Optimal Base Station Deployment with Downlink Rate Coverage Probability Constraint

Chatterjee, Shubhajeet; Abdel-Rahman, Mohammad J.; MacKenzie, Allen B.

doi:10.1109/lwc.2017.2776929

Cited by 17 publications

(21 citation statements)

References 1 publication

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“…Potential approach: To perform a comprehensive comparative study between I-WCP and D-WCP, incorporating the WCPP into the WDP placement problem could be examined. The WDP placement problem, such as the placement problem of mmWave SBSs in UDNs to improve coverage performance, is exhaustively researched by using stochastic geometry, stochastic optimization, and game theory analytical tools O'Lone et al (2017); Chatterjee et al (2018a); Han et al (2012). In this regard, a joint placement optimization framework could be devised to address the challenges of the WCPP and the WDP placement.…”

Section: Direct or Indirect Wcpmentioning

confidence: 99%

The wireless control plane: An overview and directions for future research

Mazied

ElNainay

Abdel-Rahman

et al. 2019

Journal of Network and Computer Applications

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Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research.

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Section: Direct or Indirect Wcpmentioning

confidence: 99%

The wireless control plane: An overview and directions for future research

Mazied

ElNainay

Abdel-Rahman

et al. 2019

Journal of Network and Computer Applications

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“…Proof : In Theorem 1 in [9], we have derived an expression for downlink rate coverage probability of a non-virtualized wireless network that has a similar BS rate allocation model as in Section II-C except slicing i.e., BSs allocate their full capacity (or, active time) to a single network (i.e., one SP). However, in our proposed virtualized wireless network model, a BS (say, BS b) is potentially time-shared among multiple SPs.…”

Section: B Virtual Network Rate Coverage Probabilitymentioning

confidence: 99%

“…In this subsection, we discuss how to solve Problem 1 using (9). As can be seen, Problem 1 is a combinatorial optimization problem with NP complexity.…”

Section: B Virtual Network Rate Coverage Probabilitymentioning

confidence: 99%

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Virtualization Framework for Cellular Networks with Downlink Rate Coverage Probability Constraints

Chatterjee

Abdel-Rahman

MacKenzie

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Wireless network virtualization is emerging as an important technology for next-generation (5G) wireless networks.A key advantage of introducing virtualization in cellular networks is that service providers can robustly share virtualized network resources (e.g., infrastructure and spectrum) to extend coverage, increase capacity, and reduce costs. However, the inherent features of wireless networks, i.e., the uncertainty in user equipment (UE) locations and channel conditions impose significant challenges on virtualization and sharing of the network resources. In this context, we propose a stochastic optimizationbased virtualization framework that enables robust sharing of network resources. Our proposed scheme aims at probabilistically guaranteeing UEs' Quality of Service (QoS) demand satisfaction, while minimizing the cost for service providers, with reasonable computational complexity and affordable network overhead.

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“…A previously known connectivity lower bound [23] is rather loose to be used for developing the connectivity-constrained mmWave BS deployment techniques that this work is envisioning. Prior work in this topic mainly focused on conventional sub-6GHz systems [24]- [29], and their connectivity model and the environment are different from those in the mmWave MTG.…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient Millimeter Wave Base Station Deployment in Manhattan-Type Geometry

Dong

Kim

et al. 2019

IEEE Access

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Urban millimeter wave (mmWave) communications are limited by link outage due to frequent blockages by obstacles. One approach to this problem is to increase the density of base stations (BSs) to achieve macro diversity gains. Dense BS deployment, however, incurs the increased BS installation cost as well as power consumption. In this work, we propose a framework for connectivity-constrained minimum cost mmWave BS deployment in Manhattan-type geometry (MTG). A closed-form expression of network connectivity is characterized as a function of various factors such as obstacle sizes, BS transmit power, and the densities of obstacles and BSs. Optimization that attains the minimum cost is made possible by incorporating a tight lower bound of the analyzed connectivity expression. A low-complexity algorithm is devised to effectively find an optimal tradeoff between the BS density and transmit power that results in the minimum BS deployment cost while guaranteeing network connectivity. Numerical simulations corroborate our analysis and quantify the best tradeoff of the BS density and transmit power. The proposed BS deployment strategies are evaluated in different network cost configurations, providing useful insights in mmWave network planning and dimensioning.INDEX TERMS Millimeter wave network, connectivity, base station deployment cost, Manhattan-type geometry, lattice process.

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Optimal Base Station Deployment with Downlink Rate Coverage Probability Constraint

Cited by 17 publications

References 1 publication

The wireless control plane: An overview and directions for future research

The wireless control plane: An overview and directions for future research

Virtualization Framework for Cellular Networks with Downlink Rate Coverage Probability Constraints

Cost-Efficient Millimeter Wave Base Station Deployment in Manhattan-Type Geometry

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