Optimization Model for 5G Network Planning

Bondarenko, Oleg; Ageyev, Dmytro; Mohammed, Othman

doi:10.1109/cadsm.2019.8779298

Cited by 25 publications

(13 citation statements)

References 8 publications

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“…In [53], a detailed technical and economic analysis to minimize the 5G deployment cost in the context of different architectural scenarios is presented; however, it lacks the discussion on coverage, capacity and QoE. A recent work [54] studies the 5G network planning as a profit maximization model for the MNO with minimal deployment cost.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

2020

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The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology.INDEX TERMS 5G, new radio, cellular network dimensioning, network data, capacity model.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

2020

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“…The formulated problem is multi-objective integer programming problem, and heuristic algorithms are presented to solve this challenging optimization problem. In contrast, a mixed integer linear programming (MILP) problem for the 5G network is formulated in [13]. The goal of the optimization problem is to maximize operating profit of the proposed 5G network.…”

Section: Related Workmentioning

confidence: 99%

“…Repair algorithm: The pseudo-code for the repair algorithm is presented in Algorithm 1. In the repair algorithm, a candidate solution, X in (13), that is generated randomly or is evolved by an evolutionary procedure, may violate one or more constraints of the optimization problem. Violation of any constraint makes the candidate solution illegal.…”

Section: Repair Mechanismmentioning

confidence: 99%

Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

Ali

Liu

Ejaz

2020

J Wireless Com Network

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In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

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“…They still rely on 'siloed' processes where engineering and cost assessment take place as distinctly separate phases. Better cellular planning tools are needed [11] and although many exist, they often focus more on either the engineering aspects [12]- [16], or the economic aspects of the planning stage [17], [18]. Furthermore, there is a need for such tools to be also available to the research community, to better connect the analysis of new technological advances to the engineering costs involved in deployment.…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Techno-Economic Assessment Framework for 5G Deployment

Oughton¹,

Κατσαρός²,

Entezami³

et al. 2019

IEEE Access

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Optimal network planning is crucial to ensure viable investments. However, engineering analysis and cost assessment frequently occur independently of each other. Whereas considerable research has been undertaken on 5G networks, there is a lack of openly accessible tools that integrate the engineering and cost aspects, in a techno-economic assessment framework capable of providing geospatially-explicit network analytics. Consequently, this paper details an open-source python simulator for integrated modelling of 5G (pysim5G), that enables both engineering and cost metrics to be assessed in a single unified framework. The tool includes statistical analysis of radio interference to assess the system-level performance of 4G and 5G frequency band coexistence (including millimeter wave), while simultaneously quantifying the costs of ultra-dense 5G networks. An example application of this framework explores the techno-economics of 5G infrastructure sharing strategies, finding that total deployment costs can be reduced by 30% using either passive site sharing, or passive backhaul sharing, or by up to 50% via a multi-operator radio access network. The key contribution is a fully-tested, open-source software codebase, allowing users to undertake integrated techno-economic assessment of 5G deployments in a single geospatial framework.

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Optimization Model for 5G Network Planning

Cited by 25 publications

References 8 publications

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

An Open-Source Techno-Economic Assessment Framework for 5G Deployment

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