Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System.

Area of Science:

• Telecommunications Engineering
• Wireless Communication Systems
• Machine Learning Applications

Background:

• Fifth generation (5G) and beyond 5G (B5G) networks require ultra-dense deployments with high spectral efficiency.
• Massive MIMO heterogeneous networks (HetNets) present deployment challenges due to high capital expenditure and inter-cell/inter-tier interference.
• Conventional analytical methods for performance modeling are complex due to the stochastic nature of network parameters.

Purpose of the Study:

• To propose a machine learning (ML) approach for predicting the performance of massive MIMO HetNet systems.
• To provide a valuable tool for network service providers to analyze performance before significant investment.
• To address the need for precise performance analysis in complex, multi-cell network scenarios.

Main Methods:

• Developed a machine learning model to predict network performance in a multi-cell, two-tier massive MIMO HetNet.
• Focused on base stations operating in the sub 6GHz band.
• Utilized coverage probability (CP) and area spectral efficiency (ASE) as key performance metrics.

Main Results:

• The ML model accurately predicts numerical values for CP and ASE.
• The model can evaluate performance for arbitrary network configurations.
• Demonstrated the feasibility of using ML for performance prediction in advanced wireless networks.

Conclusions:

• The proposed ML approach offers a valuable method for performance analysis of massive MIMO HetNets.
• This tool can significantly aid decision-making processes for network service providers regarding future network deployments.
• Machine learning provides an effective solution for the complex performance modeling challenges in next-generation wireless networks.