Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157840
Title: Deep Learning for Multiple-Input Multiple-Output communication system
Authors: Tan, Kaiwei
Keywords: Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tan, K. (2022). Deep Learning for Multiple-Input Multiple-Output communication system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157840
Abstract: Multiple-Input Multiple-Output (MIMO) technology is an emerging and promising technology for wireless communications, especially in the implementation of the fifth generation (5G). Due to implementation of Internet-of-Things (IoT), 5G needs to account for higher user density. With the strive for high spectral efficiency, high energy efficiency and low latency, the study of MIMO is crucial to enhance 5G performance. As such, the use of Deep Learning (DL), modelled by the human brain, can be a useful tool in the exploration of various aspects of MIMO systems. Taking into considerations the multiple antenna characteristics of MIMO systems, the study of Rayleigh fading and Additive White Gaussian Noise (AWGN) needs to be incorporated into wireless transmission techniques. Along with the generation of Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), the study of MIMO channel capacity and detection of data symbols are vital in the implementation of a DL model. With the nature of DL, we also consider DeepMIMO and various dataset options to bring about the best fit model. Analysis results using Detection Network (DETNet) show the success of DL in the study of channel capacity and BER.
URI: https://hdl.handle.net/10356/157840
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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