A New Noise Variance Based Layered Pruning ML-DFE Algorithm

A new noise variance based reduced maximum likelihood decision feedback equalization (ML-DFE) algorithm has been developed. This algorithm reduces the calculation complexity by exploring the intrinsic statistical properties layer by layer. Through setting layered thresholds, part of the nodes in the...

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Bibliographic Details
Published in:2012 IEEE 75th Vehicular Technology Conference (VTC Spring) pp. 1 - 4
Main Authors: Shubo Ren, Xinyu Mao, Jianjun Wu, Haige Xiang
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Algorithm design and analysis
Complexity theory
Decision feedback equalizers
Maximum likelihood decoding
Maximum likelihood detection
MIMO
Noise
ISBN:9781467309899, 1467309893
ISSN:1550-2252
Online Access:Get full text
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Summary:A new noise variance based reduced maximum likelihood decision feedback equalization (ML-DFE) algorithm has been developed. This algorithm reduces the calculation complexity by exploring the intrinsic statistical properties layer by layer. Through setting layered thresholds, part of the nodes in the searching process will be cut by comparing with the thresholds. Simulation results show that the complexity drops lots while the performance drops small.
ISBN:9781467309899
1467309893
ISSN:1550-2252
DOI:10.1109/VETECS.2012.6240120