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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio

Seju Park1, Han-Shin Jo2, Cheol Mun3

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.

Sensors (Basel, Switzerland)
|January 15, 2021
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Summary
This summary is machine-generated.

This study introduces an improved Affinity Propagation clustering method for radio remote head clustering in cloud radio access networks. The new approach enhances spectral and energy efficiency with reduced complexity.

Keywords:
C-RANaffinity propagationclusteringexterior interferencemachine learning

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Area of Science:

  • Wireless Communication Networks
  • Signal Processing
  • Machine Learning Algorithms

Background:

  • Cloud radio access networks (C-RAN) require efficient clustering of radio remote heads (RRHs) for real-time joint transmission.
  • Existing Affinity Propagation (AP) algorithms suffer from high computational complexity due to dynamic preference adjustments.
  • Determining the optimal number of clusters in AP is challenging, especially with varying network topologies.

Purpose of the Study:

  • To propose a novel AP clustering approach with fixed preferences and an adaptive threshold for RRH clustering in C-RAN.
  • To enhance spectral and energy efficiency while maintaining low computational complexity for real-time joint transmission.
  • To address inter-cluster interference issues in AP-based RRH clustering.

Main Methods:

  • Developed an AP clustering variant with fixed preferences and an environment-adaptive threshold determined using Otsu's method on RRH coordinate images.
  • Implemented a greedy merging algorithm to mitigate inter-cluster interference from adjacent exemplars.
  • Evaluated performance using grid and uniform network topologies, considering external interference and varying transmission power levels.

Main Results:

  • The proposed algorithm achieves comparable execution times to existing methods.
  • Demonstrated superior spectral efficiency compared to conventional AP clustering algorithms.
  • Showcased improved energy efficiency in RRH clustering for C-RAN.

Conclusions:

  • The proposed fixed-preference AP clustering with an adaptive threshold offers a more efficient solution for RRH clustering in C-RAN.
  • This method effectively balances performance, complexity, and efficiency for real-time joint transmission applications.
  • The findings suggest a promising direction for optimizing C-RAN resource management.