Performance Analysis Of Precoding Techniques For Extremely Large Mimo System

Abstract

Currently, beyond the 5G mobile network, is a promising technology in wireless communication. This requires a very large number of antennas to increase the performance. Extremely Large Multiple Input Multiple Output (XL-MIMO) is a technique that allows the use of numerous antennas to improve spectral efficiency and is one of the most important technologies for 5G and beyond 5G (B5G) wireless communication systems. Despite its advantages, the proliferation of antennas introduces challenges such as interference and heightened complexity. To address interference and optimize the transmission of multiple independent data streams, spatial multiplexing and beamforming play pivotal roles. In this thesis, five distinct precoding schemes have been examined such as SwoR-rKA, rKA, TPE, RZF, and MRT, in terms of their complexity and performance. XL-MIMO systems take advantage of spatial variety to provide high capacity and spectrum efficiency. Because of the large number of antennas, this performance comes at the expense of significant computational complexity. Low-complexity precoding techniques can be used to solve this problem by lowering computational complexity without sacrificing performance. SwoR-rKA has high performance in terms of SE. The rKA method chooses the column to be updated randomly, on the other hand, the SwoR-rKA approach tends to choose a column that corresponds to a channel in better condition. This method is ineffective because the channel in good condition can be selected more frequently for small iteration. To address this issue, the PCP- SwoR-rKA method chooses a column that corresponds to a channel in bad condition in order to reduce the error term. When SwoR-rKA is compared with RZF, it has a 0.9% improvement in SE. Based on the simulation result, it can be observed that the PCP-SwoR-rKA method lowers the bit error by 12.5% compared to the existing method with the same performance and complexity.