# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 6 Conclusions and Future Work  
One of the challenges for 5G cellular communication networks is to provide effective channel connectivity, especially for denser UE scenarios. In a 5G network, the random access channel (RACH) procedure is the core channel access mechanism to set up the wireless communication association between a UE and eNB. However, the efficiency of the currently deployed RACH system is greatly affected by the rise in the number of contending UEs in a network. It is due to the limited available channel contention preamble set. The selection of contention preambles based on the uniformly distributed random-access mechanism of the RACH system is one reason for this efficiency loss. In a uniform distribution, each UE has an equal opportunity to select identical contention preambles close to the mean value of the distribution, creating a rise in collisions among the UEs. Since there is only a single contention stage for the UEs to access the channel, we may consider alternate solutions to allow the UEs to access the channel as early as possible. For this purpose, we propose a Poisson process-based RACH, named 2 PRACH, which is based on continuous exponential distribution. The proposed 2 PRACH distributes contention preambles between two bounds in a Poisson point method, in which random variables exist continuously and independently with a constant average rate allowing UEs to access the channel resources at their earliest slots. In this way, the 2 PRACH mechanism distributes the UEs in a parametric collection of the probability distribution. The performance evaluation results of simulation experiments show that 2 PRACH significantly improves the reliability of the network. The increased reliability is achieved due to the enhanced capacity of the UEs to transmit their data packets. At the same time, the long waiting period of the uniformly distributed preamble is eliminated to achieve reduced latency, as well.  
In the future, we plan to apply a reinforcement learning-enabled framework to improve the efficiency of 2 PRACH. The behaviorist appraisal feature of reinforcement learning models is the incentive to incorporate reinforcement-learning to refine the RA procedure in 5G networks. Besides, we are also working to develop a novel analytical model for Poisson process-based channel access mechanisms.  
Conceptualization, methodology, validation, A.O.A. and R.A.; formal analysis, resources, data curation, D.A. and A.A.; writing--original draft preparation, A.O.A. and R.A.; writing--review and editing, D.A. and A.A.; supervision, project administration, T.K.; funding acquisition, R.A. and T.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia grant number RG-2-611-41. The APC was funded by King Abdulaziz University, Saudi Arabia and Sejong University, Korea.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (RG-2-611-41). The author, therefore, gratefully acknowledge DSR technical 440 and financial support.  
The authors declare no conflict of interest.