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| dc.contributor.author | MERHA, FREWOIN | |
| dc.date.accessioned | 2020-11-26T06:08:00Z | |
| dc.date.available | 2020-11-26T06:08:00Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/11688 | |
| dc.description.abstract | To meet up the ever-increasing subscribers demand for higher data rates and higher mobile data traffic growth in the telecommunication industry, the fifth generation (5G) system is being considered for the next future cellular communication standards. The two principal design requirements being aimed at 5G system are robust data transmission rates in Gigabits and low power consumption systems. 5G provides for users with higher system capacity, low latency and low system complexity. One of the core technologies used in 5G system is massive MIMO and it improves spectral efficiency, energy efficiency, degree of freedom and lower system complexity. Massive MIMO technology is an evolving smart antenna technology which has some key promising potentials to boost 5G networks. In massive MIMO systems, hundreds of antennas are employed at the base station to provide service to many users at the same time and frequency. This enables the system to serve the users with uniformly good quality of service simultaneously, with low cost hard ware and without using extra bandwidth and energy. In this thesis the performance analysis and comparison of three linear precoding techniques for single cell downlink massive MIMO system. The system employs a large number of base station antennas serving several user terminals with in the same cell. Analyzed the system performance in terms of achievable sum rates, the energy efficiency, power efficiency, downlink transmit power and BER at perfect channel state information for a downlink single cell massive MIMO systems with linear precoding schemes by using vector and matrix normalization methods are derived, analyzed and compared under the same conditions and assumptions. The simulation result shows that MF gives the best performance with vector normalization and ZF gives the best performance with matrix normalization and also ZF provides best performance with vector/matrix normalization in higher power under the same condition and assumption. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Communication Systems Engineering | en_US |
| dc.title | PERFORMANCE ANALYSIS OF LINEAR PRECODING TECHNIQUES FOR 5G MASSIVE MIMO SYSTEM | en_US |
| dc.type | Thesis | en_US |
