MATRIX DECOMPOSITION FOR MIMO DETECTION
Objective
This study investigates matrix decomposition for Massive MIMO systems, comparing computational complexities of QR, Cholesky, and UN algorithms against modern large MIMO detection techniques to guide VLSI and system design choices.
Abstract
Massive multiple-input multiple-output, or MIMO, is a key technology for fifth generation (SG) communication systems. MIMO symbol detection is one of the most computationally demanding tasks for a large MIMO baseband receiver. Massive MIMO systems were typically used for small-scale MIMO detection due to their modular architecture and numerical stability. We examine matrix decomposition techniques for these systems in this study. We demonstrate the computational complexity of linear detection techniques based on QR, Cholesky, and UN decomposition algorithms for a number of large MIMO topologies. We compare them with the state-of-the-art approximation inversion based large MIMO detection methods. The results provide important insights to assist very large-scale integration (VLSI) and system designers in selecting the most appropriate massive MIMO detection techniques.
Keywords: Massive MIMO, Approximate Matrix Inversion, Matrix Decomposition, QR, LDL, Cholesky.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
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
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
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
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
