Distributed Cell Clustering Based on Multi-Layer Message Passing for Downlink Joint Processing Coordinated Multipoint Transmission

Dewa, Gilang Raka Rayuda; Park, Cheolsoo; Sohn, Illsoo

doi:10.3390/app10155154

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In the following pages, we will explore the fundamental principles and practical considerations of cell clustering, offering a roadmap to navigate the complexities of this crucial process. Cell clustering, at its core, involves the categorization of cells into groups based on shared features, enabling researchers to identify unique cell populations, uncover novel biomarkers, and gain insights into cellular functions [2]. The implications of this work are profound, extending to fields such as genomics, immunology, cancer research, and regenerative medicine.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Cellular Diversity: A Comprehensive Guide to Cell Clustering Methods

Steven,

Kaleb

2022

EIJBMS

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The study of cellular diversity has become increasingly critical in various fields of biology, including genomics, single-cell analysis, immunology, and cancer research. Cell clustering methods play a pivotal role in understanding and characterizing this diversity, enabling the identification of distinct cell populations within complex tissues and heterogeneous samples. This comprehensive guide aims to provide an overview of various cell clustering techniques, offering researchers a roadmap to navigate the intricacies of cellular heterogeneity analysis. In this guide, we begin by outlining the importance of cell clustering in elucidating cellular heterogeneity and its implications for biological research. We then discuss the foundational principles behind cell clustering methods, covering the broad spectrum of techniques, including traditional clustering algorithms, dimensionality reduction methods, and machine learning approaches. The guide delves into the practical aspects of data preprocessing, feature selection, and quality control, all of which are crucial steps before embarking on cell clustering. We also examine the specific challenges and considerations when dealing with single-cell RNA-sequencing data, which has emerged as a cornerstone technology in the study of cellular diversity. Throughout the guide, we emphasize the importance of selecting appropriate clustering methods based on the research objectives, data characteristics, and biological context. We discuss various validation strategies and visualization tools to assess the quality and interpretability of clustering results.

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Section: Introductionmentioning

confidence: 99%

Unveiling Cellular Diversity: A Comprehensive Guide to Cell Clustering Methods

Steven,

Kaleb

2022

EIJBMS

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“…Joint processing coordinated multipoint transmission (JP-CoMP) requires the clustering of neighboring cells and cooperative transmission within each cluster. Clustering algorithms can be static and dynamic, centralized or distributed [29,30]. Static clustering relies on a predetermined fixed base station cluster.…”

Section: Introductionmentioning

confidence: 99%

Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks

et al. 2020

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The design of multiple input multiple output (MIMO) schemes capable of achieving both high spectral and energy efficiency constitutes a challenge for next-generation wireless networks. MIMO schemes based on generalized spatial modulations (GSM) have been widely considered as a powerful technique to achieve that purpose. In this paper, a multi-user (MU) GSM MIMO system is proposed, which relies on the transmission of precoded symbols from a base station to multiple receivers. The precoder’s design is focused on the removal of the interference between users and allows the application of single-user GSM detection at the receivers, which is accomplished using a low-complexity iterative algorithm. Link level and system level simulations of a cloud radio access network (C-RAN) comprising several radio remote units (RRUs) were run in order to evaluate the performance of the proposed solution. Simulation results show that the proposed GSM MU-MIMO approach can exploit efficiently a large number of antennas deployed at the transmitter. Moreover, it can also provide large gains when compared to conventional MU-MIMO schemes with identical spectral efficiencies. In fact, regarding the simulated C-RAN scenario with perfect channel estimation, system level results showed potential gains of up to 155% and 139% in throughput and coverage, respectively, compared to traditional cellular networks. The introduction of imperfect channel estimation reduces the throughput gain to 125%.

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“…In the context of next-generation wireless communications systems, Dewa et al [7] propose a distributed cell clustering algorithm that maximizes the overall downlink throughput of the Joint Processing Coordinated Multipoint Transmission (JP-CoMP) scheme. The proposed algorithm consistently provides a higher performance compared to conventional methods in terms of both average user equipment throughput and average edge user throughput.…”

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confidence: 99%