Abstract

Fog Radio Access Network (F-RAN) is a promising technology to address the bandwidth bottlenecks and network latency problems, by providing cloud-like services to the end nodes (ENs) at the edge of the network. The network latency can further be decreased by minimizing the transmission delay, which can be achieved by optimizing the number of Fog Nodes (FNs). In this context, we propose a stochastic geometry model to optimize the number of FNs in a finite F-RAN by exploiting the multi-slope path loss model (MS-PLM), which can more precisely characterize the path loss dependency on the propagation environment. The proposed approach shows that the optimum probability of being a FN is determined by the real root of a polynomial equation of a degree determined by the far-field path loss exponent (PLE) of the MS-PLM. The results analyze the impact of the path loss parameters and the number of deployed nodes on the optimum number of FNs. The results show that the optimum number of FNs is less than 7% of the total number of deployed nodes for all the considered scenarios. It also shows that optimizing the number of FNs achieves a significant reduction in the average transmission delay over the unoptimized scenarios.

Highlights

  • Fog computing is considered as an enabler of the Internet of Things (IoT) and a key technology for fifth-generation (5G) and beyond networks

  • We studied the impacts of the dual-slope path loss model (DS-PLM) parameters on the optimum number of Fog Nodes (FNs), including the path loss exponent (PLE) of near- and far-fields, and the critical distance

  • We showed that the optimum number of FNs can be obtained by solving for the real root of a polynomial equation, the degree of which is determined by the far-field PLE of the link from the end nodes (ENs) to the FNs

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Summary

Introduction

Fog computing is considered as an enabler of the Internet of Things (IoT) and a key technology for fifth-generation (5G) and beyond networks. The propagation environment can be more precisely characterized using multi-slope path loss models (MS-PLMs), in which different link distances experience different PLEs. Stressing the significance of using MS-PLMs in performance analysis of wireless networks [32,33,34], this paper utilizes MS-PLM to deliver a more precise framework regarding optimizing the number of FNs. In this paper, we propose a stochastic geometry model to minimize the transmission delay in BPP F-RANs by determining the optimum number of FNs that maximizes the average data rate using. We derive a closed-form expression of the objective function to minimize the transmission delay by utilizing the dual-slope path loss model (DS-PLM), which is appropriate for any values of the PLEs for uplinks from the ENs to the FNs and the links formed by the FNs and the cloud center.

Network Topology and the Key Assumptions
Propagation Model
Problem Formulation
The Framework for Optimizing the Number of FNs
Single-Slope
Dual-Slope
N-Slope
Numerical Results
Objective
Limitations and Future
Concluding Remarks
Full Text
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