Graph Neural Network-Based Node Deployment for Throughput Enhancement.

Area of Science:

• Wireless Communication
• Network Optimization
• Machine Learning

Background:

• Rapid growth in mobile data traffic necessitates enhanced wireless network throughput.
• Network node deployment is crucial for throughput enhancement but involves complex, non-convex optimization problems.
• Existing convex-approximation methods may offer suboptimal solutions for network throughput.

Purpose of the Study:

• To propose a novel graph neural network (GNN) method for optimizing network node deployment.
• To address the limitations of existing methods in achieving high network throughput.
• To provide theoretical support for the GNN's capability in approximating complex functions and gradients.

Main Methods:

• A graph neural network (GNN) is fitted to network throughput data.
• GNN gradients are utilized for iterative updates of network node locations.
• A policy gradient algorithm is employed for GNN training dataset generation.
• A hybrid node deployment strategy is investigated for further throughput improvement.

Main Results:

• The proposed GNN method effectively optimizes network node deployment for improved throughput.
• The GNN demonstrates the capacity to approximate multivariate permutation-invariant functions and their gradients.
• Numerical experiments confirm the competitive performance of the proposed methods against baseline approaches.

Conclusions:

• The novel GNN-based approach offers an effective solution for network node deployment challenges in wireless communications.
• This method provides a promising alternative to traditional optimization techniques, enhancing network throughput.
• The study validates the theoretical underpinnings and practical efficacy of using GNNs for network optimization.