative analysis on their strength is also presented in this paper. Originality/value Validation in user login authentications is generally processed and executed by password. To authenticate universally, alphanumeric passwords are used. One of the promising proposed methodologies in this type of password authentication is Markov password.

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“…Other authors have also used Markov chains to describe statistical behavior change in the channel, such as Liu et al [10] and Altinel and Kurt [11]. Lutz et al [12], for example, defined a channel model with two states, one classified as good and the other classified as bad, depending on the conditions and level of attenuation in each scenario.…”

Section: Introductionmentioning

confidence: 99%

Bit Error Probability of M-QAM under Impulsive Noise and Fading Modeled by Markov Chains

Silva¹,

Alencar²,

Queiroz³

et al. 2018

RADIOENGINEERING

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This article presents new exact expressions, written in terms of elementary transcendental functions, for calculating the bit error probability of M-ary Quadrature Amplitude Modulation (M-QAM) scheme considering the wireless communication channel modeled by a Markov chain with N states. For the numerical evaluation of the expressions obtained, a particular case of a Markov chain with two states is considered, with each state representing distinct scenarios. In the first scenario it is considered the presence of Gated Additive White Gaussian Noise (GAWGN) and fading η-µ or κ-µ, while the second scenario considers the presence of the Double Gated Additive White Gaussian Noise (G 2 AWGN) and fading η-µ or κ-µ. Bit error probability curves as a function of the signal-to-permanent-noise ratio for different values of the signal-to-impulsive-noise ratio, fading parameters and modulation order M are also presented.

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Wireless channel modeling and performance analysis based on Markov chain

Cited by 6 publications

References 32 publications

WiFi-Based Indoor Positioning and Communication: Empirical Model and Theoretical Analysis

WiFi-Based Indoor Positioning and Communication: Empirical Model and Theoretical Analysis

State space classification of Markov password – an alphanumeric password authentication scheme for secure communication in cloud computing

Bit Error Probability of M-QAM under Impulsive Noise and Fading Modeled by Markov Chains

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