.png){kind=icon}
Return
Decoding With Hypothesis Testing: A Near ML Decoding Scheme for MIMO Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2011.2104986.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A near maximum-likelihood (NML) scheme for the decoding of multiple-input-multiple-output (MIMO) systems is addressed by incorporating the technique of hypothesis testing in the searching procedure. The proposed decoding scheme selects the best node based on the node metric, determines one child node of the best node via hypothesis testing, and connects the best node with some sibling nodes of the child node. From simulation results, it is confirmed that the proposed scheme has a lower computational complexity than other NML decoders and that the performance difference between the proposed and maximum-likelihood schemes is negligibly small.
Keywords:
Hypothesis testing
metric-first search
multiple-input-multiple-output (MIMO) systems
near maximum-likelihood (NML) decoder
tree search
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
Cited Papers
.png){kind=icon}
ShareSave
Breadth-first signal decoder: A novel maximum-likelihood scheme for multi-input multi-output systems
PREAI
ShareSaveExtending a fixed-complexity sphere decoder to obtain likelihood information for turbo-MIMO systems
PREAI
ShareSaveShareSave
View more