Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter
Configuration in Cellular Network
- URL: http://arxiv.org/abs/2301.03412v1
- Date: Thu, 29 Dec 2022 18:51:36 GMT
- Title: Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter
Configuration in Cellular Network
- Authors: Mehrtash Mehrabi, Walid Masoudimansour, Yingxue Zhang, Jie Chuai,
Zhitang Chen, Mark Coates, Jianye Hao and Yanhui Geng
- Abstract summary: We propose a learning-based framework for handover parameter configuration.
First, we introduce a novel approach to imitate how the network responds to different network states and parameter values.
During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy.
- Score: 47.29123145759976
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The mobile communication enabled by cellular networks is the one of the main
foundations of our modern society. Optimizing the performance of cellular
networks and providing massive connectivity with improved coverage and user
experience has a considerable social and economic impact on our daily life.
This performance relies heavily on the configuration of the network parameters.
However, with the massive increase in both the size and complexity of cellular
networks, network management, especially parameter configuration, is becoming
complicated. The current practice, which relies largely on experts' prior
knowledge, is not adequate and will require lots of domain experts and high
maintenance costs. In this work, we propose a learning-based framework for
handover parameter configuration. The key challenge, in this case, is to tackle
the complicated dependencies between neighboring cells and jointly optimize the
whole network. Our framework addresses this challenge in two ways. First, we
introduce a novel approach to imitate how the network responds to different
network states and parameter values, called auto-grouping graph convolutional
network (AG-GCN). During the parameter configuration stage, instead of solving
the global optimization problem, we design a local multi-objective optimization
strategy where each cell considers several local performance metrics to balance
its own performance and its neighbors. We evaluate our proposed algorithm via a
simulator constructed using real network data. We demonstrate that the handover
parameters our model can find, achieve better average network throughput
compared to those recommended by experts as well as alternative baselines,
which can bring better network quality and stability. It has the potential to
massively reduce costs arising from human expert intervention and maintenance.
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