.png){kind=icon}
.png){kind=icon}
Return
A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/CC.2017.8233666.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Massive multiple-input multiple-output (MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error (MMSE) signal detection using the accelerated overrelaxation (AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.
Keywords:
massive multiple-input multiple-output (MIMO)
accelerated overrelaxation (AOR) iterative method
minimum mean square error (MMSE)
convergence
complexity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
1.9K
Citations:
5.0K