Exploring Evolutionary Spectral Clustering for Temporal-Smoothed Clustered Cell-Free Networking

TL;DR

This paper introduces an evolutionary spectral clustering method for dynamic cell-free networks that accounts for user mobility, aiming to optimize network sum rate while reducing handovers through temporal smoothing.

Contribution

It proposes a novel temporal-smoothed clustering algorithm based on evolutionary spectral clustering to handle user mobility in dynamic networks.

Findings

Effective in reducing handovers in mobile environments.

Maintains high sum rate comparable to static clustering.

Smooths network partitions over time.

Abstract

Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This could lead to a large number of handovers in the practical dynamic environment with moving users, seriously hindering the implementation of clustered cell-free networking in practice. This paper considers user mobility and aims to simultaneously maximize the sum rate and minimize the number of handovers. By transforming the multi-objective optimization problem into a time-varying graph partitioning problem and exploring evolutionary spectral clustering, a temporal-smoothed clustered cell-free networking algorithm is proposed, which is shown to be effective in smoothing network partitions over time and reducing handovers while maintaining similar sum rate.