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Dynamic Clustering and RRH Selection in Non-coherent Ultra-Dense CRAN with Limited Fronthaul Capacity

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Abstract

In this study, We aim to design a Dynamic Clustering scheme to maximize the Weighted Sum Rate in Ultra-Dense Cloud Radio Access Network (UD-CRAN) where mm-Wave fronthaul links with limited capacity is deployed. To improve the network performance in terms of users’ data rate, The Non-Coherent Joint Transmission is considered which is the superior transmission strategy in the limited fronthaul capacity regime. The Dynamic Clustering is proposed, which has the potential to optimize RRH selection and form a sparse beamforming vector for each user. In this study, we investigate and analyze the closed-form expressions of users’ data rate and use the \(l_1\ norm\) to approximate the non-convex fronthaul capacity constraint as a convex weighted power constraint. then, We developed a modified Successive Convex approximation-based algorithm to solve the complicated optimization problem. Our simulation results indicate that in UD-CRAN with mm-Wave fronthaul links, the proposed Dynamic Clustering improve the performance of the system significantly.

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Data Availability

The data that support the findings of this study are available on request from the corresponding author.

Code Availability

The code that support the findings of this study are available on request from the corresponding author.

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Authors and Affiliations

  1. Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Alireza M. Hosseini & Abbas Mohammadi

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  1. Alireza M. Hosseini
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  2. Abbas Mohammadi
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The authors are with the Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

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Correspondence to Alireza M. Hosseini.

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Hosseini, A.M., Mohammadi, A. Dynamic Clustering and RRH Selection in Non-coherent Ultra-Dense CRAN with Limited Fronthaul Capacity. Wireless Pers Commun 131, 1131–1148 (2023). https://doi.org/10.1007/s11277-023-10473-6

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