Abstract
In this paper, we will represent several methods that can reduce the computational complexity to detect signals for Uplink Massive MIMO Systems. Then we will show the simulation performance of these methods and analyse them. Finally we will give improvement for better performance.
Highlights
For uplink massive multiple-input multiple-output (MIMO) systems, some traditional methods such as linear minimum mean square error (MMSE) signal detection algorithm or zero forcing (ZF) signal detection method, can achieve the near-optimal performance
This article will represent five promising approaches that can reduce the computational complexity from O( K3 ) to O( K 2 ) and these methods can achieve the near-optimal performance with only a small number of iterations
Analysis:when number of iteration i=1,2,3,4, the performance of method 1 is shown in picture.We can see that,compared with other methods proposed in this paper, because of the pre-processing of the steepest descent algorithm, it has the best performance when i=1.,with the increase of i, its advantages are not clear any more
Summary
For uplink massive multiple-input multiple-output (MIMO) systems, some traditional methods such as linear minimum mean square error (MMSE) signal detection algorithm or zero forcing (ZF) signal detection method, can achieve the near-optimal performance. Because of their high complexity about complicated matrix inversion, they are difficult to be implemented rapidly in large-scale MIMO systems in the future. This article will represent five promising approaches that can reduce the computational complexity from O( K3 ) to O( K 2 ) and these methods can achieve the near-optimal performance with only a small number of iterations. In the end of the article one of these methods will be improved for better performance
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