Deployment of 5G Networking Infrastructure with Machine Type Communication Considerations

TL;DR

This paper presents a heuristic deployment framework for 5G infrastructure that optimizes small cell and backhaul placement to enhance coverage and cost-efficiency, especially in fiber-scarce scenarios.

Contribution

It introduces a multi-objective integer programming model and a novel heuristic combining relaxation, search, and constraint methods for 5G deployment optimization.

Findings

Proposed framework outperforms conventional models in coverage and cost.

Solutions are close to theoretical lower bounds.

Effective in fiber-scarce deployment scenarios.

Abstract

Designing optimal strategies to deploy small cell stations is crucial to meet the quality-of-service requirements in next-generation cellular networks with constrained deployment costs. In this paper, a general deployment framework is proposed to jointly optimize the locations of backhaul aggregate nodes, small base stations, machine aggregators, and multi-hop wireless backhaul links to accommodate both human-type and machine-type communications. The goal is to provide deployment solutions with best coverage performance under cost constraints. The formulated problem is shown to be a multi-objective integer programming for which it is challenging to obtain the optimal solutions. To solve the problem, a heuristic algorithm is proposed by combining Lagrangian relaxation, the weighted sum method, the $\epsilon$-constraint method and tabu search to obtain both the solutions and bounds, for the objective function. Simulation results show that the proposed framework can provide solutions with better performance compared with conventional deployment models in scenarios where available fiber connections are scarce. Furthermore, the gap between obtained solutions and the lower bounds is quite tight.