Evaluation of Beamforming Algorithms for Massive MIMO
Khan, Muhammad Nasir (2019)
Khan, Muhammad Nasir
2019
Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2019-05-29
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201905311807
https://urn.fi/URN:NBN:fi:tty-201905311807
Tiivistelmä
Massive MIMO relay system is an expansion of the Multiple-Input-Multiple-Output (MIMO) which enabled multiple users and antennas to communicate with each other for data sharing. A relay system with multiple antenna system has an advantage over simple MIMO system as it interconnects base station and users with each other for sharing of information and both BS and users are independent of many antennas. High data rate applications such as Machine-to-Machine communication and wireless sensor networks are experiencing transmit power loss, channel capacity and mismanagement of data. The demand for the Massive MIMO relay system is opening a door for ultra-high latency wireless network applications in case of saving transmit power and transmission of accurate information over the wireless networks.
Due to the loss in transmit power and mismanagement of information over wireless networks, it is difficult to get better performance. Different approaches were made to optimize the overall transmit power of communication systems. One of the approaches was explained in this thesis work. The focus of the thesis is the use of beamforming algorithms named as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF) to maximize the overall capacity of the MIMO system. These algorithms were evaluated on different scenarios to handle the performance and behavior with different network conditions. Various use cases were used for analyzing the beamforming algorithms. The performance of both algorithms was observed by considering the scenarios such as varying the transmit and receive antenna’s size and modulation schemes. Singular Value Decomposition (SVD) Method was used at the main MIMO channel to optimize the channel capacity. SVD divides the MIMO channel into different subchannels and optimizes the channel capacity of individual channels.
The summary of results showed that MRC and ZF in the CP-OFDM environment when the number of RX antennas increased then they gave better BER performance as compared to the single antenna system. On the other hand, with higher modulation schemes efficiency was not good but with lower modulation scheme performance was satisfactory.
Due to the loss in transmit power and mismanagement of information over wireless networks, it is difficult to get better performance. Different approaches were made to optimize the overall transmit power of communication systems. One of the approaches was explained in this thesis work. The focus of the thesis is the use of beamforming algorithms named as Maximum Ratio Combining (MRC) and Zero-Forcing (ZF) to maximize the overall capacity of the MIMO system. These algorithms were evaluated on different scenarios to handle the performance and behavior with different network conditions. Various use cases were used for analyzing the beamforming algorithms. The performance of both algorithms was observed by considering the scenarios such as varying the transmit and receive antenna’s size and modulation schemes. Singular Value Decomposition (SVD) Method was used at the main MIMO channel to optimize the channel capacity. SVD divides the MIMO channel into different subchannels and optimizes the channel capacity of individual channels.
The summary of results showed that MRC and ZF in the CP-OFDM environment when the number of RX antennas increased then they gave better BER performance as compared to the single antenna system. On the other hand, with higher modulation schemes efficiency was not good but with lower modulation scheme performance was satisfactory.