Abstract:
The next generation of wireless communications high data rate and link with high reliability became main factors in measuring the performance of system. Therefore massive Multiple Input Multiple Output systems (massive MIMO) will be promising technology for fifth generation wireless communication to cover the increasing number of users and various wireless applications with acceptable data rate and link with high reliability. Multi User Interference (MUI) is main problem in performance of massive MIMO system. This thesis focus on Multi User Interference(MUI), which is interference results from other user in same cell which has one of the major impact factor in decreasing performance (achievable data rate) of cellular communication system when more users access to the wireless link. The specific goal is to eliminate or mitigate the effect of multi user interference to enhance system performance by increasing number of base station antennas and applying linear pre-coding techniques. This research presents the study and compare the performance analysis of two linear pre-coding techniques which are Matched Filter (MF) and Zero Forcing (ZF) for downlink massive multiple input multiple output System over perfect channel depending on vector normalization and matrix normalization methods when number of base station antennas from 1 to 300 and from 1 to 600, number of user from 1 to 200 and downlink transmitting power 0 dB and -10 dB. Simulations results show that using linear pre-coding techniques and increasing number of base station antennas enhance system performance. When number of base station antenna 600 the achievable sum rate improvements for ZF and MF (433.7451 bit/sec//Hz, 433.2624 bit/sec/Hz ) at high power and ( 86.5119 bit/sec/Hz 86.3519 bit/sec/Hz ) at low power respectively. The results indicate that vector normalization and matrix normalization for ZF gives better performances at high downlink transmission power (0 dB) while at low power MF has better performance in vector normalization and ZF has better performance in matrix .
