An Integrated Technique for Security of Cellular 5G-IoT Network Healthcare Architecture

Verma, Manoj Kumar; Sheetlani, Jitendra; Mishra, Vivek; Mishra, Megha

doi:10.1007/978-981-16-5640-8_42

Cited by 1 publication

(2 citation statements)

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“…Several technological advances, including 5G radio access networks (5G-RAN), the Internet of Things (IoT), ultra-reliable low-latency communications (URLLC), heterogeneous networks, including small cells and machine-tomachine (M2M) communications, as well as the understanding of the mechanisms of specific methodologies, are important building blocks of the 5G air interface [3], [4]. Vehicular communication (VC) solutions, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) schemes, are widely used in safety-, information-, mobility-and environmentrelated applications.…”

Section: Inrtoductionmentioning

confidence: 99%

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Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

Shankar¹,

Chaudhar²,

Mishra³

2022

JTIT

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In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.

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Section: Inrtoductionmentioning

confidence: 99%

“…is the correlation factor for the gain of the temporally neighboring channel, the carrier frequency is denoted by f c , c is the light speed [4]- [8], [33], and transmission symbol rate is represented by R s . The zeroth-order Bessel function of the first kind is defined as J 0 (:).…”

Section: Journal Of Telecommunications and Information Technologymentioning

confidence: 99%