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A Poisson Process-Based Random Access Channel for 5G and Beyond Networks

1
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
3
Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Korea
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 2020 IEEE International Symposium on Networks, Computers and Communications (ISNCC 2020), Montreal, QC, Canada, 20–22 October 2020.
A.O.A. and R.A. have equally contributed as First Author.
Academic Editor: José A. Tenreiro Machado
Mathematics 2021, 9(5), 508; https://doi.org/10.3390/math9050508
Received: 4 January 2021 / Revised: 15 February 2021 / Accepted: 24 February 2021 / Published: 2 March 2021
(This article belongs to the Section Network Science)
The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency. View Full-Text
Keywords: applied statistics; random access; channel access; 5G; beyond 5G; resource allocation applied statistics; random access; channel access; 5G; beyond 5G; resource allocation
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MDPI and ACS Style

Almagrabi, A.O.; Ali, R.; Alghazzawi, D.; AlBarakati, A.; Khurshaid, T. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics 2021, 9, 508. https://doi.org/10.3390/math9050508

AMA Style

Almagrabi AO, Ali R, Alghazzawi D, AlBarakati A, Khurshaid T. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics. 2021; 9(5):508. https://doi.org/10.3390/math9050508

Chicago/Turabian Style

Almagrabi, Alaa O., Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, and Tahir Khurshaid. 2021. "A Poisson Process-Based Random Access Channel for 5G and Beyond Networks" Mathematics 9, no. 5: 508. https://doi.org/10.3390/math9050508

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