Deep Learning-based Hybrid Precoding and Combining Designs for Millimeter Wave MIMO Systems
Objective
This paper aims to develop a deep learning-based hybrid precoding and combining design framework for mmWave MIMO systems, using a trained neural network to optimize RF and baseband precoders and combiners.
Abstract
This paper presents a deep learning (DL)-based approach to address the hybrid precoding and combining design problem in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. After training a neural network (NN), we input a test dataset to predict the phases of the RF analog precoders and combiners. Once the RF analog precoder is determined, the corresponding baseband precoder is derived using a least squares method. A similar procedure is applied to obtain the baseband combiner from the RF analog combiner. Simulation results demonstrate that the proposed method achieves competitive spectral efficiency, validating the effectiveness of the deep neural network (DNN)-based design.
Keywords: Hybrid Beamforming, Multiple-Input Multiple-Output (MIMO), Deep Learning, Deep Neural Networks.
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Block Diagram
Specifications
Software: Matlab R2022b.
Hardware:
Operating Systems:
• Windows 10
• Windows 7 Service Pack 1
• Windows Server 2019
• Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support.
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Communication?
· About Communication
· Introduction to Communication
· How Communication Works?
· Importing the System Design, Characterization and Visualization
· Analyzing of BER tool
· Analyzing of Error Rate Test Console
· Generation of WSN
· WSN network creation
· Nodes Communication
· Clustering
· Routing
· Convolutional
· Equalization and Synchronization etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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