Self-Organized Scheduling Request for Uplink 5G Networks: A D2D Clustering Approach
Type
ArticleAuthors
Gharbieh, MohammadBader, Ahmed
Elsawy, Hesham

Yang, Hong-Chuan
Alouini, Mohamed-Slim

Adinoyi, Abdulkareem
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2018-10-16Online Publication Date
2018-10-16Print Publication Date
2019-02Permanent link to this record
http://hdl.handle.net/10754/629962
Metadata
Show full item recordAbstract
In one of the several manifestations, the future cellular networks are required to accommodate a massive number of devices; several orders of magnitude compared to today’s networks. At the same time, the future cellular networks will have to fulfill stringent latency constraints. To that end, one problem that is posed as a potential showstopper is extreme congestion for requesting uplink scheduling over the physical random access channel (PRACH). Indeed, such congestion drags along scheduling delay problems. In this paper, the use of self-organized device-to-device (D2D) clustering is advocated for mitigating PRACH congestion. To this end, the paper proposes two D2D clustering schemes, namely; Random-Based Clustering (RBC) and Channel-Gain-Based Clustering (CGBC). Accordingly, this paper sheds light on random access within the proposed D2D clustering schemes and presents a case study based on a stochastic geometry framework. For the sake of objective evaluation, the D2D clustering is benchmarked by the conventional scheduling request procedure. Accordingly, the paper offers insights into useful scenarios that minimize the scheduling delay for each clustering scheme. Finally, the paper discusses the implementation algorithm and some potential implementation issues and remedies.Citation
Gharbieh M, Bader A, El Sawy H, Yang H-C, Alouini M-S, et al. (2018) Self-Organized Scheduling Request for Uplink 5G Networks: A D2D Clustering Approach. IEEE Transactions on Communications: 1–1. Available: http://dx.doi.org/10.1109/TCOMM.2018.2876008.Sponsors
The work of the KAUST team was supported in part by STC under grant RGC/3/2374-01-01arXiv
arXiv:1810.06235Additional Links
https://ieeexplore.ieee.org/document/8491334ae974a485f413a2113503eed53cd6c53
10.1109/TCOMM.2018.2876008