TACAN: The Shaping of Delay Distribution Under Multipath Fading Channel for Industrial IoT Systems

Fang-fang, Liu; Dai, Xuewu; Jin, Mengran; Zhang, Wuxiong; Yang, Yang; Qin, Fei

doi:10.1109/jiot.2022.3153515

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…One-dimensional Markov chain [3] � First-order FSMC [3,7] � Mathematical derivation [13][14][15] Single channel in OMA [3] � Higher-order FSMC [4] � Measurement [9,10] � Hidden Markov model [9,10] � Machine learning [11] � ⋯ Multidimensional Markov chain [3,24] Parallel Markov chains [8,25,26] � Measurement [6,27] Multiple channels in OMA [5] � Machine learning [12,28] Order statistics of multidimensional Markov chains [29] --Multiple channels in NOMA…”

Section: Mathematical Model Category Characterisation Methods Applica...mentioning

confidence: 99%

“…[3], the observable Markov model, for example, those models in refs. [5][6][7][8], is suitable for situations where the state of the channel is directly observable. The hidden Markov model is suitable for situations where the state of the channel is hidden with imperfect and noisy channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the finite‐state Markov channel was employed to design a cooperative multi‐relay network with hybrid backscatter and wireless‐powered relaying [5]. A delay shape control method in the wireless‐enabled Industrial Internet of Things system was proposed based on the finite‐state Markov model of a fading channel [6]. Considering the finite‐state Markov channel, a non‐orthogonal hybrid automatic repeat request for ultra‐reliability and low‐latency communications was designed and analysed [7].…”

Section: Introductionmentioning

confidence: 99%

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Markov chain modelling of ordered Rayleigh fading channels in non‐orthogonal multiple access wireless networks

Chen

Zhang

Zhu

2023

IET Signal Processing

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A first‐order finite‐state Markov chain (FSMC) typically models the Rayleigh fading channel in the open literature because the first‐order FSMC is analytically tractable and can derive closed‐form results. Non‐orthogonal multiple access (NOMA) has been recognised as a novel wireless technology that addresses challenges in the next generation of mobile communications. According to the power‐domain NOMA protocol, channels in the NOMA wireless network are sorted by the channel gain. Then considering NOMA, there is insufficient information on how to further form a suitable model for ordered Rayleigh fading channels based on the first‐order FSMC. Given the mathematical statement on how to model the order statistics of multidimensional Markov chains for ordered Rayleigh fading channels, the authors consider these order statistics as a Markov chain, and propose specific processes of representing the state space and constructing the transition probability matrix accordingly. Numerical and simulation results validate the mathematical correctness and accuracy of these novel processes. In addition, for ordered Rayleigh fading channels, the performances of various methods of partitioning the entire signal‐to‐noise ratio range are compared. The performance comparison results are the same as those obtained for the individual unordered Rayleigh fading channel.

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