Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks

In this paper, we introduce a new stochastic geometry model, the ‘Modified Matérn Cluster Process (MMCP)’, to provide an accurate representation of cell locations in a network. This model not only incorporates realistic constraints but also takes into account the clu...

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Main Authors: Dorsaf Ghozlani, Aymen Omri, Seifeddine Bouallegue, Hela Chamkhia, Ridha Bouallegue
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/11000121/
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author Dorsaf Ghozlani
Aymen Omri
Seifeddine Bouallegue
Hela Chamkhia
Ridha Bouallegue
author_facet Dorsaf Ghozlani
Aymen Omri
Seifeddine Bouallegue
Hela Chamkhia
Ridha Bouallegue
author_sort Dorsaf Ghozlani
collection DOAJ
description In this paper, we introduce a new stochastic geometry model, the &#x2018;Modified Mat&#x00E9;rn Cluster Process (MMCP)&#x2019;, to provide an accurate representation of cell locations in a network. This model not only incorporates realistic constraints but also takes into account the clustered distribution of user equipment (UE). Specifically, UEs are assumed to cluster around small-cell base stations (<italic>SCBs</italic>), which adhere to a minimum separation distance and are organized according to a Mat&#x00E9;rn Hard-Core Process (MHCP) II, ensuring efficient spatial distribution and reduced interference. To further enhance network performance, we propose a novel transmission scheme specifically designed to mitigate interference. This innovative approach results in the development of a tractable analytical framework, which includes a detailed assessment of the non-interference probability, thereby optimizing resource allocation strategies across the network. Within this framework, we derive expressions for the successful transmission probabilities for both uplink and downlink communications. Additionally, we explore the average ergodic capacity, a crucial metric for evaluating the overall efficacy of the three-dimensional (3-D) MMCP. These derivations enable a deeper understanding of the model&#x2019;s impact on network performance. To validate our theoretical findings, we conduct extensive Monte Carlo simulations, which demonstrate the accuracy of our proposed model. Our results highlight the critical importance of accounting for user distribution when evaluating dynamic system performance.
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spelling doaj-art-beb17b6a985d41f991a93e7d897a94d22025-08-20T02:34:36ZengIEEEIEEE Access2169-35362025-01-0113906519066710.1109/ACCESS.2025.356920211000121Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular NetworksDorsaf Ghozlani0https://orcid.org/0009-0008-7197-5549Aymen Omri1https://orcid.org/0000-0003-2551-9100Seifeddine Bouallegue2https://orcid.org/0000-0001-5338-2223Hela Chamkhia3Ridha Bouallegue4Innovation of Communicant and Cooperative Mobiles (Innov&#x2019;Com) Laboratory (SUP&#x2019;COM), Higher School of Communications of Tunis, University of Carthage, Ariana, TunisiaCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarComputer Science and Engineering Department, Qatar University, Doha, QatarInnovation of Communicant and Cooperative Mobiles (Innov&#x2019;Com) Laboratory (SUP&#x2019;COM), Higher School of Communications of Tunis, University of Carthage, Ariana, TunisiaIn this paper, we introduce a new stochastic geometry model, the &#x2018;Modified Mat&#x00E9;rn Cluster Process (MMCP)&#x2019;, to provide an accurate representation of cell locations in a network. This model not only incorporates realistic constraints but also takes into account the clustered distribution of user equipment (UE). Specifically, UEs are assumed to cluster around small-cell base stations (<italic>SCBs</italic>), which adhere to a minimum separation distance and are organized according to a Mat&#x00E9;rn Hard-Core Process (MHCP) II, ensuring efficient spatial distribution and reduced interference. To further enhance network performance, we propose a novel transmission scheme specifically designed to mitigate interference. This innovative approach results in the development of a tractable analytical framework, which includes a detailed assessment of the non-interference probability, thereby optimizing resource allocation strategies across the network. Within this framework, we derive expressions for the successful transmission probabilities for both uplink and downlink communications. Additionally, we explore the average ergodic capacity, a crucial metric for evaluating the overall efficacy of the three-dimensional (3-D) MMCP. These derivations enable a deeper understanding of the model&#x2019;s impact on network performance. To validate our theoretical findings, we conduct extensive Monte Carlo simulations, which demonstrate the accuracy of our proposed model. Our results highlight the critical importance of accounting for user distribution when evaluating dynamic system performance.https://ieeexplore.ieee.org/document/11000121/3-D HetNetsinterference managementsuccessful transmission probabilitystochastic geometryperformance analysis
spellingShingle Dorsaf Ghozlani
Aymen Omri
Seifeddine Bouallegue
Hela Chamkhia
Ridha Bouallegue
Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
IEEE Access
3-D HetNets
interference management
successful transmission probability
stochastic geometry
performance analysis
title Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
title_full Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
title_fullStr Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
title_full_unstemmed Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
title_short Modified MCP-Based Modeling and Performance Analysis of 3-D Cellular Networks
title_sort modified mcp based modeling and performance analysis of 3 d cellular networks
topic 3-D HetNets
interference management
successful transmission probability
stochastic geometry
performance analysis
url https://ieeexplore.ieee.org/document/11000121/
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